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Development of a novel high-performance portable barrier based on full-scale field tests and simulation optimization

Health disparities influence peripheral venous access insertion time in the emergency department: An observational study

Objective This retrospective, multicenter study aimed to investigate disparities in peripheral intravenous catheter (PIVC) placement wait times among patients in emergency departments (EDs), focusing on the impact of factors such as race, sex, age, and comorbidities. Methods Electronical health record (EHR) data from four EDs within Corewell Health System were analyzed for adult patients who underwent PIVC placement between January 1st, 2021 and January 31st, 2023. Multivariable linear regression models were employed to analyze associations between patient demographics (including race, sex, age, and comorbidities) and PIVC placement wait times. Adjustments were made for Charlson comorbidity index, emergency severity index, hospital size, obesity, and method of PIVC insertion. Results Among 319,938 PIVC placements analyzed, significant disparities were observed: Black patients waited 9.65% longer for PIVC placement compared to White patients (p < 0.001). Women experienced a 6.67% longer wait time than men (p < 0.001). Obese and elderly patients also experienced prolonged wait times. These disparities persisted across both ultrasound-guided and traditionally placed PIVCs. Conclusions This study underscores substantial disparities in PIVC placement wait times in EDs, influenced by race, sex, age, and comorbidities. Addressing these disparities is crucial for improving equity in emergency care delivery. Future research should focus on implementing targeted interventions to mitigate these disparities and enhance timely access to essential medical interventions for all patient populations.

Correction: Research on partial discharge signal recognition and classification of power transformer based on acoustic-VMD and CNN-LSTM

Comparing the effectiveness of emotion regulation therapy and cognitive behavioral therapy on treatment adherence in hemodialysis patients: A randomized controlled clinical trial

Introduction Non-adherence is a common challenge among patients undergoing hemodialysis (HD). This randomized controlled trial compared the efficacy of Emotion Regulation Therapy (ERT) and Cognitive Behavioral Therapy (CBT) on improving treatment adherence in hemodialysis patients, with a control group receiving standard care. Materials and methods Ninety hemodialysis patients were equally randomized into ERT, CBT, and control groups (N = 30 each group), with six attrition cases per group. Baseline demographics (age, BMI, dialysis duration, education) showed no significant intergroup differences (p > 0.05). Adherence was assessed across five domains: dialysis, medication, fluid intake, dietary regimen, and total adherence. A MANCOVA/ANCOVA model analyzed changes at pre-intervention, post-intervention, and 3-month follow-up, controlling for baseline characteristics. Results Both intervention groups demonstrated significant adherence improvements post-treatment versus controls (P < 0.001). CBT showed superior immediate effects, with total adherence scores increasing from 836.66 ± 192.95 to 1073.33 ± 89.28 (Δ + 28.3%), while ERT improved from 833.33 ± 210.53 to 920.00 ± 181.04 (Δ + 10.4%). At follow-up, CBT maintained higher adherence (1050.83 ± 93.88 vs. ERT’s 890.00 ± 155.30), though both groups experienced dialysis adherence declines from post-treatment peaks (CBT: 580 to 574.16; ERT: 520 to 483.33). Control group adherence deteriorated across all domains (total: 911.66 to 835.00). Time-intervention interactions were significant for total adherence (P < 0.001), dialysis (P = 0.006), and medication adherence (P < 0.001), with largest CBT effects on fluid restriction adherence (Δ + 56.1% vs. ERT’s Δ + 8.3%). Discussion While both therapies enhanced adherence, CBT produced greater short-term improvements, particularly in behavioral domains (fluid/dietary compliance), whereas ERT showed better maintenance of medication adherence. The differential trajectory patterns suggest CBT’s structured behavioral strategies may offer immediate benefits, while ERT’s emotion-focused techniques could support longer-term regimen acceptance. Integration of both approaches into renal care protocols may optimize adherence outcomes.

State estimation of multi-sensor systems based on error-state Kalman

With the rapid advancement of multi-sensor systems, the capabilities of robots in complex scenes are gradually improving. A multi-sensor system state estimation algorithm based on error-state Kalman filter is proposed to address the issues of noise interference, sensor data loss, and interference from moving targets in dynamic scenes. Firstly, the state estimation method of multi-sensor system based on sequential fusion framework is designed to effectively integrate and process different sensor data. On this basis, further research is conducted to design a lightweight detection algorithm based on multi-sensor data for identifying and processing moving targets in dynamic scenes, thereby reducing their interference with state estimation. Finally, a sequential fusion odometer based on error-state Kalman filtering is constructed to enhance the accuracy and stability of state estimation, and further optimize the performance of the entire state estimation system. Experimental results show that the proposed algorithm achieves an estimation error of only 0.36, significantly outperforming the comparison algorithms. The mean average precision on the KITTI and NuScenes datasets reaches 0.89 and 0.85, respectively. The algorithm maintains stable efficiency across varying data scales, with low packet loss rates and controllable false detection rates in medium-to-long-term state estimation. Packet loss rates in noisy environments and dynamic target interference scenarios are 0.53% and 1.07%, respectively. The multi-sensor fusion state estimation algorithm proposed in the study can effectively handle the interference problem in dynamic scenes, significantly improving the localization and mapping performance of robots in complex environments. The research provides an effective solution for the stable positioning and mapping of robots in complex dynamic environments, which is of great significance for improving the application performance of robots in fields such as autonomous driving and special operations.

Executive functions and psychopathology: A transdiagnostic network analysis

Mental health research is shifting toward dimensional, transdiagnostic frameworks, yet the role of executive functions (EFs) across psychopathological domains remains unclear. In this study, we examined transdiagnostic associations and potential directional pathways linking EFs with psychopathology in a large sample of preadolescents (N = 9,119) from the Adolescent Brain Cognitive Development (ABCD) study. We employed a Gaussian graphical model (GGM) to estimate partial correlations and a directed acyclic graph (DAG) to infer potential directional influences between EFs and psychopathology. Modest associations were observed among the EFs and psychopathology. Working memory emerged as a central node, showing positive associations with attention problems, social problems, and rule-breaking behavior, and negative associations with anxious/depressed and somatic complaints. These results were mirrored in the DAG, which identified working memory and attention problems as key converging hubs. Sex-stratified analyses revealed notable differences in network structure. Our findings reveal a core transdiagnostic role for working memory in preadolescent psychopathology.

Correction: Protocol for research examination of individual suicides occurring in chronic pain: A qualitative approach to psychological autopsy methodology

Controlling a simple model of bipedal walking to adapt to a wide range of target step lengths and step frequencies

We tested whether the same control principles that support steady-state walking are sufficient for robust, and rapid gait adaptations over a wide range of step lengths and frequencies. We begin by demonstrating that periodic gaits exist at combinations of step frequency and step length that span the full range of gaits achievable by humans. However, their open-loop stability is not enough to rapidly transition to target gaits. Next, we show that actuating with only one push-off and one hip spring of fixed stiffness cannot fully control the walker in the entire gait space. We solve this by adding a second hip spring with an independent stiffness to actuate the second half of the swing phase. This allowed us to design local feedback controllers that provided rapid convergence to target gaits by making once-per-step adjustments to control inputs. To adapt to a range of target gaits that vary over time, we interpolated between local controllers. This policy performs well, accurately tracking rapidly varying combinations of target step length and step frequency with human-like response times.

The role of affective temperaments in predicting depression and anxiety symptoms in patients with primary hyperparathyroidism

Background This study aimed to investigate the relationship between affective temperament traits and the severity of depression and anxiety symptoms in patients diagnosed with primary hyperparathyroidism. Methods A cross-sectional study was conducted including 47 patients with primary hyperparathyroidism and 36 healthy controls. Participants were evaluated using the Memphis, Pisa, Paris and San Diego Temperament Assessment Scale to assess affective temperament profiles, and the Hospital Anxiety and Depression Scale to determine symptoms of anxiety and depression. Clinical, biochemical, and sociodemographic data were also collected. Correlation analyses and a generalized linear model were used to explore associations and predictors of psychiatric symptoms. Data were collected at Çanakkale Onsekiz Mart University between June 2016 and January 2017. Results Patients with primary hyperparathyroidism showed significantly higher scores for depressive, cyclothymic, and anxious temperaments compared to healthy controls. Anxiety and depression scores were also significantly higher in the patient group. Among patients, depressive, cyclothymic, irritable, and anxious temperament traits were positively correlated with both anxiety and depression levels, whereas hyperthymic temperament showed no significant association. Multivariate analysis revealed that anxious and cyclothymic temperaments were significant predictors of anxiety symptoms, while hyperthymic temperament was associated with lower depression scores. No significant associations were found between biochemical parameters and psychiatric symptom severity, except for a positive correlation between serum calcium levels and hyperthymic temperament. Conclusions Affective temperament characteristics, particularly anxious and cyclothymic traits, are closely associated with the severity of anxiety and depression symptoms in patients with primary hyperparathyroidism. Hyperthymic temperament may act as a protective factor against depression in this population. Incorporating temperament assessment into the clinical evaluation of these patients may facilitate early identification of those at higher risk for psychiatric comorbidities and guide more effective, individualized intervention strategies.

Adaptive fractional-order non-singular terminal sliding mode control for omnidirectional quadrotors based on WRBF neural network

This paper presents a novel robust six-degree-of-freedom trajectory tracking control strategy for tilt-rotor quadrotors operating under uncertainties and disturbances. The key contribution lies in a unified framework that synergistically co-designs a Fractional-Order Nonsingular Terminal Sliding Mode Controller (FONTSMC) with an adaptive Wavelet Radial Basis Function (WRBF) neural network, establishing a deeply integrated architecture rather than a simple combination of independent modules. This co-designed structure introduces three fundamental advances: first, the WRBF network enables precise online estimation and compensation of unstructured uncertainties while the fractional-order nonsingular terminal sliding surface ensures fast finite-time convergence without singularity; second, the Mexican Hat wavelet activation function significantly enhances local approximation accuracy, learning speed, and noise robustness compared to conventional Gaussian RBF networks; third, a parallel control structure integrated with Moore-Penrose pseudo-inverse-based allocation efficiently maps synthesized 6-DOF commands to redundant actuators. Closed-loop stability is rigorously guaranteed through Lyapunov analysis. Comprehensive simulations demonstrate that the proposed controller outperforms conventional NTSMC and RBF-FONTSMC methods in tracking accuracy, convergence speed, control effort, and response smoothness, confirming its superior capability for complex UAV operations.

Integrated analysis of single-cell and bulk RNA-seq reveals MAGEA3/6-associated immune subtypes and key immune genes in gastric cancer

The immune microenvironment is critical in gastric cancer (GC), yet epithelial-specific genes linked to immune infiltration remain poorly defined. We integrated single-cell RNA-seq (GSE112302) and TCGA-STAD data to identify epithelial-related differentially expressed genes (DEGs). MAGEA3 and MAGEA6 were selected to classify immune subtypes. Immune infiltration was analyzed using ESTIMATE, CIBERSORT, ssGSEA, and Xcell. WGCNA and survival analysis identified prognostic immune-related genes. MAGEA3/6-high tumors showed low immune infiltration, defining an immune-cold subtype, while MAGEA3/6-low tumors were immune-hot. Further analysis confirmed that BCL11B and PAEP expression levels were significantly associated with immune cell infiltration and immune regulatory activity. MAGEA3/6 expression defines immune subtypes in GC and highlights potential targets for immunotherapy.

Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction

Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predictive accuracy in complex, high-dimensional, and imbalanced health datasets. To develop and evaluate enhanced machine learning and hybrid ensemble models for the prediction of coronary heart disease, with a focus on improving diagnostic performance, interpretability, and applicability in resource-constrained settings. We utilized a nationally representative dataset of 253,680 individuals from the Behavioral Risk Factor Surveillance System. Preprocessing included normalization and balancing via the Synthetic Minority Oversampling Technique (SMOTE). Baseline models—Decision Trees, Random Forests, Gradient Boosting, and Support Vector Machines—were compared against improved versions: Adaptive Noise–Resistant Decision Tree (ADNRT), Hybrid Imbalanced Random Forest (HIRF), Pruned Gradient Boosting Machine (PGBM), and Enhanced Support Vector Machine (ESVM). Ensemble approaches (stacking, boosting, bagging, Bayesian model averaging and majority voting) were implemented and evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC). Calibration and learning curves were also analyzed. Enhanced models consistently outperformed their baseline counterparts. PGBM achieved the highest sensitivity (90.8%), while HIRF demonstrated the best overall calibration and balance (AUC = 0.937; sensitivity = 88.4%; specificity = 82.9%). The stacking ensemble emerged as the best-performing model with an accuracy of 87.2%, sensitivity of 89.6%, specificity of 84.7%, and AUC of 0.94. Calibration and learning curve analyses confirmed strong generalizability and low overfitting across ensemble models. Hybrid ensemble machine learning models significantly outperform traditional classifiers in CHD prediction, offering high accuracy, robustness, and interpretability. These models present a scalable framework for implementing AI-driven diagnostic tools in low–resource environments, potentially transforming early detection and prevention of coronary heart disease.

A screening strategy for bioactive components from Amaranth: An integrated approach of network pharmacology, molecular docking and molecular dynamics simulation

Amaranth is a traditional medicinal and forage plant with promising anti-inflammatory properties. To enhance its utilization in livestock and feed industries, this study investigated the bioactive compounds and mechanisms of Amaranth at different growth stages using metabolomics and network pharmacology. LC-MS/MS identified 266 metabolites, including key compounds such as ferulic acid, isoferulic acid, sinapic acid, and 13-HODE. A total of 132 inflammation-related targets were screened, and enrichment analysis revealed their involvement in ATP binding, inflammatory response, and PI3K-Akt/MAPK signaling pathways. Molecular docking and molecular dynamics simulations confirmed strong interactions between core targets (e.g., IL6 , MMP9 ) and major compounds. These findings demonstrate that phenolic acids and fatty acids in Amaranth possess anti-inflammatory activity, underpinning its prospective use in the formulation of biofunctional feeds and in promoting the health of livestock.

Deficiencies in communication between clinical microbiological laboratories and physicians may impair the diagnosis of Lyme borreliosis: A study of the use and application of serology in three neighbouring counties in Sweden

Purpose The diagnosis of Lyme borreliosis (LB) can be challenging. The aim of this study was to investigate, describe and compare the actual use, application and documentation of LB serology in three neighbouring LB-endemic counties in Sweden. As part of this, we intended to study the concordance between laboratory reports and physicians’ assessments regarding LB. Methods Three hundred patients sampled for LB serology in the counties of Jönköping, Kalmar and Östergötland, in 2016 were randomly selected for this study. Data was collected from the laboratory information technology systems of the departments of Clinical Microbiology in the three counties and from medical records. Results Suspected Lyme neuroborreliosis (LNB) was the most common indication for LB serology, and was found in a total of 188/300 (63%) patients: 75/100 in Jönköping, 66/100 in Östergötland and 47/100 in Kalmar. Cerebrospinal fluid examination was performed on a minority of patients in whom LNB was suspected, 34/188 (18%). LB serology was performed on sera from 15 patients with suspected erythema migrans. Sufficient information to enable an assessment of concordance between laboratory reports and medical records was available for 158/300 (53%) patients, while 94/158 (59%) were considered to have concordant records. Conclusions LB serology is frequently performed on questionable indications contrary to guidelines, which limits the value and potential of the analysis. Notably, the use appears to be different in three neighbouring counties that follow the same national guidelines. Although new diagnostic technologies, may improve laboratory diagnostics in the future, there is still a need for interventions to enable a more rational use of LB serology.

Antibacterial efficacy of Solanum muricatum aiton metabolites against methicillin-resistant staphylococcus aureus: Insights into bioactive compounds and molecular mechanisms

The incidence of methicillin-resistant Staphylococcus aureus (MRSA) has been steadily increasing in Ethiopia over the past few decades. As a result, the need for new antibiotic classes has become imperative to combat the growing threat of multidrug-resistant bacteria, including MRSA. Phytochemical investigation of the aerial parts extract of the edible plant Solanum muricatum Aiton (F. Solanaceae) afforded eight known metabolites: kaempferol 3- O -gentiobioside ( 1 ), kaempferol 3- O -sambubioside ( 2 ), quercetin 3- O -rhamnoside ( 3 ), procyanidin A2 ( 4 ), procyanidin A2 3- O -glucoside ( 5 ), (2 S )-2-hydroxy-3-[(9 Z ,12 Z )-1-oxo-9,12-octadecadien-1-yl]oxy]propyl- O-β -D-galactopyranoside ( 6 ), palmitic acid ( 7 ), and linoleic acid ( 8 ). The structures of the isolated compounds were assigned by 1D and 2D NMR. The crude extract exhibited moderate anti- Staphylococcus activity (MIC = 196.8 µg/mL), while compound 1 (kaempferol 3- O -gentiobioside) showed the strongest inhibitory effect (MIC = 8.3 µM), followed by compounds 2 and 3 (MIC = 10.2 and 11.2 µM, respectively). These compounds significantly reduced MRSA biofilm formation by up to 75.09% at sub-MIC concentrations ( p  < 0.05). Checkerboard assays revealed synergistic interactions among compounds 1 , 2 , and 3 and between these compounds and gentamicin (FICI < 0.5), suggesting enhanced therapeutic potential when combined. An integrated computational approach combining protein-protein interaction (PPI) network analysis, molecular docking, and molecular dynamics (MD) simulations was employed. The PPI network analysis, constructed using the STRING and STITCH databases, revealed critical MRSA-associated targets and their interactions with bioactive compounds from S. muricatum . Network hub analysis identified key immune-regulatory and antibacterial resistance-related proteins, suggesting potential intervention points. Molecular docking results identified kaempferol 3-gentiobioside (compound 1 ) as the most potent inhibitor of APH(3’)-IIIa, with strong binding energy and interactions with key catalytic residues. Further 150 ns MD simulations confirmed the stability of the compound 1 -APH(3’)-IIIa complex, as evidenced by minimal RMSD fluctuations, sustained hydrogen bonding, stable protein compactness (Rg), and favorable potential energy values.

Spatial genetic diversity and populational differentiation of Ternstroemia sylvatica (Ericales: Pentaphylacaceae) in eastern Mexico

Ternstroemia sylvatica inhabits several temperate and tropical montane forests in eastern Mexico. Its current discontinuous distribution results from both natural and anthropogenic fragmentation. We assessed the genetic diversity and population differentiation of T. sylvatica across its distribution range using 18 microsatellite markers. We sampled 366 individuals from 16 populations, analyzing genetic diversity ( He ) and population structure via STRUCTURE and Discriminant Analysis of Principal Components (DAPC). Our results revealed high genetic differentiation ( F ST  = 0.21), with most genetic variation occurring within populations (79.50%). STRUCTURE analysis identified two major genetic clusters: a northern group, comprising the populations with the lowest genetic diversity, and a southern group with higher genetic diversity ( He  = 0.59–0.73) geographically structured into ten subgroups. Additionally, the results suggest historical fragmentation, limited gene flow among populations and inbreeding, as a heterozygote deficit is prevalent across populations. The high genetic diversity in specific populations indicates potential hybridization with other sympatric Ternstroemia species.

Correction: The dynamic of treatment-seeking in a community sample with obsessive-compulsive symptoms: A mixed method approach

Extent and causes of the collapse in the registration of innovative medications in Lebanon: A mixed-methods analysis

Objectives Delays in innovative drug registration across countries, or the “drug approval lag”, can cause inequities in treatment access, thereby worsening patient outcomes. Registration delays hinder the first step in making treatments available, and are often linked to regulatory inefficiencies, constrained healthcare financing, and fragmented decision-making. Since late 2019, Lebanon’s health system has faced overlapping socioeconomic and political crises, yet their impact on innovative medication registration remains undocumented. This study aimed to measure this impact by comparing Lebanon’s drug approval lag before (2014–2019) and after (2020–2024) the crisis and to develop an interpretive framework exploring the rationale behind an informal policy to delay innovative medication registration. Methods A mixed-methods approach was adopted. Innovative medications approved by the Food and Drug Administration (FDA) or the European Medicines Agency (EMA) between 2014 and 2024 were included and compared to local registration timelines in Lebanon. In-depth interviews with policymakers, industry leaders, and healthcare providers informed the interpretive framework. Results Findings revealed a dramatic fall in innovative medication registrations post-crisis over these two periods. The proportion of FDA-approved innovative medications registered in Lebanon dropped from 43.6% to 0% and EMA-approved medications dropped from 59.4% to 0%. Pre-crisis, average registration time was under two years; post-crisis, delays are estimated to exceed four years. Our interpretive framework suggests the intermediate effects of delaying innovative medication registration are mainly to control costs and reduce reimbursement pressures on the Ministry of Public Health. However, key stakeholders believe the resulting negative consequences, such as reduced access to life-saving treatments and greater dependence on parallel market importation, outweigh the short-term benefits. Conclusion Health systems are complex adaptive systems, where policies affecting innovative drug approvals may not only delay access, but also trigger unintended consequences. In Lebanon, the registration of innovative medications should remain independent from reimbursement decisions and grounded in evidence-informed health policies.

The effect of Tannic acid on colonic anastomosis in abdominal sepsis: An experimental study

Background This study aimed to evaluate the effect of tannic acid on colonic anastomosis in a sepsis model induced by cecal abrasion. Materials and methods Thirty Sprague-Dawley rats were used. The animals were randomly divided into three groups of ten: Group 1 (n:10): Colonic anastomosis + 0.9% isotonic NaCl. Group 2 (n:10): Cecal ligation and puncture + Colonic anastomosis + 0.9% isotonic NaCl. Group 3 (n:10): Cecal ligation and puncture + Colonic anastomosis + Tannic Acid group. The rats were sacrificed on the fifth postoperative day, and the resected colon segments, bursting pressure, hydroxyproline levels, and histopathologic features of the anastomosis were evaluated. Results The bursting pressure value was statistically significantly higher in Group 3, where tannic acid was administered (p < 0.05). Group 2 and Group 3, in which peritonitis was induced, had moderate levels of fibroblastic activity, inflammatory cell infiltration, neovascularisation, and collagen; whereas, they were higher in Group 1. Although the inflammation value dropped in Group 3 compared to Groups 1 and 2, there was no statistical difference in Group 2. The hydroxyproline values were 2.046 ± 1.1411 mcg/gr and 5.9730 ± 4.35900 mcg/gr tissues, respectively, in Groups 2 and 3, where septic conditions prevailed, and a statistically significant difference was found (p < 0.05). Conclusion This experimental study revealed that the use of tannic acid during anastomosis has a positive effect on wound healing, acting through higher colonic anastomotic bursting pressures and higher tissue hydroxyproline levels.

AI based comparison of prefrontal cortex activity between piano-majors and non-piano-major musicians during score-based playing and Motif-improvisation

This study aimed to investigate how neural activation patterns differ between pianists and non-pianist musicians during piano performance tasks, using an LSTM-Autoencoder model applied to fNIRS data. A total of 22 participants, comprising both piano-majors and non-piano-majors, were involved in this study. Each participant’s performance data, collected during Score-based playing and improvisation tasks, was analyzed using a Long Short-Term Memory (LSTM) Autoencoder model. Reconstruction errors in specific brain channels, measured through Functional Near-Infrared Spectroscopy (fNIRS), showed group-level patterns—particularly in channels 1 and 15 (app. BA 45L, BA 45R)—but these differences did not reach statistical significance in inferential tests. These findings are therefore reported as exploratory and suggest distinct neural engagement patterns associated with varying levels of musical expertise.