Browse Articles

Non-linear effects of self-compassion and negative psychological states on nurses’ work engagement

Extracorporeal hyperoxygenation therapy (EHT) for CO poisoning: in vitro and in vivo feasibility of a full-scale batch system

Abstract Carbon monoxide (CO) poisoning is one of the most common causes of injury and death from poisoning. The primary objective of therapy is to eliminate CO from the patient as quickly as possible to prevent acute and long-term effects. The ideal treatment is hyperbaric oxygen in a pressure chamber. However, pressure chambers are scarce, and therefore, the most common treatment is normobaric oxygen (NBO), which, however, has limited efficacy. Here, we present a full-scale batch system for extracorporeal hyperoxygenation therapy (EHT), which facilitates CO elimination extracorporeally by increasing the dissolved oxygen concentration in the blood. The EHT was characterized in vitro, resulting in a minimum carboxyhemoglobin half-life of 3.26 ± 0.11 min. In large animal trials the EHT reduced the median carboxyhemoglobin half-life by 42% (29.77 min EHT vs. 70.8 min control (NBO)). However, the EHT also induced oscillations in hemodynamic pressures due to changes in the animals’ circulatory volume during operation. After optimization, the EHT could be a promising option for treating CO poisoning.

An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network

Abstract Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnosed individual. Severe cases of DFU can lead to lower limb amputation in diabetic patients, making their diagnosis a complex and costly process that poses challenges for medical professionals. Manual identification of DFU is particularly difficult due to their diverse visual characteristics, leading to multiple cases going undiagnosed. To address this challenge, Deep Learning (DL) methods offer an efficient and automated approach to facilitate timely treatment and improve patient outcomes. This research proposes a novel feature fusion-based model that incorporates two parallel tracks for efficient feature extraction. The first track utilizes the Swin transformer, which captures long-range dependencies by employing shifted windows and self-attention mechanisms. The second track involves the Efficient Multi-Scale Attention-Driven Network (EMADN), which leverages Light-weight Multi-scale Deformable Shuffle (LMDS) and Global Dilated Attention (GDA) blocks to extract local features efficiently. These blocks dynamically adjust kernel sizes and leverage attention modules, enabling effective feature extraction. To the best of our knowledge, this is the first work reporting the findings of a dual track architecture for DFU classification, leveraging Swin transformer and EMADN networks. The obtained feature maps from both the networks are concatenated and subjected to shuffle attention for feature refinement at a reduced computational cost. The proposed work also incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to visualize and interpret the decision making of the network. The proposed model demonstrated better performance on the DFUC-2021 dataset, surpassing existing works and pre-trained CNN architectures with an accuracy of 78.79% and a macro F1-score of 80%.

Sensory seeking and its influence on sustained attention performance in adult males with Autism Spectrum Condition

Crystal structure modulating performances for 213-nm GeO2 solar-blind photodetectors via DC reactive magnetron sputtering method

Impact of monthly air pollution and weather conditions on cardiorespiratory mortality in Portuguese Metropolitan Areas

Dissociative experiences alter resting state functional connectivity after childhood abuse

Abstract Dissociative experiences commonly occur alongside adverse childhood experiences (ACE), yet research on their neurofunctional biomarkers has overlooked their unique association with dimensions of childhood abuse and neglect. We investigated interactions between dissociative experiences and childhood abuse, anticipating anti-correlations between the right-lateralized anterior middle frontal gyrus (raMFG) and the medial temporal lobe, as well as the temporal gyri. Examining resting-state functional connectivity in 91 participants with a history of ACE, we employed seed-to-voxel analyses seeding the raMFG. Multiple linear regression and post-hoc moderation/mediation models explored interactions and individual effects of dissociation and dimensions of ACE. The Dissociative Experiences Scale (DES) and Childhood Trauma Questionnaire (CTQ) quantified dissociation and dimensions of ACE. A DES by CTQ-A (childhood abuse) interaction predicted an anti-correlation between the raMFG and right hippocampus, moderated by CTQ-A. The CTQ revealed negative connectivity between the raMFG and right anterior cingulate cortex. CTQ-N (childhood neglect) indicated that both the right supplementary motor area and right insula related positively to the raMFG. Our findings underscore a distinct neural signature of childhood abuse-related dissociative experiences, potentially linked to dissociated memories.

Cost-effective adsorption of cationic dyes using ZnO nanorods supported by orange peel-derived carbon

Abstract Here, porous carbon (PC) and ZnO nanorods@PC (ZnO-NR@PC) composite derived from orange peel (OP) have been synthesized via a simple carbonization process. The prepared materials have been characterized by XRD, FT-IR, TEM, and BET analysis. The adsorptive properties of the prepared PC and ZnO-NR@PC composite have been investigated toward methylene blue (MB) and crystal violet (CV) cationic dyes from their aqueous solutions. The adsorption studies concluded that the maximum adsorption efficiency was achieved after 90 min in the basic conditions (pH = 10). Langmuir, Freundlich, Dubinin–Radushkevich (D-R), and Temkin non-linear isotherm models were applied to fit the experimental data. The adsorption of MB and CV dyes by the OP is fitted with the Freundlich model, and the adsorption of both dyes by the PC and the ZnO-NR@PC composite fitted with the Langmuir model. The estimated maximum adsorption capacity estimated from the adsorption of MB and CV by the ZnO-NR@PC composite was 74.45 and 74.89 mg/g, respectively. The calculated adsorption free energy from D-R and Temkin models indicates the adsorption of MB, and CV dye molecules by the OP, PC, and ZnO-NR@PC composite may be physical. The kinetic studies revealed the adsorption of MB and CV dyes onto the OP, PC and ZnO-NR@PC composite fitted with the pseudo-second-order model. On the otherhand, the thermodynamic studies confirmed the adsorption of MB, and CV dyes onto ZnO-NR@PC composite is an endothermic and spontaneous process. Furthermore, the prepared materials displayed high adsorption stability with an overall removal efficiency of about 90% after five cycles. The mechanism of MB and CV dyes by the ZnO-NR@PC composite is proposed to be controlled by electrostatic bonding, π-π interactions, and ion exchange. The results indicated the potential ability of OP-derived porous carbons as adsorbents for cationic dyes from aqueous media.

Author Correction: Normal tissue radioprotection by amifostine via Warburg-type effects

Investigating risk factors for migraine in Syrian women: a cross-sectional case-control study

Establishment of an animal model for monkeypox virus infection in dormice

Isotemporal substitution of sedentary time with different physical activity intensities and sleep in obesity parameters across eight latin American countries

Associations between phenol and paraben exposure and the risk of developing breast cancer in adult women: a cross-sectional study

Mechanical properties and mechanism of damage and deterioration of coal under cyclic loading

Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning

Nitrogen enrichment and vascular plant richness loss reduce bryophyte richness

Abstract Grasslands’ high diversity is threatened by land-use changes, such as nitrogen fertilization, leading to productive but low-richness, fast-growing plant communities. Bryophytes are a key component of grassland diversity and react strongly to land use. However, it is unclear whether land-use effects are direct or mediated by changes in vascular plants. Increases in vascular plant cover are likely to decrease bryophyte abundance through light competition. Whether changes in vascular plant composition and richness also play a role remains unclear. We sampled bryophytes in a factorial grassland experiment manipulating nitrogen fertilization, fungicide, species richness, and functional composition of vascular plants crossed with moderate disturbances by weeding. Disturbance increased bryophyte richness and modulated treatment effects. In contrast to previous studies reporting indirect negative fertilization effects via increasing vascular plant productivity and reduced light levels, nitrogen fertilization directly reduced bryophyte cover and species richness, possibly because of toxic effects. Low vascular plant richness and dominance of fast-growing species reduced bryophyte richness. This might be because of decreased structural and resource niche heterogeneity in species-poor communities. Our results highlight novel mechanisms by which land-use intensification can affect bryophytes and suggest that a loss of vascular plant richness might have cascading effects on other taxonomic groups.

Survival and risk factors for metastatic colorectal cancer patients with a history of prior malignancy

Quantum-inspired K-nearest neighbors classifier for enhanced printer source identification in forensic document analysis

Abstract Document source identification in printer forensics focuses on determining the source printer of a document by analyzing characteristics such as printer model, serial number, defects, or unique artifacts. This is crucial in forensic investigations involving counterfeit documents or anonymous threats. However, identifying consistent patterns across different printers remains challenging, especially when perpetrators attempt to obscure these artifacts. Machine learning models in this field must identify discriminative features that differentiate printers while minimizing noise. In particular, choosing an appropriate distance metric for K-Nearest Neighbors (KNN) classifiers is critical and requires experimentation. This study proposes a quantum-inspired approach to improve KNN’s performance in printer source identification. By exploring alternative number of neighbors (K), quantum-inspired computing can optimize feature space calculations, even in noisy conditions. This allows the system to iteratively refine and select the optimal K value based on classification performance, ensuring that the best K is identified for the specific dataset and task. The system utilizes the Grey Level Co-occurrence Matrix (GLCM) for feature extraction, which is robust to changes in rotation and scale. Experimental results demonstrate that the Quantum-inspired KNN (QKNN) classifier outperforms classical KNN, achieving higher accuracy in identifying subtle printing artifacts, even under variable conditions.

Inhibition of GPR68 induces ferroptosis and radiosensitivity in diverse cancer cell types

Association between parental education level and intelligence quotient of children referred to the mental healthcare system: a cross-sectional study in Poland

Abstract Considering the gap in understanding of the link between parental education and child intelligence quotient (IQ), our study aimed to investigate the association between parental education and the IQ of children referred to the mental healthcare system, explore which parent’s education level is more influential, and examine the impact of the child’s age and sex on these relationships. This cross-sectional study included 80,303 children aged 3–18 years who were referred to the mental healthcare system between 2018 and 2023. We predefined IQ composite score (Full IQ Scale), as measured by the Stanford Binet 5 Intelligence Scale, Fifth Edition (SB-5) as the primary outcome; the remaining SB-5 composite scores were the secondary outcomes. Linear regression analysis was performed using staircase coding for ordinal predictors with several binary independent variables. A significant correlation was found between parental education levels and IQ of the sampled children, with higher levels of parents’ education predicting higher IQ scores, particularly with mother’s education explaining 18.23% of the variance in children’s overall intelligence. No significant interaction was observed between parental education and child’s sex in predicting child IQ. However, a significant interaction was observed with age, showing that IQ decreases with age in children of parents with lower education, while it increases with age in children of parents with higher education. Our study underscores the pivotal influence of parental education on the IQ levels of children referred to mental healthcare services. Maternal education level was a stronger predictor of child IQ, potentially because mothers tend to be the primary caregivers. These findings suggest the need for targeted support programs for caregivers, particularly those with lower education levels, to facilitate the early detection of developmental challenges. Integrated education and healthcare efforts are crucial for equitable mental healthcare access.