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Correction: Magnitude and determinant factors of traditional uvulectomy practices among children under the age of five in Lemo District, Central Ethiopia
How China’s bold talent recruitment has shaped science
Unlocking the potential of ChatGPT in detecting the XCO2 hotspot captured by orbiting carbon observatory-3 satellite
An open-source platform for structured annotation and computational workflows in digital pathology research
Prevalence, characteristics and treatment of concomitant injury to liver and spleen with vascular injury after blunt abdominal trauma
Abstract Our purpose was to assess the prevalence of liver injuries as well as concomitant injuries to the liver and spleen in patients with blunt or penetrating abdominal trauma, and to determine the prevalence, management and outcome of active bleeding and contained vascular injuries (CVI; pseudoaneurysm/AV-fistula) seen on admission CT. A retrospective, single-center, longitudinal cohort study with nine-year data (2013–2021) of all ≥ 15-year-old patients with severe blunt or penetrating abdominal trauma and an ICD code for liver and/or splenic trauma. CT examinations were identified. Radiology, medical reports and images were reviewed and only patients with an adequate admission CT were included in the final study group. Of 2805 patients with abdominal trauma (71% males), 409 patients (14.6%) had a liver injury, and 329 had a CT on admission (329/409; 80.4%). 313 patients (11.2%) had a splenic injury and 262 had a CT (262/313; 83.7%). Of these, 65 patients or 2.3% (65/2805) had injury to both organs, with 49 patients with CT (49/65; 75.4%), combined group (CG) (79% males). The median (range) ISS was 21 (4–75) for single organ injury patients and 34 (9–75) for patients with both organs injured (p < 0.0001). Active liver or splenic bleeding was seen in 5.8% and 17.9%, respectively. In CG, 11 (11/49; 22.4%) patients had active bleeding. Of these, two patients had active bleeing in both organs (4.1%). Liver patients with active bleeding had significantly higher ISS (p = 0.025) than those without. In CG, ISS did not differ significantly between patients with and without active bleeding (p = 0.073), however, it tended to be higher in those with active bleeding. Most liver injuries with active bleeding were treated non-operatively (12/19; 63.2%). An active bleeding was more common in spleen than in liver patients; odds ratio (OR) (95% CI) 3.57 (2.04–6.25), p < 0.0001. A CVI was more common in splenic compared with liver injuries, OR 6.71 (95% CI; 2.27–19.9, p < 0.0001). Active bleeding was more common in CG than in single organ injury patients; OR 3.67 (1.73–7.79), p < 0.0016. 30-day survival rate did not differ between patients with or without active bleeding, but was slightly lower in CG compared with only liver injury (89.8% vs. 93.7%, p = 0.36). In conclusion the prevalence of liver injury in abdominal trauma seen on admission CT was 11.7% of all patients with blunt or penetrating abdominal trauma, and concomitant splenic and liver injury was seen in 1.7%. Non-operative management was applied in almost two thirds of patients with liver injuries. Active bleeding was seen in 5.8% of liver, 17.9% of splenic and 22.4% of CG patients. ISS was doubled in CG compared with single organ injury patients. Active bleeding was more common in CG, and CG had slightly increased mortality rate compared with single organ injury patients.
Multi attribute group decision-making based on quasirung orthopair fuzzy Frank aggregation operators for optimal vehicle selection
Quantum annealing feature selection on light-weight medical image datasets
Abstract We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations. We present a method to solve larger feature selection instances than previously demonstrated on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. We compare our approach against a range of feature selection strategies, including randomized baselines, classical supervised and unsupervised methods, combinatorial optimization via classical and quantum solvers, and learning-based feature representations. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware. While learned feature representations such as autoencoders achieve superior reconstruction performance, they do not offer the same level of interpretability or direct control over input feature selection as our approach.
Optimizing gibberellic acid concentration and exposure time for effective dormancy breaking and sprouting enhancement in potato
Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches
‘Wind droughts’ driven by climate change put green power at risk
Comparative numerical study of highly nonlinear flip-through impact experiment
Effects of natural Lithium and Lithium isotopes on voltage gated sodium channel activity in SH-SY5Y and IPSC derived cortical neurons
Sorafenib-treated Th9 cells exhibit superior anti-tumor effect in lung metastasis
Multi-module UNet++ for colon cancer histopathological image segmentation
Bacterial indicators of environmental stress in the gut microbiome of free-ranging European roe deer inhabiting agricultural landscapes
Abstract Environmental stressors can influence the gut microbiota of wild ruminants, yet their effects in free-ranging populations remain poorly understood. This study examined associations between physiological stress and gut microbiota composition in free-ranging European roe deer (Capreolus capreolus) from agricultural landscapes in central Poland. Fecal samples from 54 legally hunted individuals were analyzed for cortisol metabolite concentrations and bacterial community composition using 16S rRNA gene sequencing. Cortisol metabolite levels ranged from 17.9 to 371.4 ng ml⁻¹, allowing classification into low- and high-stress groups. Alpha diversity metrics did not differ between groups, but beta diversity analyses revealed significant differences in microbial community structure linked to stress. Stress remained a significant predictor of microbiota composition even after adjusting for confounding variables such as area and season, with its effect varying by context. Ratios of Christensenellaceae to Rikenellaceae, Bacteroidaceae and Prevotellaceae were significantly elevated in the high-stress group, indicating potential as microbial biomarkers of physiological stress. Additionally, Barnesiellaceae and Succinivibrionaceae (families involved in immune modulation and fermentation) were depleted under higher stress conditions. These findings highlight the role of gut microbiota in responses to environmental stressors and suggest that microbial signatures could serve as biomarkers for assessing the impact of agriculture on wildlife health and ecosystem stability.
Correlation of air pollution and risk of sudden sensorineural hearing loss: a Mendelian randomization study
3D-printed optogenetic neural probe integrated with microfluidic tube for opsin/drug delivery
Health professionals require more defined protocols, better funding and patient resources to support couples with recurrent pregnancy loss
Efficient applications of green synthesized CeO2/Bi2O3 nanocomposite for simultaneous electrochemical determination of Pb (II) and Cd (II) in real samples
Abstract In this study, Bi2O3/CeO2 nanocomposite was synthesized using serine, which played a dual role by promoting uniform particle morphology and aiding combustion during synthesis, resulting in a highly porous nanostructure. The produced nanocomposite was applied as a highly efficient modifier for screen-printed electrode (Bi2O3/CeO2/SPE), facilitating the simultaneous quantification of Pb(II) and Cd(II) using square wave anodic stripping voltammetry (SWASV). Compared to conventional sensors, the proposed electrode exhibited significantly enhanced electrochemical behavior, attributed to the synergistic structural and electrical properties of CeO₂ and Bi₂O₃ as well as an increased surface area. The sensor demonstrated a reliable response and effective peak separation at optimal parameters. The current signals exhibited linearity within the concentration range between 0.5 and 85 µg/L for both ions, achieving the limit of detection (LOD) of 0.09 µg/L for Pb(II) and 0.14 µg/L for Cd(II). The Bi2O3/CeO2/SPE was effectively utilized to detect cadmium and lead ions in water and food samples, demonstrating high recovery values across different spiked samples, and the outcomes closely matched those obtained through standard ICP analysis.