Scientific Reports最新文献

Lumbar foraminal stenosis can be surgically treated by foraminal decompression or facet joint resection and fusion (transforaminal lumbar interbody fusion, TLIF). While conventional foraminal decompression poses a risk of segmental instability, the endoscopic approach (extended endoscopic lumbar foraminotomy, EELF) resects only the ventral part of the facet joint with a horizontal surgical trajectory. A prospective observational study was performed to analyze the cost-effectiveness of EELF versus TLIF. Patients with dominant unilateral radicular pain from lumbar foraminal stenosis at or above L4-5, without severe central stenosis or instability, were included from January 2021 to February 2023. EELF involved sufficient foraminal widening using a reamer, followed by an endoscopic procedure. The primary outcome was the cost per quality-adjusted life year (QALY) gain at postoperative 12 months. Among 26 patients in each group, the primary analysis included 23 EELF patients (mean age: 72 ± 8 years) and 22 TLIF patients (mean age: 70 ± 8 years). EELF was significantly more cost-effective (EELF: $15,536.0 ± 4,190.0/QALY vs. TLIF: $32,869.4 ± 5,429.3/QALY, p < .001) and demonstrated shorter operating times, less blood loss, and shorter length of stay (p < .05), with no significant difference in clinical outcomes. Thus, EELF could be a cost-effective and less invasive alternative for treating lumbar foraminal stenosis.

Accurately predicting the phase behavior and properties of reservoir fluid plays an essential role in the simulation of petroleum recovery processes. Similar to the inaccurate liquid-density prediction issue in the isobaric-isothermal (PT) phase equilibrium calculations, an inaccurate pressure prediction issue can also be observed in isothermal-isochoric (VT) phase equilibrium calculations which involves a liquid phase. In this work, a practical methodology is proposed to incorporate a volume-translated equation of state in VT phase equilibrium calculations for more accurate pressure predictions. For this purpose, we adopt the state-of-art volume translation model recently proposed by Abudour et al. (Fluid Phase Equilib 349:37-55 2012, Fluid Phase Equilib 349:37-55, 2013). Single liquid phase calculations for 18 compounds and two hydrocarbon mixtures are conducted to demonstrate the soundness of the proposed methodology and evaluate the accuracy of pressure predictions. The calculated pressures by VT calculations with volume translated PR-EOS are compared to the actual pressures. The calculation results demonstrate that, by incorporating Abudour et al. (2012, 2013) volume translated PR-EOS models into the VT-based phase equilibrium calculation algorithm, the accuracy of pressure prediction in the single liquid phase region for both pure substances and mixtures can be significantly improved. Lastly, we apply the proposed algorithm to the two-phase VT phase equilibrium calculations for a ten-component oil sample MY10 which contains only normal alkanes. We numerically correct the pressure by applying the Abudour et al. VTPR-EOS to both the liquid phase and vapor phase. The pressures calculated by different phases become different. The pressures predicted based on the liquid phase are shifted downwards significantly, which leads to more accurate pressure predictions. To our knowledge, this issue is rarely investigated to incorporate the volume translation concept in VT phase equilibrium calculations.

To assess retinal pigment epithelium (RPE) tears in eyes which underwent pars plana vitrectomy (PPV) for submacular hemorrhage (SMH) secondary to age-related macular degeneration and to investigate the prognostic factors of visual outcomes. This study was a retrospective, observational case series that included 24 eyes of 24 patients who underwent PPV with subretinal tissue plasminogen activator and air for SMH. RPE tears were investigated using spectral-domain or swept-source optical coherence tomography images with raster scan, combined confocal scanning laser ophthalmoscope near-infrared images and color fundus photographs. Multiple regression analysis was performed to identify the predictors of visual outcome at month 3 and 6 after surgery. In 21 eyes out of 24 eyes (87.5%), RPE tear was detected in the posterior pole. Eight eyes (33.3%) had large RPE tears ( ≧ 1 disc diameter (DD)). Out of the 8 eyes, 5 eyes progressed fibrotic scars within 3 months despite successful SMH removal and showed decrease in their visual acuity (VA). The ratio of eyes with large RPE tears ( ≧ 1DD) were significantly higher in eyes which had undergone anti-VEGF therapy within 3 months than in treatment-naïve eyes (71.4% vs. 25.0%, p = 0.048). The multiple regression analysis revealed that a small RPE tear (< 1DD) and treatment-naïve condition were associated with better VA at month 3 and 6. SMH within 3 months after anti-VEGF therapy might be accompanied by a large RPE tear. An RPE tear which was smaller than 1DD and treatment-naïve condition were associated with better prognosis.

The benefit of aspirin in primary prevention for atherosclerotic cardiovascular diseases (ASCVD) is questionable due to bleeding complications. We analyzed the Korean National Health Insurance data to compare the efficacy and overall bleeding of sarpogrelate, an antiplatelet agent with lower bleeding risk, versus aspirin in high-/very-high-risk diabetic populations without prior ASCVD. The primary endpoint was net adverse clinical events (NACE), defined as a composite of efficacy and overall bleeding. The efficacy was a composite of all-cause death, myocardial infarction (MI) and stroke, whereas overall bleeding included intracranial hemorrhage (ICH) and gastrointestinal (GI) bleeding. A total of 10,778 high-/very-high-risk diabetic patients (9550 on aspirin, 1228 on sarpogrelate) were analyzed. After propensity score matching, sarpogrelate was linked to a lower incidence of NACE (HR:0.71; 95% CI 0.57-0.88), mainly driven by 62% reductions in overall bleeding (0.38; 0.17-0.81), a composite of 64% and 72% lower rate of GI bleeding and ICH, respectively. Additionally, there was no significant differences in MI or stroke between groups. In high- or very-high-risk diabetic patients without ASCVD, sarpogrelate use was associated with net clinical benefit mainly due to the reduction of significant reduction in overall bleeding events.

Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability. Therefore, in order to achieve these objectives, the combination of Aquila and Salp Swarm Algorithms (ASSA) is used to select the best Virtual Machines (VMs) for the execution of workflows. So, in each iteration of ASSA execution, a number of VMs are selected by the ASSA. Then by using the Reducing MakeSpan Time (RMST) technique, the MST of the workflow on selected VMs is reduced, while maintaining reliability and deadline. Then, using VM merging and Dynamic Voltage Frequency Scaling (DVFS) technique on the output from RMST, the static and dynamic EC is reduced, respectively. Experimental results show the effectiveness of the proposed method compared to previous methods.

In this study, we investigated the effect of severe dysphagia on the overall survival of patients who underwent PEG. A cohort of patients who underwent PEG between April 2016 and April 2021 was retrospectively analyzed. The Hyodo-Komagane score was used to evaluate swallowing via endoscopy. Patients with a Hyodo-Komagane score ≤ 8 were defined as having moderate dysphagia, whereas those with a score ≥ 9 were defined as having severe dysphagia. The primary outcome was overall survival. The prognostic factors were identified using multivariate analysis with the Cox proportional hazards model. Values of p < 0.05 were considered statistically significant. Among the 107 patients, 60 (56.1%) were classified into the moderate dysphagia group and 47 (43.9%) into the severe dysphagia group. The median follow-up period was 16.7 months. The overall survival was significantly worse in the severe group than in the moderate group (p < 0.0001). A multivariate analysis revealed that severe dysphagia was an independent poor prognostic factor (hazard ratio, 2.956; 95% confidence interval, 1.592-5.489; p < 0.001). Aspiration-related pneumonia was most common causes of death after PEG. Severe dysphagia was identified as an independent poor prognostic factor for survival in patients who underwent PEG.

Biodiesel presents a sustainable alternative to fossil fuels, yet traditional homogeneous catalysts like sodium and potassium hydroxide face challenges with separation and reuse. Calcium oxide (CaO) is an effective heterogeneous catalyst for biodiesel production, but its chemical instability under reaction conditions restricts its long-term performance. This study introduces MOF-mediated synthesis (MOFMS) of heterogeneous catalysts, specifically CaO@ZnO and ZnO@CaO nanocomposites, from inexpensive and non-toxic metal salts and linkers in water. Comprehensive characterization techniques, including XRD, FT-IR, BET, FE-SEM, ICP, and CO₂-TPD, were employed to analyze these catalysts. When applied to biodiesel production from soybean oil at ambient temperature and pressure, CaO@ZnO and ZnO@CaO achieved impressive biodiesel conversion rates of 99% and 92%, respectively, within 25 min. Both catalysts maintained their activity over six utilization cycles, with Ca²⁺ leaching remaining below 4% (2% for CaO@ZnO and 4% for ZnO@CaO) after the sixth run. These results provide valuable insights into catalyst preparation and leaching control, enhancing reusability in biodiesel production. Future research should aim to improve the long-term stability and reusability of these catalysts, investigate their performance with various feedstocks, and evaluate the feasibility for industrial applications.

Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken. Data augmentation maneuvers permitted input of merely 514 frontal photographs of cleft-affected faces adapted to a base model of 70,000 normal faces. The Frechet Inception Distance was used to measure the similarity of the newly generated facial images to the cleft training dataset. Perceptual Path Length and the novel Divergence Index of Normality measures also assessed the performance of the novel image generator. CleftGAN generates vast numbers of unique faces depicting a wide range of cleft lip deformity with variation of ethnic background. Performance metrics demonstrated a high similarity of the generated images to our training dataset and a smooth, semantically valid interpolation of images through the transfer learning process. The distribution of normality for the training and generated images were highly comparable. CleftGAN is a novel instrument that generates an almost boundless number of realistic facial images depicting cleft lip. This tool promises to become a valuable resource for the development of machine learning models to objectively evaluate facial form and the outcomes of surgical reconstruction.

Fiber-reinforced polymer composites are subjected to harsh environmental conditions over the course of their designed lifespan. Studying the aging process of fiber-reinforced polymer composites exposed to boiling water is critical for improving their durability. This study uses a hand lay-up technique to fabricate composites from glass fiber, bamboo fiber, nanoclay, and epoxy. Tensile and flexural tests are conducted following ASTM standards. The % of water uptake of the boiling water-soaked specimens is measured, and the performance of the dry composites is compared with those of boiling water-soaked composites. The results show that boiling water-soaking conditions have an adverse impact on all the composites. Boiling water-soaked epoxy samples show a reduction in tensile properties by 25 and 30% and flexural properties by 18 and 22% under processes 1 and 2 of boiling water soaking, respectively, compared to dry ones. Meanwhile, for fiber-reinforced composites, the tensile properties decrease by 19 and 27%, and the flexural properties decline by 12 and 20% under processes 1 and 2 of boiling soaking, respectively. However, incorporating nanoclay enhances the tensile and flexural properties of the epoxy and the composites by 5 to 7% and 10 to 12%, respectively. The water absorption rate and the impact of boiling water-soaking on composite strength decrease with the addition of nanoclay. Additionally, nanoclay reduces the percentage of reduction in tensile properties by 17 and 26% and in flexural properties by 11 and 18% under processes 1 and 2 of boiling soaking, respectively. SEM analysis of the fracture surfaces reveals the causes of specimen failure under tensile load, with distinct differences between dry and boiling water-soaked specimens.

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and [Formula: see text] of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.