Search Results (24,846)

Background: Acute kidney injury (AKI) frequently occurs following major liver resection, adversely affecting both short- and long-term outcomes. This study aimed to determine the incidence of AKI post-hepatectomy and identify relevant pre- and intraoperative risk factors. Our secondary objectives were to develop a predictive score for postoperative AKI and assess the associations between AKI, chronic kidney disease (CKD), and 1-year mortality. Methods: This was a retrospective study in a cancer referral center in Marseille, France, from 2018 to 2022. Results: Among 169 patients, 55 (32.5%) experienced AKI. Multivariate analysis revealed several independent risk factors for postoperative AKI, including age, body mass index, the use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, time to liver resection, intraoperative shock, and bile duct reconstruction. Neoadjuvant chemotherapy was protective. The AKIMEBO score was developed, with a threshold of ≥15.6, demonstrating a sensitivity of 89.5%, specificity of 76.4%, positive predictive value of 61.8%, and negative predictive value of 94.4%. AKI was associated with increased postoperative morbidity and one-year mortality following major hepatectomy. Conclusion: AKI is a common complication post-hepatectomy. Factors such as time to liver resection and intraoperative shock management present potential clinical intervention points. The AKIMEBO score can provide a valuable tool for postoperative risk stratification. Full article

Background and Objective: This study presents a combined osteometric and biomechanical analysis of a Korean female cadaver exhibiting bilateral lower limb bone asymmetry with abnormal curvature and callus formation on the left femoral midshaft. Methods: To investigate bilateral bone length differences, osteometric measurements were conducted at standardized landmarks. Additionally, we developed three gait models using Meta Motivo, an open-source reinforcement learning platform, to analyze how skeletal asymmetry influences stride dynamics and directional control. Results: Detailed measurements revealed that the left lower limb bones were consistently shorter and narrower than their right counterparts. The calculated lower limb lengths showed a bilateral discrepancy ranging from 39 mm to 42 mm—specifically a 6 mm difference in the femur, 33 mm in the tibia, and 36 mm in the fibula. In the gait pattern analysis, the normal model exhibited a straight-line gait without lateral deviation. In contrast, the unbalanced, non-learned model demonstrated compensatory overuse and increased stride length of the left lower limb and a tendency to veer leftward. The unbalanced, learned model showed partial gait normalization, characterized by reduced limb dominance and improved right stride, although directional control remained compromised. Conclusions: This integrative approach highlights the biomechanical consequences of lower limb bone discrepancy and demonstrates the utility of virtual agent-based modeling in elucidating compensatory gait adaptations. Full article

Microbiome analysis has revolutionized our understanding of various biological processes, spanning human health and epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathways, and community structure. In this study, we address the primary biases encountered in reconstructing networks through correlation measures, particularly in light of the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these issues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson’s correlation to correctly identify positive and negative relationships among relative abundances, especially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations. Full article

This communication presents an accurate and computationally efficient approach for wideband radar cross-section (RCS) estimation and scattering point reconstruction using infinitesimal dipole modeling (IDM) with compressive sensing. The proposed method eliminates the need for field sampling at numerous frequency points across the wideband range through Green’s function adjustment. Additionally, compressive sensing is employed for induced current calculation to reduce both frequency and angular sampling requirements. Numerical validation demonstrates that the method achieves a 50% reduction in field sample data and an 82.3% reduction in IDM processing time while maintaining comparable accuracy through Green’s function adjustment. Furthermore, compared to approaches without compressive sensing, the method shows a 55.1% and a 75.5% reduction in error in averaged RCS for VV-pol and HH-pol, respectively. The proposed method facilitates efficient wideband RCS estimation of various targets while significantly reducing measurement complexity and computational cost. Full article

Objectives: This study investigates whether scan time in the high-resolution magnetic resonance imaging (MRI) of human embryos can be reduced without compromising spatial resolution by applying zero-shot self-supervised learning (ZS-SSL), a deep-learning-based reconstruction method. Methods: Simulations using a numerical phantom were conducted to evaluate spatial resolution across various acceleration factors (AF = 2, 4, 6, and 8) and signal-to-noise ratio (SNR) levels. Resolution was quantified using a blur-based estimation method based on the Sparrow criterion. ZS-SSL was compared to conventional compressed sensing (CS). Experimental imaging of a human embryo at Carnegie stage 21 was performed at a spatial resolution of (30 μm)³ using both retrospective and prospective undersampling at AF = 4 and 8. Results: ZS-SSL preserved spatial resolution more effectively than CS at low SNRs. At AF = 4, image quality was comparable to that of fully sampled data, while noticeable degradation occurred at AF = 8. Experimental validation confirmed these findings, with clear visualization of anatomical structures—such as the accessory nerve—at AF = 4; there was reduced structural clarity at AF = 8. Conclusions: ZS-SSL enables significant scan time reduction in high-resolution MRI of human embryos while maintaining spatial resolution at AF = 4, assuming an SNR above approximately 15. This trade-off between acceleration and image quality is particularly beneficial in studies with limited imaging time or specimen availability. The method facilitates the efficient acquisition of ultra-high-resolution data and supports future efforts to construct detailed developmental atlases. Full article

Background and Objectives: Total hip arthroplasty (THA) following pelvic osteotomy for developmental dysplasia of the hip (DDH) is technically challenging due to altered acetabular morphology. This study aimed to compare radiographic cup position and perioperative characteristics of THA after three common pelvic osteotomies—periacetabular osteotomy (PAO), shelf procedure, and Chiari osteotomy—with primary THA in Crowe type I DDH. Methods: A retrospective review identified 25 hips that underwent conversion THA after pelvic osteotomy (PAO = 12, shelf = 8, Chiari = 5) and 25 primary THAs without prior osteotomy. One-to-one matching was performed based on sex (exact match), age (within 5 years), and BMI (within 2 kg/m²) without the use of propensity scores. Cup inclination, radiographic anteversion, center-edge (CE) angle, and cup height were measured on standardized anteroposterior radiographs (ICC = 0.91). Operative time, estimated blood loss, and use of bulk bone grafts or reinforcement rings were reviewed. One-way ANOVA with Dunnett’s post hoc test and chi-square test were used for statistical comparison. Results: Cup inclination, anteversion, and CE angle did not differ significantly among groups. Cup height was significantly greater in the PAO group than in controls (29.0 mm vs. 21.8 mm; p = 0.0075), indicating a more proximal hip center. The Chiari and shelf groups showed upward trends, though not significant. Mean operative time tended to be longer after PAO (123 min vs. 93 min; p = 0.078). Bulk bone grafts and reinforcement rings were more frequently required in the PAO group (17%; p = 0.036 vs. control), and occasionally in Chiari cases, but not in shelf or control groups. Conclusions: THA after PAO is associated with higher cup placement and greater need for reconstructive devices, indicating increased technical complexity. In contrast, shelf and Chiari conversions more closely resemble primary THA. Preoperative planning should consider hip center translation and bone-stock restoration in post-osteotomy THA. Full article

This article presents VRF-DETR, a lightweight real-time object detection framework for aerial remote sensing images, aimed at addressing the challenge of insufficient receptive fields for easily confused categories due to differences in height and perspective. Based on the RT-DETR architecture, our approach introduces three key innovations: the multi-scale receptive field adaptive fusion (MSRF²) module replaces the Transformer encoder with parallel dilated convolutions and spatial-channel attention to adjust receptive fields for confusing objects dynamically; the gated multi-scale context (GMSC) block reconstructs the backbone using Gated Multi-Scale Context units with attention-gated convolution (AGConv), reducing parameters while enhancing multi-scale feature extraction; and the context-guided fusion (CGF) module optimizes feature fusion via context-guided weighting to resolve multi-scale semantic conflicts. Evaluations were conducted on both the VisDrone2019 and UAVDT datasets, where VRF-DETR achieved the mAP₅₀ of 52.1% and the mAP_50-95 of 32.2% on the VisDrone2019 validation set, surpassing RT-DETR by 4.9% and 3.5%, respectively, while reducing parameters by 32% and FLOPs by 22%. It maintains real-time performance (62.1 FPS) and generalizes effectively, outperforming state-of-the-art methods in accuracy-efficiency trade-offs for aerial object detection. Full article

Aquatic macrophytes are a vital component of lake ecosystems, profoundly influencing ecosystem structure and function. Under future scenarios of more frequent extreme floods and intensified lake eutrophication, aquatic macrophytes will face increasing challenges. Therefore, understanding aquatic macrophyte responses to flood disturbances and nutrient enrichment is crucial for predicting future vegetation dynamics in lake ecosystems. This study focuses on Huangmaotan Lake, a Yangtze River floodplain lake, where we reconstructed 200-year successional trajectories of macrophyte communities and their driving mechanisms. With a multiproxy approach we analyzed a well-dated sediment core incorporating plant macrofossils, grain size, nutrient elements, heavy metals, and historical flood records from the watershed. The results demonstrate a significant shift in the macrophyte community, from species that existed before 1914 to species that existed by 2020. Unlike the widespread macrophyte degradation seen in most regional lakes, this lake has maintained clear-water plant dominance and experienced continuous vegetation expansion over the past 50 years. We attribute this to the interrelated effects of floods and the enrichment of ecosystems with nutrients. Specifically, our findings suggest that nutrient enrichment can mitigate the stress effects of floods on aquatic macrophytes, while flood disturbances help reduce excess nutrient concentrations in the water column. These findings offer applicable insights for aquatic vegetation restoration in the Yangtze River floodplain and other comparable lake systems worldwide. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Consumer interest in vegetarian, ethical, and clean-label foods is reviving the use of plant-derived milk coagulants. Cardosins from Cynara cardunculus (“thistle”) are aspartic proteases with strong clotting activity, yet their technological impact in cheese remains under-explored. This study compared a commercial thistle extract (PC) with traditional bovine rennet rich in chymosin (AC) during manufacture and 60-day ripening of Caciotta cheese. Classical compositional assays (ripening index, texture profile, color, solubility) were integrated with scanning electron microscopy, three-dimensional surface reconstruction, and descriptive sensory analysis. AC cheeses displayed slower but sustained proteolysis, yielding a higher and more linear ripening index, softer body, greater solubility, and brighter, more yellow appearance. Imaging revealed a continuous protein matrix with uniformly distributed, larger pores, consistent with a dairy-like sensory profile dominated by milky and umami notes. Conversely, PC cheeses underwent rapid early proteolysis that plateaued, producing firmer, chewier curds with lower solubility and darker color. Micrographs showed a fragmented matrix with smaller, heterogeneous pores; sensory evaluation highlighted vegetal, bitter, and astringent attributes. The data demonstrate that thistle coagulant can successfully replace animal rennet but generates cheeses with distinct structural and sensory fingerprints. The optimization of process parameters is therefore required when targeting specific product styles. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

Background and Objectives: There is a global rise of public interest in integrative medicine. The principles of integrative medicine combining conventional medicine with evidence-based complementary therapies have been implemented in many medical areas, including plastic surgery, to improve patient’s outcome. The aim of the present study was to systematically analyze the application and use of additional non-pharmacological interventions (NPIs) of patients of a German department of plastic surgery. Materials and Methods: The present real-world data study utilized data from the Network Oncology registry between 2016 and 2021. Patients included in this study were at the age of 18 or above, stayed at the department of plastic surgery and received at least one plastic surgical procedure. Adjusted multivariable logistic regression analyses were performed to detect associations between the acceptance of NPIs and predicting factors such as age, gender, year of admission, or length of hospital stay. Results: In total, 265 patients were enrolled in the study between January 2016 and December 2021 with a median age of 65 years (IQR: 52–80) and a male/female ratio of 0.77. Most of the patients received reconstructive surgery (90.19%), followed by hand surgery (5.68%) and aesthetic surgery (2.64%). In total, 42.5% of the enrolled patients accepted and applied NPIs. Physiotherapy, rhythmical embrocations, and compresses were the most often administered NPIs. Conclusions: This exploratory analysis provides a descriptive overview of the application and acceptance of NPIs in plastic surgery patients within a German integrative care setting. While NPIs appear to be well accepted by a subset of patients, further prospective studies are needed to evaluate their impact on clinical outcomes such as postoperative recovery, pain management, patient-reported quality of life, and overall satisfaction with care. Full article

Main roads are usually equipped with traffic flow monitoring devices in the road network to record the traffic flow data of the main roads in real time. Three complex scenarios, i.e., Y-junctions, multi-lane merging, and signalized intersections, are considered in this paper by developing a novel modeling system that leverages only historical main-road data to reconstruct branch-road volumes and identify pivotal time points where instantaneous observations enable robust inference of period-aggregate traffic volumes. Four mathematical models (I–IV) are built using the data given in appendix, with performance quantified via error metrics (RMSE, MAE, MAPE) and stability indices (perturbation sensitivity index, structure similarity score). Finally, the significant traffic flow change points are further identified by the PELT algorithm. Full article

Background/Objectives: Oral cancer resection often leads to maxillofacial defects and dentition loss, compromising patients’ quality of life. Implant-supported prosthetic rehabilitation offers a reliable solution to restore function, though factors such as bone reconstruction, radiotherapy, and timing of implant placement (immediate vs. delayed) may influence outcomes. This study aimed to evaluate long-term implant survival and rehabilitation timelines in oncologic patients, comparing two cohorts (2005–2014 and 2015–2024) to assess the impact of evolving clinical practices. Methods: A retrospective cohort study was conducted at Hospital General Universitario Gregorio Marañón (Madrid, Spain), including 304 patients who underwent ablative oral cancer surgery and subsequent implant-based rehabilitation between 2005 and 2024. Data on demographics, oncologic treatment, reconstruction, implant timing, and prosthetic rehabilitation were collected. Outcomes were compared using Kaplan–Meier analysis and appropriate statistical tests between the 2005–2014 (n = 122) and 2015–2024 (n = 182) cohorts. Results: A total of 2341 Ticare Implants^® were placed, supporting 281 prostheses. Implant placement during primary surgery increased from 41% to 71% (p < 0.001). The median time from surgery to prosthesis significantly decreased from 24 to 15 months (p < 0.001). Five-year implant survival was 95% in the early cohort versus 97% in the later cohort. Implant survival was comparable between irradiated and non-irradiated patients (~94–96%). Fixed prostheses became more frequent (92% vs. 79%, p = 0.002). Conclusions: Implant-supported rehabilitation in oncologic patients is highly feasible and durable, with improved timelines and functional outcomes associated with early implant placement and modern digital planning strategies. Full article