Search Results (100)

The advent of recycling antibodies, leveraging pH-dependent antigen binding and optimized FcRn interaction, has advanced the field of antibody therapies, enabling extended durability and reduced dosages. Eculizumab (Soliris^®) demonstrated the efficacy of C5 inhibitors for paroxysmal nocturnal hemoglobinuria (PNH), while its derivative, ravulizumab (Ultomiris^®), recognized as a recycling antibody, extended the dosing intervals. However, limitations including intravenous administration and inefficacy in patients with the R885H single-nucleotide polymorphism (SNP) in C5 could necessitate alternative solutions. Crovalimab (PiaSky^®), a next-generation recycling antibody, overcomes these challenges with innovative charge engineering, achieving the enhanced cellular uptake of C5–crovalimab complexes and targeting a unique C5 epitope, allowing for efficacy regardless of the R885H SNP. This study highlights crovalimab’s distinctive molecular features, showing its eliminated binding to Fcγ receptors and C1q, alongside its optimized antigen binding characteristics. The impact of charge engineering was reconfirmed in mice, demonstrating faster C5 clearance than recycling antibodies. Notably, in the maintenance dosing regimen, crovalimab neutralizes approximately seven C5 molecules per antibody on average. Furthermore, its design also reduces the viscosity to facilitate high-concentration formulations suitable for subcutaneous delivery. Consequently, crovalimab offers a four-weekly subcutaneous injection regimen for PNH, marking a substantial improvement in treatment convenience and potentially transforming patients’ quality of life. Full article

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model’s adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model’s potential as a reliable tool for real-time RUL prediction in industrial applications. Full article

Background: The 2015 American Thyroid Association guidelines recommend the maintenance of serum thyroid stimulating hormone (TSH) levels ≤2 mIU/L in patients with low-risk papillary thyroid carcinoma (PTC) who underwent lobectomy; however, the evidence is insufficient. We investigated the association between maintaining the TSH status at ≤2 mIU/L and tumor recurrence in patients with low-risk PTC who underwent lobectomy through a 5-year landmark analysis. Methods: Between 2010 and 2016, 662 patients with low-risk PTC were included. The postoperative TSH status was determined using the ‘TSH > 2 ratio’, which was calculated using the TSH test results during the 5-year follow-up. The optimal cutoff value of ‘TSH > 2 ratio’ for tumor recurrence was determined using a receiver operating characteristic curve analysis. Recurrence-free survival (RFS) was compared between the groups using Kaplan–Meier and Cox proportional hazard regression analyses. Results: Patients with ‘TSH > 2 ratio’ > 0.1833 (n = 498) had a worse RFS outcome compared to patients with ‘TSH > 2 ratio’ ≤ 0.1833 (n = 164; p < 0.001). ‘TSH > 2 ratio’ > 0.1833 was a significant risk factor for tumor recurrence after the 5-year landmark (hazard ratio: 4.795, 95% confidence interval: 2.102–10.937, p < 0.001). Conclusions: Maintaining TSH levels ≤ 2 mIU/L below a certain percentage among the total TSH tests during the 5-year follow-up period has a negative impact on tumor recurrence. Full article

This paper investigates the optimal maintenance strategies for a self-repairable component under detection at fixed time intervals. The failure process of a component is considered as two parallel and competing degradation processes: an internal degradation process and an external shock degradation process. Unscheduled maintenance from unofficial sources after each shock to a component is regarded as a self-repairable behavior of the component, and its effectiveness is well evaluated. The official maintenances are preventive maintenance (PM) and corrective maintenance (CM), and two thresholds are set based on reliability values to represent the minimum points for performing PM and CM, respectively. The approximately optimal PM and CM thresholds are found by minimizing the overall maintenance cost rate of a component over a specified operating time. Finally, we demonstrate the feasibility of the model through a numerical case study, give a summary, and suggest possible future research directions. Full article

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices. Full article

Natural polyamines, including spermidine (SPD), spermine (SPM) and putrescine (PUT), are evolutionarily conserved endogenous molecules crucially involved in central cellular processes. Their physiological importance may extend to the maintenance of cognitive function during aging. However, limited population-based epidemiological studies have explored the link between dietary polyamines and dementia risk. This study was a prospective analysis of 77,092 UK Biobank participants aged ≥ 60 years without dementia at baseline. We used Cox proportional hazard regression models to explore the associations between dietary polyamines and the risk of dementia, and restricted cubic splines to test the non-linear relationships. During a median follow-up of 12 years, 1087 incidents of all-cause dementia cases occurred, including 450 Alzheimer’s disease (AD) cases and 206 vascular dementia (VD) cases. The fully adjusted hazard ratios (HRs) for the upper fourth quintile of dietary SPD, in comparison with the lowest quintile of intake, were 0.68 (95% confidence interval [95% CI]: 0.66–0.83) for the risk of all-cause dementia, 0.62 (95% CI: 0.45–0.85) for AD and 0.56 (95% CI: 0.36–0.88) for VD, respectively. A 26% reduction in dementia risk [HR: 0.74, (95% CI: 0.61–0.89)] and a 47% reduction in AD [HR: 0.53, (95%CI: 0.39–0.72)] were observed comparing the third with the lowest quintiles of dietary SPM. Dietary PUT was only associated with a reduced risk of all-cause dementia in the fourth quintile [HR (95% CI): 0.82 (0.68–0.99)]. Reduced risk was not found to be significant across all quintiles. There were ‘U’-shaped relationships found between dietary polyamines and all-cause dementia, AD and VD. Stratification by genetic predisposition showed no significant effect modification. Optimal intake of polyamines was linked to a decreased risk of dementia, with no modification by genetic risk. This potentially suggests cognitive benefits of dietary natural polyamines in humans. Full article

As one of the most critical components of the power grid system, transformer maintenance strategy planning significantly influences the safe, economical, and sustainable operation of the power system. Periodic imperfect maintenance strategies have become a research focus in preventive maintenance strategies for large power equipment due to their ease of implementation and better alignment with engineering realities. However, power transformers are characterized by long lifespans, high reliability, and limited defect samples. Existing maintenance methods have not accounted for the dynamic changes in maintenance costs over a transformer’s operational lifetime. Therefore, we propose a maintenance interval optimization method that considers imperfect maintenance and dynamic maintenance costs. Utilizing defect and maintenance cost data from 400 220 KV oil-immersed transformers in northern China, we employed Bayesian estimation for the first time to address the distribution fitting of defect data under small sample conditions. Subsequently, we introduced imperfect maintenance improvement factors to influence the number of defects occurring in each maintenance cycle, resulting in more realistic maintenance cost estimations. Finally, we established an optimization model for transformer maintenance cycles, aiming to minimize maintenance costs throughout the transformer’s entire lifespan while maintaining reliability constraints. Taking a transformer’s strong oil circulation cooling system as an example, our method demonstrates that while meeting the reliability threshold recognized by the power grid company, the system’s maintenance cost can be reduced by 41.443% over the transformer’s entire life cycle. Through parameter analysis of the optimization model, we conclude that as the maintenance cycle increases, the factors dominating maintenance costs shift from corrective maintenance to preventive maintenance. Full article

In the early stages of developing wave energy converter (WEC) projects, a quantitative assessment of component failure consequence costs is essential. The WEC types, deployment site features, and accessibility should all be carefully considered. This study introduces an operation and maintenance failure consequence cost (O&M-FC) model, distinct from conventional O&M models. The model is illustrated with case studies at three energetic Atlantic sites, each of which considers two types of generic floating WECs: a 300 kW point absorber (PA) with a hydraulic power-take-off (PTO) and a 1000 kW oscillating water column (OWC) with an air-wells-turbine PTO. This study compares 39 failure modes for PA and 27 for OWC in terms of direct repair costs and indirect lost production costs, examining the impact of location accessibility, capacity factors, and the mean annual energy production. The discussion revolves around the sensitive parameters. Recommendations for failure mitigations are presented, and the impact of planned maintenance (PM) during the operational phase is examined for 20 MW PA and OWC WEC projects. For a given WEC type, the method thoroughly evaluates how the location affects performance metrics. It offers a decision-making tool for determining optimal PM intervals to meet targets such as O&M costs, operating profit, or availability. Full article

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided. Full article

Background and Objectives: Endocan, secreted from the activated endothelium, is a key player in inflammation, endothelial dysfunction, proliferation of vascular smooth muscle cells, and angiogenesis. We aimed to investigate the link between endocan and aortic stiffness in maintenance hemodialysis (HD) patients. Materials and Methods: After recruiting HD patients from a medical center, their baseline characteristics, blood sample, and anthropometry were assessed and recorded. The serum endocan level was determined using an enzyme immunoassay kit, and carotid–femoral pulse wave velocity (cfPWV) measurement was used to evaluate aortic stiffness. Results: A total of 122 HD patients were enrolled. Aortic stiffness was diagnosed in 53 patients (43.4%), who were found to be older (p = 0.007) and have a higher prevalence of diabetes (p < 0.001) and hypertension (p = 0.030), higher systolic blood pressure (p = 0.011), and higher endocan levels (p < 0.001), when compared with their counterparts. On the multivariate logistic regression model, the development of aortic stiffness in patients on chronic HD was found to be associated with endocan [odds ratio (OR): 1.566, 95% confidence interval (CI): 1.224–2.002, p < 0.001], age (OR: 1.040, 95% CI: 1.001–1.080, p = 0.045), and diabetes (OR: 4.067, 95% CI: 1.532–10.798, p = 0.005), after proper adjustment for confounders (adopting diabetes, hypertension, age, systolic blood pressure, and endocan). The area under the receiver operating characteristic curve was 0.713 (95% CI: 0.620–0.806, p < 0.001) for predicting aortic stiffness by the serum endocan level, at an optimal cutoff value of 2.68 ng/mL (64.15% sensitivity, 69.57% specificity). Upon multivariate linear regression analysis, logarithmically transformed endocan was proven as an independent predictor of cfPWV (β = 0.405, adjusted R² change = 0.152; p < 0.001). Conclusions: The serum endocan level positively correlated with cfPWV and was an independent predictor of aortic stiffness in chronic HD patients. Full article

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities. Full article

Relevant properties of the somatotype as important indicators can be associated with the body composition characteristics as well as both metabolic and bio-mechanical efficiency of athletes in the sport concerned. The primary aim of this single cross-sectional study was to determine the somatotype profiles in association with body composition and nutritional profiles among Lithuanian elite athletes (n = 189) involved in water, cycling and combat sports. The body composition along with the somatotype profiles and the nutritional status of athletes were evaluated using a battery of multiple frequency (5, 50, 250, 550, and 1000 kHz) bioelectrical impedance analysis (BIA) and a 3-day food record analysis. In terms of the prediction for athletes to be classified as endomorphs, mesomorphs or ectomorphs, the linear discriminant analysis was conducted to assess the grouping of samples. Both the multiple linear regression and multivariate logistic regression statistical analyses were performed to explore the associations between the independent and dependent variables. The central tendency values for the somatotype components of endomorphy, mesomorphy and ectomorphy in athletes playing water, cycling and combat sports were 4.3–4.9–3.4, 4.3–4.8–3.4 and 4.5–5.5–2.9, respectively. The central mesomorph somatotype with a trend towards endomorphy was dominant and varied according to a high muscle-to-fat ratio in elite athletes. Significant (p ≤ 0.001) positive associations between both endomorphy and mesomorphy values and higher body fat percentage as well as lower and upper limb muscle mass were identified. The lower levels of trunk muscle mass were related to athletes’ endomorphy and mesomorphy, too. Furthermore, in the athletes’ sample under analysis, high-level mesomorphs were prone to consume low-carbohydrate (adjusted odd ratio (AOR) 0.5, 95% confidence interval (CI) 0.2; 0.9) and high-protein diets (AOR 2.5, 95% CI 1.1; 5.5). Contrastingly, the elite athletes with a higher expression of endomorphy were on high-carbohydrate (AOR 5.4, 95% CI 1.1; 8.3) and high-fat diets (AOR 4.6, 95% CI 1.5; 7.1) along with insufficient protein diet (AOR 0.3, 95% CI 0.1; 0.9). Finally, whilst nutrition goals as a mediator can play a significant role in undergoing the maintenance of balance between the optimal body composition for athletic performance and the development of an ecto-mesomorphic somatotype, the elite athletes with higher levels of endomorphy value should be aware of lowering the body fat percentage coupled with dietary fat reduction and higher protein intakes. The findings obtained from the study may serve as an antecedent for a more targeted management of the elite athletes’ training process. Somatotyping as an additional assessment method can be successfully deployed in choosing correct coaching techniques, contributing to talent recognition processes or identifying reference morphometric parameters in elite athletes competing in water, cycling and combat sports. Full article

Background and Objectives: Peripheral arterial stiffness (PAS), assessed by brachial-ankle pulse wave velocity (baPWV), is an independent biomarker of cardiovascular diseases (CVD) in patients on maintenance hemodialysis (HD). Malondialdehyde-modified low-density lipoprotein (MDA-LDL), an oxidative stress marker, has been linked to atherosclerosis and CVD. However, the association between serum MDA-LDL and PAS among HD patients has not been fully elucidated. This study aimed to examine the association of serum MDA-LDL with PAS in HD patients and to identify the optimal cutoff value of serum MDA-LDL for predicting PAS. Materials and Methods: A cross-sectional study was conducted in 100 HD patients. Serum MDA-LDL was quantified using an enzyme-linked immunosorbent assay (ELISA), and baPWV was measured using a volume plethysmographic device. Patients were divided into the PAS group (baPWV > 18.0 m/s) and the non-PAS group (baPWV ≤ 18.0 m/s). The associations of baPWV and other clinical and biochemical parameters with serum MDA-LDL were assessed by multivariable logistic regression analyses. A receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cutoff value of serum MDA-LDL for predicting PAS. Results: In multivariable logistic regression analysis, higher serum MDA-LDL, older age, and higher serum C-reactive protein [odds ratios (ORs) and 95% confidence intervals: 1.014 (1.004–1.025), 1.044 (1.004–1.085) and 3.697 (1.149–11.893)] were significantly associated with PAS. In the ROC curve analysis, the optimal cutoff value of MDA-LDL for predicting PAS was 80.91 mg/dL, with a sensitivity of 79.25% and a specificity of 59.57%. Conclusions: Greater serum MDA-LDL levels, particularly ≥80.91 mg/dL, were independently associated with PAS in HD patients. The findings suggest that oxidative stress plays a crucial role in the pathogenesis of PAS, and targeting MDA-LDL may be a potential therapeutic strategy for reducing cardiovascular risk in HD patients. Full article

In patients with normal renal function, significant teicoplanin dose adjustments are often necessary. This study aimed to develop a population pharmacokinetic (PK) model for teicoplanin in healthy adults and use it to recommend optimal dosage regimens for patients with normal renal function. PK samples were obtained from 12 subjects and analyzed using a population approach. The derived parameters informed Monte Carlo simulations for dosing recommendations. The PK profile was best described using a three-compartment model, in which the estimated glomerular filtration rate calculated via the CKD-EPI equation and adjusted for body surface area was identified as a significant covariate affecting total clearance. For pathogens with a minimum inhibitory concentration of 1 mg/L, a loading dose (LD) of 14 mg/kg administered every 12 h for four doses, followed by a maintenance dose (MD) of 16 mg/kg administered every 24 h, is recommended. These findings indicate the need for dosage adjustments, such as increasing the LD and MD or decreasing the dosing interval of MD in patients with normal renal function. Because of the long half-life of teicoplanin and the requirement for long-term administration, therapeutic drug monitoring at strategic intervals is important to avoid nephrotoxicity associated with elevated trough concentrations. Full article

In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system’s success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system’s maintenance intervals. Full article