Search Results (2,901)

Background: Total hip arthroplasty (THA) is a well-established surgical procedure for end-stage hip arthrosis. Innovations such as minimally invasive approaches and new technologies have improved outcomes and reduced invasiveness. The introduction of short-stem prostheses, which offer potential benefits in bone preservation, has been a significant development in recent years. This prospective case series study aims to compare invasiveness of the short-stem (SS) and conventional-stem (CS) prostheses in THA with a posterolateral approach (PLA) by assessing perioperative serum markers. Methods: A prospective case series was conducted involving consecutive patients who underwent primary THA from January 2022 to December 2023. Demographics and preoperative, postoperative day 1 (POD1), and postoperative day 2 (POD2) serum levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), and white blood cells (WBCs) were measured. Results: The study included 21 patients with CS and 19 with SS, with no significant differences between groups in demographic. No statistically significant differences were found in serum markers between SS and CS groups at any time point. Both groups showed significant increases in ESR, CRP, and PCT from preoperative levels to POD2 (p < 0.001), while WBC values increased from preoperative to POD1 but decreased between POD1 and POD2. Conclusion: The short-stem prosthesis does not exhibit significantly different perioperative serum marker profiles compared to the conventional stem, suggesting similar levels of surgical invasiveness between the two implants. Further studies with larger sample sizes are needed to validate these findings and explore other aspects of short-stem THA. Full article

Air traffic controllers (ATCOs) make a significant contribution to ensuring flight safety, making this profession a highly stressful job globally. Job demands–resources (JDR) theory proposes a health impairment process stemming from job demand (complexity) to mental workload, which causes job stress, resulting in compromised flight safety. This vicious cycle is evident among ATCOs and is recognized as an unsustainable management practice. To curb this process, we propose intra-functional flexibility as a conditional factor. Intra-functional flexibility refers to the flexibility in the reallocation and coordination of resources among team members to help in urgent times. This is a relatively new concept and is yet to be empirically tested in the ATCO context. ATCOs work in a dynamic environment filled with sudden surges of urgent jobs to be handled within short time limits. Intra-functional flexibility allows standby crews to be called to ease these tensions when needed. To ascertain the role of intra-functional flexibility in mitigating health impairment among ATCOs, a questionnaire was administered to 324 ATCOs distributed across Saudi Arabia. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis exhibited two critical findings: First, the study revealed the prevalence of a vicious cycle of health impairment among Saudi ATCOs, whereby job complexity leads to increased mental workload, resulting in elevated levels of job stress. Secondly, the presence of intra-functional flexibility weakened this vicious cycle by mitigating the influence exerted by mental workload on job stress. That is, the mediation-moderated model proposed in this study provides empirical evidence supporting the applicability of intra-functional flexibility in mitigating the dire suffering of ATCOs. This study discusses limitations and future research directions in the end. Full article

Background/Objectives: Total knee arthroplasty (TKA) is an effective surgical intervention for end stage knee osteoarthritis in elderly patients, with emerging robotically assisted techniques aiming to enhance surgical precision and patient outcomes. This study aimed to compare medium-term balance and functional outcomes between robotically assisted and conventional manual TKA in community-dwelling elderly patients. Methods: This cross-sectional study included 50 elderly patients undergoing TKA, who were divided into robotically assisted (n = 25) and conventional manual (n = 25) groups. Demographic and clinical data, balance performance, and functional outcomes were compared at nearly 1.5 years postoperatively. Outcome measures included balance performance assessed by the Berg Balance Scale (BBS), pain via the Visual Analog Scale (VAS), knee function as measured by the Lysholm Knee Scoring Scale, quality of life using the Short Form-12 (SF-12), joint awareness as evaluated by the Forgotten Joint Score-12 (FJS-12), and surgical satisfaction. Results: The groups had similar demographic and clinical data regarding age, gender, follow-up duration, surgical time, and anesthesia type (p > 0.05). The robotically assisted group demonstrated better balance performance on the BBS (p = 0.043) and had a statistically shorter length of hospital stay (1.22 vs. 1.42 days; p = 0.005). However, no statistically significant differences were observed in VAS activity pain (p = 0.053), Lysholm Knee Scoring Scale (p = 0.117), SF-12 physical and mental scores (p = 0.174 and p = 0.879), FJS-12 (p = 0.760), and surgical satisfaction (p = 0.218). Conclusions: Robotically assisted TKA is associated with advantageous postoperative recovery, particularly in terms of balance performance, showing no clinical difference in other functional outcomes compared to the conventional manual technique. From a physical therapy perspective, these findings emphasize the importance of developing tailored and effective rehabilitation strategies in the medium term for functional recovery in the elderly population. Full article

The hypothalamus belongs to the central brain structure designed for the neuroendocrine regulation of many organismal functions, including the stress response, cardiovascular system, and blood pressure, and it is well known that the serotonergic hypothalamic system plays a significant role in these processes. Unfortunately, the genetic determination of serotonergic hypothalamic mechanisms has been little studied. The aim of this article is to describe the expression profile of the genes in the hypothalamic serotonergic synapses in hypertensive ISIAH rats in comparison with normotensive WAG rats in control conditions and under the influence of a single short-term restraint stress. It was found that 14 differentially expressed genes (DEGs) may provide the inter-strain differences in the serotonergic synaptic function in the hypothalamus between the hyper- and normotensive rats studied. In hypertensive rats, downregulation of Slc18a1 gene in the presynaptic serotoninergic ends and decreased expression of Cacna1s and Htr3a genes determining the postsynaptic membrane conductance may be considered as a main factors causing differences in the function of hypothalamic serotoninergic synapses in hypertensive ISIAH and normotensive WAG rats at the basal conditions. Under basal conditions, glial cell genes were not involved in the formation of inter-strain differences in serotonergic synaptic function. The analysis of transcriptional responses to restraint stress revealed key genes whose expression is involved in the regulation of serotonergic signaling, and a cascade of interrelated changes in biological processes and metabolic pathways. Stress-dependent changes in the expression of some DEGs are similar in the hypothalamus of hypertensive and normotensive rats, but the expression of a number of genes changes in a strain-specific manner. The results suggest that in hypothalamic glial cells of both strains, restraint stress induces changes in the expression of DEGs associated with the synthesis of Ip3 and its receptors. Many of the identified serotonergic DEGs participate in the regulation of not only serotonergic synapses but may also be involved in the regulation of cholinergic, GABAergic, glutamatergic, and dopaminergic synapses. The results of the study provide new information on the genetic mechanisms of inter-strain differences in the functioning of the hypothalamic serotonergic system in hypertensive ISIAH and normotensive WAG rats at rest and under the influence of a single short-term restraint (emotional) stress. Full article

This comment focuses on the contribution of experimental brain ischemia to the overwhelming incidence of Alzheimer’s disease in women as presented by Lohkamp et al. in Life 2025, 15, 333. The authors showed that in Alzheimer’s disease and ischemic stroke there are sex-dependent adaptations in the form of cross-links and vice versa. It was emphasized that the high longevity of women in itself does not explain the mechanisms underlying the biological differences between the sexes causing a female predominance in the development of Alzheimer’s disease. Differences were demonstrated between males and females: female APP/PS1 mice had greater amyloid deposition, hyperactivity, lower body weight, and reduced cerebral blood flow, as well as less neuroinflammation, which the authors suggest may have potential neuroprotection. It should be noted that some of the information presented in the article by Lohkamp et al. raises more questions than answers. Therefore, future studies should consider, for example, studies using single-cell technologies that can provide insight into the timing and sequence of cellular dysfunctions across sexes and analyze the continuity of changes over time, starting from short-term observations of a few days and ending with long-term observations of a year or more, to assess the continuity and differentiation of changes. Full article

Accurate prediction of lithium-ion battery capacity degradation is crucial for reliable state-of-health estimation and long-term performance assessment in battery management systems. This study presents an empirical modeling approach based on experimental data collected from four lithium iron phosphate (LFP) battery packs cycled over 75 to 100 charge–discharge cycles. Several mathematical models—including linear, quadratic, single-exponential, and double-exponential functions—were evaluated for their predictive accuracy. Among these, the linear and single-exponential models demonstrated strong performance in early-cycle predictions. It was found that using 30 to 40 cycles of data is sufficient for reliable forecasting within a 100-cycle range, reducing the mean absolute error by over 80% compared to using early-cycle data alone. Although these models provide reasonable short-term predictions, they fail to capture the nonlinear degradation behavior observed beyond 80 cycles. To address this, a modified linear model was proposed by introducing an exponentially decaying slope. The modified linear model offers improved long-term prediction accuracy and robustness, particularly when data availability is limited. Capacity forecasts based on only 40 cycles yielded results comparable to those using 100 cycles, demonstrating the model’s efficiency. End-of-life estimates based on the modified linear model align more closely with typical LFP specifications, whereas conventional models tend to underestimate the cycle life. The proposed model offers a practical balance between computational simplicity and predictive accuracy, making it well suited for battery health diagnostics. Full article

Background/Objective: Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical assessments rely heavily on manual review of medical imaging, a process that is time-consuming and prone to inter-observer variability. The widely accepted standard of care primarily focuses on measuring aneurysm diameter at its widest point, providing a limited perspective on aneurysm morphology and lacking efficient methods to measure aneurysm volumes. Yet, volume measurement can offer deeper insight into aneurysm progression and severity. In this study, we propose an automated approach that leverages the strengths of pre-trained neural networks and expert systems to delineate aneurysm boundaries and compute volumes on an unannotated dataset from 60 patients. The dataset includes slice-level start/end annotations for aneurysm but no pixel-wise aorta segmentations. Method: Our method utilizes a pre-trained UNet to automatically locate the aorta, employs SAM2 to track the aorta through vascular irregularities such as aneurysms down to the iliac bifurcation, and finally uses a Long Short-Term Memory (LSTM) network or expert system to identify the beginning and end points of the aneurysm within the aorta. Results: Despite no manual aorta segmentation, our approach achieves promising accuracy, predicting the aneurysm start point with an R² score of 71%, the end point with an R² score of 76%, and the volume with an R² score of 92%. Conclusions: This technique has the potential to facilitate large-scale aneurysm analysis and improve clinical decision-making by reducing dependence on annotated datasets. Full article

Background: Pediatric Crohn’s Disease (CD) affects more than physical health, influencing emotional well-being, social integration, and developmental milestones, with an impact on disease management. This study aimed to explore adolescents’ lived experiences with CD and identify factors influencing their motivation for self-management. Methods: A descriptive, cross-sectional qualitative study was conducted using a semi-structured, self-administered online questionnaire. Participants (n = 10) were adolescents with CD who had been diagnosed for over three years and were recruited from a tertiary pediatric gastroenterology center. Data included demographics, clinical characteristics, IMPACT-III (HRQOL), and PROMIS short forms. Open-ended responses underwent thematic analysis using the framework developed by Braun and Clarke. Results: Participants (80% female, median age 16.2 years, median disease duration 4.6 years) were all in clinical remission (median PCDAI = 2) and with good quality of life (median IMPACT-III = 80.7). Six themes emerged: (1) disease knowledge, (2) emotional responses, (3) coping and adaptation, (4) social support, (5) daily life and school impact, and (6) transition to adult care. Most participants demonstrated strong disease literacy and reported effective coping strategies. Emotional responses to diagnosis ranged from relief (60%) to distress (40%); relapses commonly triggered anxiety and fear. Therapeutic changes and disease monitoring were perceived as beneficial (100%) but with concern. Diagnostic procedures were viewed as burdensome by 70% of respondents. School performance and extracurricular participation were negatively affected in 40% during flares. Concerns regarding the future were reported by 40% of participants, with 30% believing that CD might limit life aspirations. While 60% managed their disease independently, 30% relied on parental support. All acknowledged the need for transition to adult care, though readiness varied. Conclusions: This study illustrates the overall impact of disease on pediatric CD patients. It reports significant emotional challenges and difficulties, as well as an impact on daily life, despite good disease knowledge. The findings underscore the importance of psychosocial well-being, ongoing mental health assessment, non-invasive monitoring, and holistic care, emphasizing the patient perspective, in managing pediatric CD. Full article

Introduction: Liver transplantation remains the definitive treatment for end-stage liver disease but faces critical challenges including organ shortages and preservation difficulties, particularly with extended criteria donor (ECD) grafts. Hypothermic machine perfusion (HMP) represents a promising alternative to traditional static cold storage (SCS). Methods: This retrospective study analyzed outcomes from 62 liver transplant recipients between 2016 and 2025, comparing 8 grafts preserved by HMP using the Liver Assist^® system and 54 grafts preserved by SCS. Parameters assessed included postoperative complications, hemodynamic stability, ischemia times, and survival outcomes. Results: HMP significantly reduced surgical (0% vs. 75.9%, p = 0.01) and biliary complications (0% vs. 34.4%, p = 0.004), improved hemodynamic stability post-reperfusion (∆MAP%: 1 vs. 21, p = 0.006), and achieved superior one-year survival rates (100% vs. 84.4%). Despite longer ischemia periods, grafts treated with HMP exhibited fewer adverse effects from ischemia-reperfusion injury. Discussion: These findings highlight the substantial benefits of HMP, particularly in improving graft quality from marginal donors and reducing postoperative morbidity. Further adoption of this technology could significantly impact liver transplantation outcomes by expanding the viable donor pool. Conclusions: The study underscores the effectiveness of hypothermic machine perfusion (HMP) as a superior preservation method compared to traditional static cold storage (SCS), HMP appears to be associated with improved short-term outcomes in liver transplantation. By substantially reducing postoperative complications and enhancing graft viability, HMP emerges as a pivotal strategy for maximizing the use of marginal donor organs. Further research and broader clinical implementation are recommended to validate these promising results and to fully harness the potential of HMP in liver transplantation. Full article

This paper focuses on the triple-junction gallium arsenide solar array of a MEO (Medium Earth Orbit) satellite that has been in orbit for 7 years. Through a combination of theoretical and data-driven methods, it conducts research on anti-radiation design verification and life prediction. This study integrates the Long Short-Term Memory (LSTM) algorithm into the full life cycle management of MEO satellite solar arrays, providing a solution that combines theory and engineering for the design of high-reliability energy systems. Based on semiconductor physics theory, this paper establishes an output current calculation model. By combining radiation attenuation factors obtained from ground experiments, it derives the theoretical current values for the initial orbit insertion and the end of life. Aiming at the limitations of traditional physical models in addressing solar performance degradation under complex radiation environments, this paper introduces an LSTM algorithm to deeply mine the high-density current telemetry data (approximately 30 min per point) accumulated over 7 years in orbit. By comparing the prediction accuracy of LSTM with traditional models such as Recurrent Neural Network (RNN) and Feedforward Neural Network (FNN), the significant advantage of LSTM in capturing the long-term attenuation trend of solar arrays is verified. This study integrates deep learning technology into the full life cycle management of solar arrays, constructs a closed-loop verification system of “theoretical modeling–data-driven intelligent prediction”, and provides a solution for the long-life and high-reliability operation of the energy system of MEO orbit satellites. Full article

Background: The Kidney Disease and Quality of Life Short Form (KDQOL-SF™ 1.3) is widely used to assess health-related quality of life (HRQoL) in patients with end-stage renal disease. However, no prior validation had been conducted in Kazakhstan, where both Kazakh and Russian are commonly spoken. This study aimed to validate the Kazakh and Russian versions of the KDQOL-SF™ 1.3 and to identify predictors of HRQoL among hemodialysis patients in Kazakhstan. Methods: A cross-sectional survey was conducted among 217 adult hemodialysis patients from February to April 2025 using a mixed-methods approach (in-person interviews and online data collection). Psychometric testing included Cronbach’s alpha, floor and ceiling effect analysis, and Pearson correlations with self-rated overall health. Multiple linear regression was used to identify predictors of the Kidney Disease Component Summary (KDCS), Physical Component Summary (PCS), and Mental Component Summary (MCS) scores. Results: Both language versions demonstrated acceptable to excellent internal consistency (Cronbach’s α = 0.692–0.939). Most subscales were significantly correlated with self-rated health, supporting construct validity. Regression analyses revealed that greater satisfaction with care, better economic well-being, and more positive dialysis experiences were significant predictors of higher KDCS and MCS scores. Lower PCS scores were associated with female gender, comorbidities, and financial burden. Importantly, financial hardship and access challenges emerged as strong negative influences on HRQoL, underscoring the role of socioeconomic and care-related factors in patient well-being. Conclusions: The KDQOL-SF™ 1.3 is a valid and reliable tool for assessing quality of life among Kazakh- and Russian-speaking hemodialysis patients in Kazakhstan. Integrating this instrument into routine clinical practice may facilitate more personalized, patient-centered care and help monitor outcomes beyond traditional clinical indicators. Addressing economic and access-related barriers has the potential to significantly improve both physical and mental health outcomes in this vulnerable population. Full article

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements–compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system’s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s. Full article

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R² and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions. Full article

Climate change and global warming are causing growing environmental concerns, prompting many countries to increase investments in renewable energies. The high growth rate of renewables in the energy systems brings significant intermittency challenges. Demand-side flexibility is presented as a viable solution to address this phenomenon. In this framework, this research study proposes a novel methodology to evaluate the flexibility potential that residential consumers can offer to the Distribution System Operator (DSO). Moreover, it pretends to provide guidelines and design of standardized parameters to disaggregate the aggregated energy consumption data of end-users. This step is essential to identify and characterize the primary energy consumption processes in the residential sector, laying the groundwork for future flexibility evaluation. Furthermore, the modeling of the energy consumption curves will enhance residential sector demand-side flexibility enabling end-users to modify their usual consumption patterns. The implemented methodology has been applied to real consumer data provided by the DSO of a Spanish municipality of about 29,000 habitants in the Alicante Province (Spain). Results achieved allowed the validation of the proposed methodology enabling the disaggregation of residential energy profiles and facilitating the subsequent dynamic assessment of residential end-user’s demand flexibility. Moreover, this work will provide valuable guidelines to carry out short-term energy resource planning and solve operational problems of the energy systems. Full article

In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article