Search Results (482)

The rising prevalence of chronic kidney disease represents a major global health burden. Limitations of current renal replacement therapies, including donor organ shortages, rejection, and dialysis-related complications, underscore the need for innovative treatment options. This narrative review assesses the feasibility of bioengineered kidneys as an alternative to current treatments by discussing advances in decellularization, recellularization, and the transplantation of cell-on-scaffold kidneys. We propose that the development of functional bioengineered kidneys follows a hierarchical, staged process, in which vascular patency is the primary prerequisite for graft survival, followed by partial restoration of glomerular filtration, with complete tubular function remaining the final and most challenging milestone. Perfusion-based whole-organ decellularization has made significant progress in preserving the extracellular matrix, enabling the production of acellular human kidney scaffolds. However, complete recellularization of whole kidneys has not yet been achieved. Nevertheless, partially repopulated kidney scaffolds have been shown to withstand physiological blood pressure, produce urine, and exhibit filtration in large-animal models. Complete endothelial coverage of the vascular network proved essential for preventing thrombosis after transplantation. Current work on bioengineered kidneys shows promising results regarding feasibility for clinical application. It is important to note that most of the included studies are proof-of-concept, characterized by small sample sizes and short observation periods. Although these findings are crucial for further research, they cannot be generalized, and larger trials are recommended. In addition to cell-on-scaffold kidneys, 3D bioprinting is a promising technique that could eliminate the need for donor scaffolds. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a $+27.36\%$ reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to $15.2 \mathrm{s}$, and restricting transient overshoot to just $0.18\%$. Full article

Cytomegalovirus (CMV) is a major determinant of post-transplant morbidity in solid organ transplant recipients, not only through direct viral disease but also through a broad spectrum of indirect effects that may adversely influence graft and patient outcomes. This review summarizes current clinical and mechanistic evidence regarding the mechanisms of CMV-associated indirect injury in transplantation, drawing on human observational studies together with supporting in vitro and animal-model data. CMV establishes lifelong latency with intermittent reactivation and exerts sustained immunomodulatory effects on both innate and adaptive immunity, which may persist even during low-level viral replication. The mechanisms discussed include monocyte reprogramming, altered antigen presentation, T-cell and natural killer cell dysregulation, endothelial activation and dysfunction, chronic inflammatory signaling, impaired antimicrobial defense, and disturbances in metabolic regulation. The review considers how these mechanisms have been proposed to translate into major post-transplant complications, including acute rejection, chronic allograft dysfunction, cardiovascular and thrombotic disease, post-transplant diabetes, and increased susceptibility to secondary bacterial, fungal, and viral infections. It also addresses current preventive strategies, although evidence regarding their effectiveness in reducing indirect clinical outcomes remains limited and largely observational. Much of the supporting evidence is associative, and the contribution of CMV is often difficult to separate from that of the overall immunosuppressive burden and the comorbidities of transplant recipients. With these considerations, the available evidence supports regarding CMV not merely as an opportunistic pathogen, but as a persistent immunobiological driver of long-term transplant injury. Improved understanding of these indirect effects may enhance risk stratification, support biomarker-guided prevention, and inform future strategies aimed at reducing long-term graft dysfunction and patient morbidity after transplantation. Full article

To address the limitations of existing sliding mode-based integrated guidance and control (IGC) schemes, such as chattering, input saturation, and insufficient robustness, this paper proposes a three-dimensional IGC design method incorporating both terminal angle and attitude angle constraints. First, a control-oriented six-degrees-of-freedom model is established based on three-dimensional relative motion and vehicle dynamics, and the control objectives for maneuvering target interception under multiple constraints are clarified. Subsequently, a finite-time terminal sliding mode guidance law based on time-to-go (TGO) is integrated with dynamic surface control to construct the IGC framework. In this design, command filters are introduced to overcome the “explosion of complexity”, while amplitude saturation functions are employed to constrain system states and control inputs. Meanwhile, a generalized super-twisting extended state observer (GSTESO) is incorporated to estimate and compensate for lumped uncertainties in the system. Finally, by combining Lyapunov stability theory with an integral barrier Lyapunov (IBL) function, it is proven that the closed-loop system is uniformly ultimately bounded and satisfies the terminal angle constraints. Comparative simulations under multiple disturbance scenarios demonstrate that the proposed method meets the accuracy requirements in terms of miss distance and LOS angle error. Moreover, it alleviates high-frequency chattering and prevents control-input saturation, showing improved robustness and disturbance rejection capability compared with the baseline methods. Therefore, the proposed approach provides a valuable reference for engineering applications of three-dimensional IGC in maneuvering target interception. Full article

The underground caverns of pumped-storage power stations generally feature large inclination angles. During the bottom-up oblique excavation by hard-rock Tunnel Boring Machines (TBMs), the Anti-Back-Slip (ABS) support system is the core device ensuring safe operations. Specifically, the synchronization of the multiple hydraulic cylinders within the ABS system is a critical factor determining the stability and safety of the TBM. Therefore, this paper designs a hydraulic control system for the ABS device and proposes an adjacent cross-coupling synergistic control strategy based on adaptive backstepping. This strategy innovatively integrates an adaptive backstepping control law into the adjacent cross-coupling topology to achieve high-precision multi-cylinder control. Utilizing the AMESim-Simulink platform, high-fidelity co-simulations are conducted under both uniform and eccentric load conditions. The results demonstrate that under nominal conditions, the proposed algorithm exhibits asymptotic convergence at the mathematical level. The system maintains robust stability under dynamic excitations. When subjected to sudden asymmetric eccentric loads of 1.0–2.0 times, the system prevents tracking divergence and limits the maximum multi-cylinder synchronization error to within 1.82 mm. This research satisfies the requirements for synchronous control and provides a theoretical and engineering reference for the disturbance-rejection synergy of inclined shaft TBM support systems. Full article

This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level or segment-level labels for real-time filtering and offline dataset curation. The framework is quantitatively validated exclusively on ECG data. Its modular architecture is designed to extend to further non-stationary periodic biomedical time-series signals including photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) through modality-specific parameter adaptation; however, this broader applicability currently reflects architectural extensibility rather than experimentally validated performance. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency domain SNR estimation, and baseline wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels from 20 healthy participants demonstrates high methodological classification accuracy (98.1%), achieving approximately a 98% F1-score, 99% sensitivity, and 97% specificity. Prospective validation on patient populations with cardiovascular pathology is identified as a necessary step toward clinical deployment. This modular approach improves the reliability of downstream analysis by preventing corrupted data from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems. Full article

Background: Cyclosporine is widely used to prevent transplant rejection; however, its systemic administration is associated with low bioavailability and a risk of severe adverse side effects. In the context of lung transplantation, local pulmonary delivery represents a promising strategy to reduce the required dose while enhancing local anti-inflammatory efficacy and limiting systemic toxicity. Methods: In this study, cyclosporine was encapsulated in liposomes coated with calcium phosphate to improve cellular uptake. The liposomal formulation was subsequently converted into a dry powder for inhalation to enable pulmonary administration, combining cyclosporine-loaded liposomes with a calcium phosphate coating, extending prior work on inhaled liposomal cyclosporine and mineral-coated liposomes into a single platform. The cyclosporine loading was optimised to achieve an efficient drug content in the final formulation. Results: The presence of the calcium phosphate coating on the liposomal surface was confirmed by the shift in zeta potential and by cryo-transmission electron microscopy. The resulting dry powder exhibited suitable aerodynamic properties for pulmonary delivery with a fine particle fraction of 33.6 ± 1.6%. In vitro biocompatibility studies performed on A549 epithelial cells and THP-1 monocytic cells demonstrated that the formulation did not affect cell viability. Furthermore, the formulation containing calcium phosphate-coated liposomes showed a stronger anti-inflammatory effect compared with both uncoated liposomal formulations and the corresponding raw material, consisting of a physical mixture of phospholipids and cyclosporine. Conclusions: Overall, despite limitations on respirability and efficacy that will require further in vivo studies, this calcium phosphate-coated liposomal dry powder could represent a promising strategy for targeted pulmonary delivery of cyclosporine, with potential to improve the prevention of lung transplant rejection while minimising systemic side effects. Full article

Background: Immunosuppressive drugs (ISDs) are essential for preventing organ rejection but have been reported to increase cancer risk with prolonged use. This study compares cancer risk between ISDs used for long-term maintenance after transplantation (T-ISDs) and those prescribed for non-transplant chronic conditions including cell-mediated (C-ISDs) and receptor-mediated (R-ISDs) ISDs. We hypothesized that cancer risk would differ between T-ISDs and both C-ISD and R-ISD groups. Methods: Using the TriNetX database, solid organ transplant recipients treated with tacrolimus (TAC), cyclosporine (CY), rapamycin (RAPA), or mycophenolate (MMF) were compared to propensity-matched R-ISDs (adalimumab, infliximab, etc.) or C-ISDs (methotrexate, azathioprine, etc.) for at least 24 encounters to determine risk of malignancy. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to assess the three-year cancer risk. Results: After matching, T-ISDs were associated with higher malignancy risk compared to both R-ISDs (n = 29,748; HR 2.616, 95% CI 2.427–2.820) and C-ISDs (n = 31,704; HR 1.271, 95% CI 1.195–1.351). Each individual immunosuppressant in the T-ISD cohort was associated with increased cancer risk compared to R-ISDs, while only TAC and CY showed higher risk than C-ISDs (TAC: n = 9846, HR 1.354, 95% CI 1.228–1.492; CY: n = 1801, HR 1.234, 95% CI 1.007–1.512). Organ-specific analyses showed consistent patterns across systems. Conclusions: Overall, T-ISDs are associated with increased malignancy risk compared to R-ISDs and modestly compared to C-ISDs. TAC and CY confer the greatest risk, while MMF demonstrates relatively lower relative risk. These findings underscore the need to individualize ISD regimens to minimize long-term cancer risk. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

Background: Tacrolimus is currently the most commonly used immunosuppressive agent after liver transplantation, administered to prevent allograft rejection, but it is also associated with numerous adverse effects. Purpose: The aim of this review was to summarize the most clinically relevant and well-documented adverse effects of tacrolimus after liver transplantation, focusing on their mechanisms, clinical presentation, approximate incidence, and overall clinical significance. Methods: We searched the PubMed and Medline databases and included articles, randomized controlled trials, cohort studies, review articles, and case reports published between 2016 and 2026. Results: The most common causes of morbidity in the early days following liver transplantation and initiation of tacrolimus therapy are acute nephrotoxicity, systemic infections, and signs of neurotoxicity. In the majority of patients, these manifestations resolved after dose reduction or complete discontinuation of the drug. During the early post-transplant period, opportunistic infections were the main cause of morbidity, with bacteria being the most frequent pathogens, while mortality in certain fungal infections reached up to 50%. While acute toxicities are primarily influenced by drug dose, long-term use of tacrolimus, regardless of dose, can lead to the development of malignancies, metabolic disorders, cardiovascular diseases, and chronic kidney disease, with incidence estimates varying widely. Conclusions: Consistent monitoring of patients on tacrolimus is crucial to ensure maximum therapeutic benefit and to reduce the risk of associated adverse effects. Full article

In response to issues such as the strong output power fluctuations of photovoltaic units caused by external environmental factors like solar irradiance, and the susceptibility of traditional control methods to grid disconnection incidents under significant system disturbances, a control strategy based on cascaded linear-nonlinear Extended State Observer (ESO) Active Disturbance Rejection Control (ADRC) is proposed for photovoltaic-storage hybrid systems. A model of the photovoltaic-storage hybrid system is constructed to achieve high- and low-frequency power distribution among energy storage units. The cascaded linear-nonlinear ESO-based ADRC is employed to estimate and compensate for system disturbances in real-time. Simulation results verify the effectiveness of the proposed control strategy, demonstrating a reduction in frequency deviation by 0.08 Hz under normal frequency dips. More critically, during severe AC bus voltage sags, the proposed strategy prevents the system frequency from falling below 49.0 Hz, thereby avoiding the under-frequency load shedding (UFLS) that occurs with conventional PI control. Additionally, the DC bus voltage fluctuation is limited to within 8 V under grid disturbances, and unlike the severe oscillations exhibited by PI control during system recovery, the proposed cascade strategy ensures an immediate and smooth transient response. Full article

Self-incompatibility (SI) is an important plant mechanism that prevents inbreeding depression by recognizing and rejecting self-pollen, thereby promoting outcrossing. However, SI can also act as a barrier in breeding programs, presenting significant challenges to breeders. Passion fruit (Passiflora edulis), a tropical fruit species of substantial economic importance, also serves as a valuable system for investigating SI mechanisms within the Passifloraceae. Nevertheless, the molecular basis of SI in passion fruit has not yet been elucidated. In this study, we investigated the SI system in yellow passion fruit (P. edulis f. flavicarpa) and employed transcriptomic analysis to examine the time-course transcriptional responses following different pollination treatments. Transcriptomic analysis revealed distinct gene expression dynamics under different pollination treatments: self-pollinated samples exhibited stronger and earlier transcriptional changes, whereas the number of differentially expressed genes (DEGs) in cross-pollinated samples was relatively lower. Numerous pathways previously associated with sporophytic self-incompatibility (SSI) were enriched in the stigma samples after self-pollination. Reactive oxygen species (ROS) are crucial signaling molecules involved in pollen germination and pollen tube growth during SI responses. Our results showed that ROS-related pathways were enriched in stigma tissues after self-pollination. In addition, oxidative stress-related responses were detected in the style shortly after self-pollination, suggesting that plastid-associated or general oxidative stress processes may also be involved, although the precise source of ROS requires further validation. FERONIA, ROP9, and ARC1 are key genes related to the SI system in Brassica. In the passion fruit SI response, the expression levels of these genes increased in the style, indicating a spatial expression pattern different from that reported in classical Brassicaceae SSI systems. Together with cytological observations showing that self-pollen rejection occurs at the stigma surface, our results suggest that yellow passion fruit may employ an SSI-like regulatory framework while exhibiting a lineage-specific spatial deployment of SI-related regulators. Overall, this study provides new transcriptomic insights into the SI mechanism of yellow passion fruit, establishes a molecular framework for understanding SI in P. edulis f. flavicarpa, and offers novel insights into the diversity of plant SI systems. Full article

This study examined a moderated mediation model linking parental rejection and overprotection to smartphone addiction, with sense of security as a mediator and forgiveness as a moderator. A total of 730 students (mean age = 12.15 ± 1.13 years; 50.7% female) were recruited from two primary and two secondary schools in Hunan, China, using cluster sampling by class, and all participants completed a set of self-report questionnaires. Results showed that, after controlling for gender and age, both parental rejection and overprotection were positively associated with smartphone addiction and negatively associated with sense of security and forgiveness. Sense of security partially mediated the links between negative parenting and smartphone addiction. Interpersonal forgiveness moderates the direct associations between parental rejection, overprotection and adolescent smartphone addiction, and self-forgiveness moderates the relationships between sense of security and smartphone addiction. The present study clarifies the associations between negative parenting behaviors (i.e., parental rejection and overprotection) and problematic smartphone use in early and middle adolescence, highlights the vital protective roles of security and forgiveness, and provides empirical evidence to inform the prevention and intervention strategies for adolescent smartphone addiction. Full article

Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination. Full article

The deployment of Motor Imagery Brain–Computer Interfaces (MI-BCI) is constrained by the inherent physiological variabilities of Electroencephalography (EEG) and parametric opacity. This paper presents a targeted technical audit of ten high-density MI-BCI computational pipelines, evaluating how existing literature addresses low Signal-to-Noise Ratio (SNR), intra-subject variability, and session-to-session instability. The investigation focuses on the contamination of data by ocular and muscular artifacts that overlap with the spectral components of Mu and Beta rhythms, often leading to algorithmic overfitting. Furthermore, the paper evaluates the impact of manifold drift where fluctuations in user state necessitate frequent recalibration as a primary hurdle for BCI portability. By applying a forensic evaluation framework to standardize the analysis across the ten selected studies, this paper identifies a high-performance landscape within standardized benchmarks, with classification accuracies reaching peak values of 95.42%. The audit specifically identifies a performance-reporting gap; while hybrid architectures demonstrate superior noise-rejection, they are frequently characterized by undocumented computational overhead. Additionally, while Neighborhood Component Analysis (NCA) emerges as a stable feature selection algorithm across the sampled literature, the systemic absence of reported execution times prevents a verified assessment of its low-latency viability. A critical technical finding is the widespread issue of Parametric Opacity, particularly regarding the omission of essential deterministic variables such as filter orders, windowing constants, and the final dimensionality of feature vectors. The audit reveals that the frequent failure to report the exact number of features utilized for classification masks potential overfitting and prevents an accurate assessment of the system’s generalization capabilities. Furthermore, only a specialized subset of the reviewed literature validates performance through formal statistical testing, such as Friedman ANOVA or Wilcoxon Signed-Rank tests, with most studies relying on peak accuracy metrics that may disguise filtered artifact residuals. This lack of granular documentation disguises the computational complexity of proposed methods and complicates their feasibility for hardware-in-the-loop validation. The findings establish that standardizing the reporting of preprocessing variables and feature-space dimensions is a prerequisite for overcoming current performance plateaus in universal BCI architectures. Full article