Search Results (107)

Purpose: Autopsy remains an important diagnostic and research modality in pediatric oncology. This study examined demographic and clinical factors associated with parental acceptance or decline of autopsy in childhood cancer. Patients and Methods: This study was a retrospective chart review of autopsy consent acceptance or decline patterns between 2007 and 2017 for inpatient pediatric oncology deaths in a large single-site oncology hospital. Demographic factors (age, race, gender), diagnostic factors (primary cancer, transplant history, and neurologic status 24 h prior to death), interventions (intensive care unit location, dialysis, ventilator, chemotherapy, medically administered nutrition), and code status in the 24 h prior to death were obtained. Analysis included descriptive and statistical correlations. Results: Among 344 inpatient decedents, 34% of families consented to autopsy. There was a difference in consent rate according to race (p = 0.015). Diagnosis, transplant status, age, and neurologic status showed no association. Use of dialysis (p < 0.001), ventilation (p < 0.001), and intensive care unit (ICU) location (p < 0.001) correlated with higher consent rates. Chemotherapy and assisted nutrition were not associated with decisions. Presence of a Do Not Resuscitate (DNR) order predicted lower consent (p < 0.001), while receipt of cardiopulmonary resuscitation (CPR) at death predicted higher consent (p < 0.001). Conclusion: One-third of families of inpatient pediatric oncology decedents with cancer agreed to autopsy. Demographic and diagnostic factors were not universally strong predictors, underscoring the personal nature of autopsy decisions. Further research should include multisite prospective designs and direct engagement with bereaved families. Full article

Distribution networks are being transformed by the growing penetration of distributed generation (DG), which changes power flows, voltage profiles, and the optimal operating point of the feeder. This study proposes a hybrid technique that combines the Gray Wolf Optimizer (GWO) with a neural network (NN) surrogate model to solve the distribution network reconfiguration (DNR) problem. The method minimizes active power losses while improving voltage regulation and preserving radial operation under operational constraints. The GWO performs global exploration of discrete switch configurations, whereas the NN accelerates local refinement by screening candidates before exact AC power flow validation. This manuscript presents benchmark results for the IEEE 33-bus and IEEE 69-bus distribution test systems. For the IEEE 33-bus benchmark, DG units are installed at buses 14, 25, and 30. For the IEEE 33-bus case, losses are reduced from 282.94 kW in the base case to 120.65 kW with DG and to 87.08 kW after hybrid reconfiguration, while the minimum voltage magnitude improves from 0.8829 p.u. to 0.9587 p.u. For the IEEE 69-bus case, total active losses decrease from 224.95 kW to 82.22 kW with DG and to 29.92 kW after reconfiguration while concurrently improving the voltage profile and line loading. From a sustainability perspective, the main contribution of the proposed workflow is to reduce technical losses at the distribution level, thereby improving energy efficiency for a given demand. Overall, the results show that the combined use of DG and surrogate-assisted reconfiguration can yield substantial efficiency gains across benchmark feeders of varying sizes, while broader multi-feeder validation and more detailed surrogate error quantification remain necessary before claiming general applicability. Full article

Background/Objectives: Multidrug resistance (MDR) remains a major pathophysiological barrier to effective chemotherapy based on anthracyclines, including daunorubicin (DNR), in the treatment of leukemia. However, conventional population-level measurements of drug uptake do not resolve variability in uptake kinetics among individual leukemia cells, which may influence intracellular drug accumulation and therapeutic response. Methods: In this study, real-time DNR uptake was quantified at the single-cell level using a microfluidic biochip that enabled long-term cellular retention and continuous monitoring. Both wild-type drug-sensitive leukemia cells and a multidrug-resistant mutant overexpressing the P-glycoprotein (P-gp) efflux pump were examined. Results: Kinetic analysis revealed that DNR uptake in drug-sensitive cells was well described by a single dominant uptake process, whereas uptake in MDR cells required a model incorporating two kinetically distinct processes. In both cell populations, pronounced cell-to-cell variation was observed in uptake rates and intracellular drug retention, indicating substantial functional heterogeneity within phenotypically similar cells. This variability persisted following the treatment with an MDR inhibitor and obscured the differences between inhibitor-treated and untreated cells when the uptake was compared across different single cells. To overcome this limitation, a same-single-cell analysis (SASCA) approach was employed, enabling direct comparison of DNR uptake in the same individual cell before and after inhibitor exposure, thereby revealing enhanced intracellular DNR retention and accelerated uptake kinetics following inhibition. Conclusions: Together, these results demonstrate that real-time single-cell kinetic analysis reveals functionally relevant heterogeneity in multidrug-resistant leukemia cells and provides insight into the pathophysiology of MDR that cannot be obtained from population-averaged measurements. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), and dynamic loads introduces significant operational challenges in modern distribution networks, including voltage violations, reverse power flows, and congestion. Distribution network reconfiguration (DNR) is widely used to improve network performance; however, most existing approaches focus primarily on radial topology optimization and rarely consider practical switching feasibility or adaptive transitions between radial and meshed configurations. This paper proposes an operational framework for congestion management based on adaptive radial–mesh reconfiguration. The framework integrates radial network optimization, temporary mesh reinforcement for congestion mitigation, and safe switching sequence validation to ensure operational feasibility. A comprehensive operational cost model incorporating power losses, time-of-use energy imports, switching operations, and on-load tap-changer actions is also developed. The proposed method is validated on a real 22 kV distribution feeder operated by the Provincial Electricity Authority in Thailand. The results demonstrate that the framework effectively mitigates congestion and reduces operational costs by 1.57–9.18% relative to baseline operation, highlighting its practical applicability in active distribution networks. Full article

Introduction: Cardiopulmonary resuscitation (CPR) is one of the most consequential decisions in clinical medicine—a pivotal moment between life and death where science, ethics, and humanity intersect. Although advances in systems of care, technology, and training have refined technique and logistics, outcomes do not consistently result in meaningful, neurologically intact survival. In oncology—where disease trajectories are heterogeneous, treatment burdens substantial, and organ reserve often limited—these tensions are especially pronounced. Methods and approaches: This manuscript examines resuscitation as a medical, ethical, and human process, with explicit focus on patients with cancer. We review contemporary strategies for early recognition of deterioration (MEWS, NEWS, MET activation), team preparedness through Immediate Life Support (ILS), and structured decision-making at the boundaries of resuscitation. We also address communication with patients and families, the legal framework of Do-Not-Resuscitate (DNR) orders, and the distinctions among treatment forgoing, palliative sedation, and euthanasia, emphasising oncology-specific considerations such as metastatic burden, treatment intent (curative vs. palliative), performance status, and organ reserve. Results and discussion: The overall effectiveness of resuscitation remains modest (approximately 5–20% survival), highlighting the importance of prevention and early intervention. In cancer care, the limits of resuscitation are both clinical and ethical, requiring proportionality between the likely benefit and the risks of prolonging suffering, careful attention to prognosis and expected neurological outcomes, and rigorous alignment with goals of care. Early and ongoing involvement of palliative services, along with robust long-term care pathways, provides humane, value-concordant alternatives for patients with advanced disease. Psychotherapists and chaplains play integral roles in supporting families and clinical staff. Structured post-event debriefing and system-level safeguards are essential to mitigate burnout and moral distress within oncology teams. Initiating or discontinuing resuscitation in oncology requires expertise, empathy, and moral clarity. Dignity-preserving care depends on aligning interventions with patient values and realistic clinical endpoints. Acceptance of the natural course of dying represents an important component of responsible and patient-centred medical care. Full article

The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time–current characteristics of protective fuses—termed Protection Curve Consideration (PCC)—within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions. Full article

Multidrug resistance (MDR), frequently mediated by overexpression of the P-glycoprotein (P-gp) efflux transporter, remains a major challenge in the treatment of leukemia by limiting intracellular accumulation of chemotherapeutic agents such as daunorubicin (DNR). This study evaluates the applicability of a microfluidic-based single-cell biochip to investigate the reversal effects of microgram-level ginsenosides on daunorubicin uptake in multidrug-resistant leukemia cells. Pure ginsenosides are difficult to obtain in bulk and are typically available only in milligram quantities, which restricts their evaluation using conventional MDR assays such as flow cytometry that require large cell populations and substantial amounts of compounds. To address this limitation, a microfluidic single-cell biochip (SCB) requiring microgram quantities of ginsenosides (<100 µg) and fewer than ten cells was employed. Intracellular DNR accumulation was measured in the CEM/VLB1000 leukemia cell line following treatment with DNR alone or in combination with ginsenoside Rg3-R, ginsenoside Rg3-S, 20(S)-protopanaxatriol (PPT), and 20(S)-protopanaxadiol (PPD), in order to compare their relative efficacy in enhancing drug accumulation. Although Rg3-R and Rg3-S share highly similar chemical structures and are glycosylated derivatives of the PPD aglycone, Rg3-S exhibited greater potency in increasing intracellular daunorubicin accumulation than Rg3-R, and both were more effective than PPD. These findings underscore the importance of ginsenoside stereochemistry modulating P-gp-associated drug resistance and demonstrate the utility of the SCB platform for quantifying daunorubicin accumulation in multidrug-resistant leukemia cells at single-cell resolution. Full article

This paper proposes a hybrid quantum–classical framework for distribution network reconfiguration (DNR) under high distributed generation (DG) penetration, integrating nonlinear AC power-flow validation with the Quantum Approximate Optimization Algorithm (QAOA). Unlike prior quantum-assisted studies that rely on simplified DC or surrogate models, the proposed approach embeds AC-feasible loss evaluation directly within the combinatorial optimization loop. The methodology first evaluates all admissible switching configurations of the IEEE 33-bus system under DG integration using full AC power flow. The resulting loss landscape is compressed into a Quadratic Unconstrained Binary Optimization (QUBO) representation and mapped to an Ising Hamiltonian, enabling variational optimization via QAOA. The dominant configuration suggested by the quantum layer is subsequently validated through AC feasibility analysis. Simulation results show that the coordinated DG + QAOA strategy reduces active power losses from 282.938 kW (baseline) to 95.773 kW, corresponding to a 66.15% reduction relative to the original topology and an additional 20.62% improvement beyond DG-only operation. The minimum bus voltage increases from 0.8828 p.u. to 0.9531 p.u., satisfying IEEE 1547 limits, while requiring only two switching operations. These results demonstrate that embedding AC-consistent validation within a hybrid QAOA framework enhances physical realism, scalability, and solution quality for combinatorial optimization in active distribution networks. Full article

Background: Older adults with Alzheimer’s disease (AD) face a heightened risk of adverse hospital outcomes, including mortality. However, early identification of high-risk patients remains a challenge. While regression models provide interpretable associations, they may miss non-linear interactions that machine learning can uncover. Objective: To identify key predictors of in-hospital mortality among AD patients using both survey-weighted logistic regression and explainable machine learning. Methods: We analyzed hospitalizations among AD patients aged ≥60 in the 2017 Nationwide Inpatient Sample (NIS). The outcome was in-hospital death. Predictors included demographics, hospital variables, and 15 comorbidities. Logistic regression used survey weighting to generate nationally representative inference; XGBoost incorporated NIS discharge weights as sample weights during 5-fold hospital-grouped cross-validation and used the same weights in performance evaluation. Missing-value imputation and feature scaling were performed within the cross-validation pipelines to prevent data leakage. Model performance was assessed using AUROC, AUPRC, Brier score, and log loss. Feature importance was assessed using adjusted odds ratios and SHapley Additive exPlanations (SHAP). A sensitivity analysis excluded palliative care and DNR status and was re-evaluated under the same grouped cross-validation. Results: In the full model, logistic regression achieved AUROC 0.879 and AUPRC 0.310, while XGBoost achieved AUROC 0.887 and AUPRC 0.324. Palliative care (aOR 6.19), acute respiratory failure (aOR 5.15), DNR status (aOR 2.20), and sepsis (aOR 2.26) were the strongest logistic predictors. SHAP analysis corroborated these findings and additionally emphasized dysphagia, malnutrition, and pressure ulcers. In sensitivity analysis excluding palliative care and DNR status, logistic regression performance declined (AUROC 0.806; AUPRC 0.206), while XGBoost performed similarly (AUROC 0.811; AUPRC 0.206). SHAP corroborated the dominant signals from end-of-life documentation and acute organ failure in the full model; in the restricted model (excluding DNR and palliative care), SHAP highlighted physiologic and frailty-related features (e.g., dysphagia, malnutrition, aspiration risk) that may be more actionable when end-of-life documentation is absent. Conclusions: Combining regression with explainable machine learning enables robust mortality risk stratification in hospitalized AD patients. Restricted models excluding end-of-life indicators provide actionable risk signals when such documentation is absent, while the full model may better support resource allocation and goals-of-care workflows. Full article

To achieve carbon neutrality goals, large amounts of renewable energy sources (RESs) are being integrated into power systems. In particular, high penetration of distributed photovoltaic (PV) makes distribution networks highly stochastic, calling for dynamic distribution network reconfiguration (DNR). Existing DNR approaches can be broadly categorized into model-driven optimization-based methods and learning-based methods, with deep reinforcement learning (DRL) being a representative paradigm for fast online decision-making. Existing DNR models typically belong to mixed-integer linear programming, which requires solution methods such as deep reinforcement learning (DRL). However, existing methods commonly struggle to account for human factors, i.e., the time-varying preferences of distribution network operators in DRL decisions. To this end, this paper proposes a natural language-driven, human-in-the-loop DNR framework, which combines a DRL base policy for hour-level dynamic reconfiguration with a large language model (LLM)-based instruction supervision layer. Based on this human-in-the-loop framework, commands from operators in natural language are translated into online adjustments of safety-screened DRL switching actions. Therefore, the framework demonstrates the fast, model-free decision capability of DRL while providing an explicit and interpretable interface for incorporating temporary and context-dependent operator requirements without retraining. Case studies on IEEE 16-bus and 33-bus distribution networks show that the proposed framework reduces network losses, improves voltage profiles, and limits switching operations. It also achieves markedly higher compliance with operator instructions than a conventional model-based method and a pure DRL baseline. These results highlight a viable path to embedding natural language guidance into the data-driven operation of active distribution networks. Full article

Objective—Partial do-not-resuscitate (DNR) orders, directives specifying limited resuscitative efforts, are intended to align medical interventions with patient preferences. However, their complexity may introduce ambiguity, inconsistent care, and ethical challenges. Design—A retrospective review was conducted of inpatient partial code order entries over a three-year period at a single institution with a pediatric oncology and immunology cohort. Partial DNR orders were identified and categorized based on included or excluded interventions (chest compressions, defibrillation, intubation, mechanical ventilation, medications). Data was analyzed to assess the frequency, variation, and internal consistency of documented preferences as well as alignment with institutional definitions and clinical feasibility. Results—Partial DNR orders represented a small (n = 15, 7%) but notable proportion of total code status entries. Wide variability was observed in the combinations of permitted and withheld interventions, with orders containing internally conflicting instructions. Documentation of inconsistencies and unclear terminology were common, raising concerns about interpretability during emergent situations. Conclusions—Partial DNR orders demonstrate heterogeneity and potential for miscommunication. These findings suggest that while partial codes may reflect nuanced patient preferences, they pose operational and ethical risks that could compromise care clarity. Clinical implications are reviewed. These findings will guide institutional deliberations regarding whether to refine, restrict, or eliminate partial code order options to enhance patient safety and decision-making transparency. Full article

Distribution networks (DNs) now operate under tighter conditions due to rising penetration of renewables, active prosumers, and exposure to transmission-level contingencies. Distribution Network Reconfiguration (DNR) has proven effective for reducing losses, improving voltage profiles, and enhancing the resiliency of the grid. This paper introduces a three-stage optimization strategy for DNR, combining topological reconfiguration with reactive power support. The first stage, Reconfiguration of Tie-Line Switches (RTLS), utilizes a Particle Swarm Optimization (PSO) algorithm augmented with a Depth-First Search (DFS) mechanism to identify optimal radial structures that minimize active power losses. Once a viable configuration is established, the process proceeds to the second stage, Shunt Capacitor Sizing (SCS), wherein PSO is again employed to determine optimal capacitor sizing across predefined bus locations. The third stage reexecutes the RTLS process using the updated reactive power profile to assess whether further improvements in loss reduction can be achieved. If a superior topology is discovered, it is adopted as the final configuration; otherwise, the SCS solution is retained. This iterative and feedback-based architecture ensures an effective balance between network efficiency and voltage stability using a heuristic approach. The proposed methodology is validated on the IEEE 33-bus and IEEE 123-bus benchmark systems, as well as a custom 7-bus test case. Comprehensive scenario-based analysis, including normal, heavily, and lightly loaded conditions and varying power factor (PF) cases (good and poor PF), confirms the robustness and effectiveness of the approach in achieving considerable loss minimization and voltage profile improvement. For instance, in heavy-load conditions, active-power losses dropped by 39% and 70% for 33-bus and 123-bus cases, respectively. Full article

Diabetic nephropathy (DNR) remains a major complication of type 2 diabetes with limited options to halt progression. We evaluated whether DNR (a sulfur-rich extract from Hongsan garlic) confers renoprotection in a db/db mouse model. Seventy male C57BLKS/J mice were randomized into seven groups (db/m control, db/db control, metformin 250 mg/kg, DNR 100/300/900 mg/kg, and metformin 250 mg/kg + DNR 300 mg/kg) and treated orally for eight weeks. Physiological, biochemical, urinary, histological, and immunohistochemical(IHC) endpoints were assessed, including serum creatinine, blood urea nitrogen(BUN), lipids, glucose, urinary microalbumin/albumin-to-creatinine ratio(ACR), glomerular area, mesangial expansion, and renal KIM-1 and TGF-β1 expression. Chemical profiling of the DNR extract by HPLC and LC–MS/MS identified allicin as a principal sulfur-containing constituent, exhibiting a distinct retention peak at 2.90 min and a protonated molecular ion at m/z 162.1 [M]⁺ with diagnostic fragment ions at m/z 145.1, 120.1, and 99.0. Allicin was qualitatively confirmed as a characteristic component of DNR, serving as a representative chemical marker for compositional characterization. DNR produced dose-dependent improvements: reductions in serum creatinine and BUN, improved lipid and glycemic profiles, decreased urinary microalbumin and ACR, and amelioration of glomerular hypertrophy and mesangial matrix expansion. IHC showed lower KIM-1 and TGF-β1 staining in treated groups. Effects at higher DNR doses were comparable to or additive with metformin for several endpoints. These findings indicate that DNR has promising renoprotective effects in this preclinical model. Full article

The reconfiguration of distribution networks plays a crucial role in minimizing active power losses and enhancing reliability, but the problem becomes increasingly complex with the integration of distributed generation (DG). Traditional optimization methods and even earlier hybrid metaheuristics often suffer from premature convergence or require problem reformulations that compromise feasibility. To overcome these limitations, this paper proposes a novel hybrid algorithm that couples Particle Swarm Optimization (PSO) with Simulated Annealing (SA) through an adaptive inertia weight mechanism derived from the Lundy–Mees cooling schedule. Unlike prior hybrid approaches, our method directly addresses the original non-convex, combinatorial nature of the Distribution Network Reconfiguration (DNR) problem without convexification or post-processing adjustments. The main contributions of this study are fourfold: (i) proposing a PSO-SA hybridization strategy that enhances global exploration and avoids stagnation; (ii) introducing an adaptive inertia weight rule tuned by SA, more effective than traditional schemes; (iii) applying a stagnation-based stopping criterion to speed up convergence and reduce computational cost; and (iv) validating the approach on 5-, 33-, and 69-bus systems, with and without DG, showing robustness, recurrence rates above 80%, and low variability compared to conventional PSO. Simulation results confirm that the proposed PSO-SA algorithm achieves superior performance in both loss minimization and solution stability, positioning it as a competitive and scalable alternative for modern active distribution systems. Full article

Background/Objectives: Although research has demonstrated the positive impacts of hearing aid (HA) digital noise reduction (DNR), limited research is available on the impacts of the strength of DNR on listening effort. This study evaluated the effects of changes in the strength of HA DNR on listening effort, measured, behaviorally, using a self-report rating scale, and, physiologically, using pupillometry. The agreement between both measures was also examined. Methods: Eleven young adults with normal hearing completed a sentence-in-noise recognition task. Stimuli were processed through four noise reduction conditions (off, minimum, medium, maximum) using DNR algorithms found in conventional digital HAs. After sentence presentation, participants subjectively rated their perceived listening effort. Pupillometry was recorded during the task to assess changes in pupil size (a proxy of listening effort) during sentence recognition. Results: Participants’ perceived listening effort reduced as the noise reduction strength increased from off to medium DNR and then plateaued for the maximum DNR condition. Pupil dilation increased from off to medium DNR and then reduced for the maximum condition. Correlation analyses suggested no agreement between self-report and pupillometry measures of listening effort. Conclusions: Both self-report and pupillometry measures demonstrated changes in listening effort, with changes in the DNR strength indicating that noise reduction systems do provide benefit in reducing listening effort to a certain extent. Lack of agreement between the measures suggests that both methods might be assessing different constructs of listening effort and care should be taken while making methodological decisions to assess listening effort in individuals wearing HAs. Full article