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Multidimensional Risk Index With Treatable Traits for Hospitalized Asthma Outcomes: Clinical Implication

2025-05-01T00:00:00Z

Title: Multidimensional Risk Index With Treatable Traits for Hospitalized Asthma Outcomes: Clinical Implication Authors: Yuan, L; Zhao, C; Wang, L; Zhang, L; Liu, Y; Liu, L; Feng, M; Wang, G; Zhang, S; Yuan, Y; Wang, Q; Li, L; Liao, S; Kang, D; Zhang, X Abstract: Abstract RATIONALE: Asthma exacerbations (AEs) lead to significant hospitalizations, reduced quality of life, and rising healthcare costs. Despite progress in asthma management, serious in-hospital adverse outcomes remain a major challenge, with their immune-inflammation patterns largely unexplored. Guidelines suggest multidimensional assessments (MDA) for hospitalized AE patients. However, comprehensive MDA tools incorporating treatable traits specifically designed for AEs are lacking, as current research has largely focused on single-dimensional predictors.METHODS: The adverse outcomes risk index for hospitalized asthma patients (AORI-HAP), a tool that predicts in-hospital adverse outcomes, incorporating key treatable traits to guide personalized treatment, was developed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Patients were categorized into three risk groups based on the tertiles of the AORI-HAP score to analyze the incidence of composite in-hospital outcomes across distinct risk levels. Mediator analysis employed underlying mechanisms leading to adverse outcomes in high-risk patients. RESULTS: The AORI-HAP incorporates multiple indicators spanning 8 dimensions, including demographics, comorbidities, immune-inflammatory mediators, liver blood tests, renal function tests, coagulation function tests, arterial blood gas analysis and biochemistry. Key predictors in AORI-HAP included a neutrophil-to-lymphocyte ratio > 8.3 (RR = 9.26, P < 0.001), AST/ALT ratio > 1.41 (RR = 3.73, P < 0.001), smoking history ≥ 10 pack-years (RR = 3.54, P = 0.005), D-Dimer ≥ 5 mg/L (RR = 3.25, P = 0.002), and fasting blood glucose ≥ 7 mmol/L (RR = 3.20, P = 0.001). Each three-unit increment in the AORI-HAP score predicted an additional day in hospital length of stay. The AORI HAP demonstrated strong predictive capability (AUC = 0.91, 95% CI: 0.86-0.95), with a sensitivity of 90.48% and specificity of 69.61%. NLR mediated 26.7% of the effect, linking high-risk status to the composite outcome.CONCLUSION: The AORI-HAP represents the first multidimensional risk-scoring tool specifically designed to predict adverse in-hospital outcomes for patients hospitalized with AEs. Distinct from previous models that rely on single indicators, AORI-HAP integrates a comprehensive range of factors and treatable traits, providing a thorough risk assessment. This tool not only identifies high-risk patients but also aids in informed clinical decision-making. Our study highlights the prevalence of non-eosinophilic inflammation among patients with AEs, suggesting neutrophils could be significant potential targets for assessment and therapeutic intervention in AEs.

Layered-to-rocksalt atomic reconfiguration on O3-type cathodes surface for high-energy and durable sodium-ion batteries

2025-10-01T00:00:00Z

Title: Layered-to-rocksalt atomic reconfiguration on O3-type cathodes surface for high-energy and durable sodium-ion batteries Authors: Liu, M; Guan, ZK; Zheng, L; Jing, P; Chen, SF; Xu, SW; Hu, LJ; Liu, X; Zhao, L; Xiao, B; Wang, PF Abstract: High-energy O3-type cathode materials have been intensively pursued due to the immense potential of sodium-ion batteries as a scalable and economic energy storage solution. However, their intrinsic sensitivity of surface to humid air inevitably triggers detrimental bulk degradation and the formation of ionically/electronically insulating surface residuals, severely impairing their battery performance and commercialization efforts. Here, we present a transformative layered-to-rocksalt atomic reconfiguration strategy that achieves dual breakthroughs, the elimination of residual alkalis and the in-situ construction of a robust layered-rocksalt heterostructure surface in the prototypical O3-NaNi1/3Fe1/3Mn1/3O2 cathode. This ingenious design defies conventional trade-offs, simultaneously preserving rapid Na⁺ diffusion kinetics, ensuring exceptional electrochemical reversibility and reinforcing structural stability. Consequently, the engineered cathode demonstrates a superior initial Coulombic efficiency of 97.6 %, a high cycling durability with capacity retention of 80.1 % after 300 cycles at 1 C and a new benchmark for rate capability with 78.9 % capacity retention at a high rate of 10 C. The proposed surface layered-to-rocksalt atomic reconfiguration strategy exemplifies a groundbreaking electrode design concept and opens up a wide of compositional possibilities for future development of high-power and high-energy cathodes, marking a significant step forward in the evolution of sodium-ion battery technology.

Deterministic and efficient source of frequency-polarization hyper-encoded photonic qubits

2025-06-25T00:00:00Z

Title: Deterministic and efficient source of frequency-polarization hyper-encoded photonic qubits Authors: Coste, N; Fioretto, DA; Thomas, SE; Wein, SC; Ollivier, H; Maillette de Buy Wenniger, I; Henry, A; Belabas, N; Harouri, A; Lemaitre, A; Sagnes, I; Somaschi, N; Krebs, O; Lanco, L; Senellart, P Abstract: The frequency or color of photons is an attractive degree of freedom to encode and distribute quantum information over long distances. However, the generation of frequency-encoded photonic qubits has so far relied on probabilistic nonlinear single-photon sources and inefficient gates. Here, we demonstrate the deterministic generation of photonic qubits hyper-encoded in frequency and polarization based on a semiconductor quantum dot in a cavity. We exploit the double dipole structure of a neutral exciton and demonstrate the generation of any quantum superposition in amplitude and phase, controlled by the polarization of the pump laser pulse. The source generates frequency-polarization single-photon qubits at a rate of 4 MHz corresponding to a generation probability at the first lens of 28±2%, with a photon number purity >98%. The photons show an indistinguishability >91% for each dipole and 88% for a balanced quantum superposition of both. The density matrix of the hyper-encoded photonic state is measured by time-resolved polarization tomography, evidencing a fidelity to the target state of 94±8% and concurrence of 77±2%, here limited by frequency overlap in our device. Our approach brings the advantages of quantum dot sources to the field of quantum information processing based on frequency encoding.

Log-transformed approaches to variance estimation using auxiliary data

2025-12-01T00:00:00Z

Title: Log-transformed approaches to variance estimation using auxiliary data Authors: Singh, P; Sharma, P; Singh, A; Zaman, T; Emam, W Abstract: Reliable statistical inference requires accurate population variance estimate, especially in domains where measurement limitations and intrinsic variability impact data. Applying traditional estimators to skewed or heavy-tailed populations frequently results in inefficient results. We provide a novel class of generalized logarithmic variance estimators that use logarithmic transformations and auxiliary data to stabilize variance and improve estimator performance in order to overcome this constraint. We calculate the suggested estimators’ bias and Mean Squared Error (MSE) expressions and assess their effectiveness using comprehensive Monte Carlo simulations with different sample sizes. The suggested estimator, T r 1 − , produces a significant reduction in MSE up to 57% increase in efficiency (PRE) at n=600 when compared to the usual estimator. The suggested methodologies resilience and suitability for high-variability situations are further confirmed using real-world datasets. In every context, the findings show that the suggested estimators perform better than the current ones in terms of lower MSE and greater PRE. This research demonstrates how logarithmic transformations may be used to create variance estimators that are more accurate and effective, especially when auxiliary variables are provided.