Advancing Open Science

Objective: The aim of this study was to evaluate the impact of chronic kidney disease (CKD) on clinical presentation, laboratory parameters, ECG, and echocardiographic features of patients with chronic heart failure (CHF). Methods: This retrospective cross-sectional study included 2227 patients hospitalized in a tertiary care center due to CHF. Patients were divided into two groups based on the presence of CKD, defined as eGFR < 60 mL/min/1.73 m². Demographic, clinical, laboratory, and echocardiographic data were collected for all patients. Comparative analyses were performed to assess differences in cardiovascular risk factors, comorbidities, laboratory parameters, and echocardiographic findings between the two groups. Results: The proportion of men was significantly higher in the non-CKD group, whereas women predominated in the CKD group (p < 0.001). Dyspnea, orthopnea, leg swelling, claudication, and expectoration were significantly more frequent in patients with CKD, while chest pain and palpitations were more common in the non-CKD group (all p < 0.05). A significant difference in the distribution of NYHA functional classes was observed between the groups (p < 0.001), with NYHA class IV being more prevalent in the CKD group and classes II and III more frequent in the non-CKD group. Levels of CRP and NT-proBNP were significantly higher in the CKD group (p < 0.001). In-hospital mortality was 2.5-fold higher in patients with CKD (28.6% vs. 11.1%; p < 0.001). Conclusions: Coexistence of CKD was associated with a more severe clinical presentation, advanced functional limitation, and a distinct laboratory and echocardiographic profile in CHF patients.

Achieving high reliability remains the critical challenge for pulsed power supplies (PPS), whose core components are susceptible to severe degradation and catastrophic failure due to long-term operation under electrical, thermal and magnetic stresses, particularly those associated with high voltage and high current. This reliability challenge fundamentally limits the widespread deployment of PPSs in defense and industrial applications. This article provides a comprehensive and systematic review of the reliability challenges and recent technological progress concerning PPSs, focusing on three hierarchical levels: component, system integration, and extreme operating environments. The review investigates the underlying failure mechanisms, degradation characteristics, and structural optimization of key components, such as energy storage capacitors and power switches. Furthermore, it elaborates on advanced system-level techniques, including novel thermal management topologies, jitter control methods for multi-module synchronization, and electromagnetic interference (EMI) source suppression and coupling path optimization. The primary conclusion is that achieving long-term, high-frequency operation depends on multi-physics field modeling and robust, integrated design approaches at all three levels. In summary, this review outlines important research directions for future advancements and offers technical guidance to help speed up the development of next-generation PPS systems characterized by high power density, frequent repetition, and outstanding reliability.

Lymph node evaluation is a central determinant of oncologic quality in the surgical management of non-small-cell lung cancer (NSCLC). Accurate assessment of hilar and mediastinal lymph nodes underpins pathologic staging, informs postoperative treatment decisions, and remains essential for prognostic stratification and assessment of resection completeness. Although international guidelines provide clear recommendations, real-world data consistently demonstrate substantial variability in lymph node staging practices, with inadequate evaluation frequently observed across institutions and surgical settings. Insufficient nodal assessment, manifested as the omission of mediastinal staging, limited station sampling, or low lymph node yield, is associated with reduced nodal upstaging, inappropriate omission of adjuvant therapy, higher recurrence rates, and inferior long-term survival. Contemporary guidance from major societies, including the National Comprehensive Cancer Network, European Society of Thoracic Surgeons, International Association for the Study of Lung Cancer, and the Commission on Cancer, has increasingly converged on a station-based definition of adequacy, emphasizing systematic evaluation of both N1 and N2 nodal stations rather than reliance on absolute node counts alone. In parallel, preoperative mediastinal staging algorithms have evolved toward routine use of endobronchial and esophageal ultrasound as first-line invasive modalities, reserving surgical mediastinoscopy for selected high-risk or inconclusive cases. Evidence from randomized trials, population-level databases, and meta-analyses indicates that thorough nodal assessment improves staging accuracy and survival, while recent data support the selective use of lobe-specific or tailored lymphadenectomy in carefully staged, low-risk early disease. Finally, emerging quality improvement interventions, including standardized specimen handling, operative checklists, and multidisciplinary feedback mechanisms, have demonstrated measurable improvements in guideline adherence and patient outcomes. This narrative review integrates contemporary evidence and guideline recommendations to outline a practical framework for implementing reliable, high-quality lymph node staging in modern lung cancer surgery.

Sintering is a key processing route to consolidate nuclear fuel powders into dense compacts, yet the atomic-level mechanisms governing the sintering of actinide compounds remain poorly understood. Herein, the sintering kinetics and structural evolution of uranium mononitride (UN) nanoparticles are investigated using molecular dynamics (MD) simulations. A three-stage sintering mechanism is revealed based on the symmetrical dual nanoparticle models: initial surface diffusion and neck formation, followed by interface amorphization driven by shear stress, and finally, lattice reconstruction and recrystallization, which peak during the cooling process. By studying the effect of sintering temperature, we find that near-complete densification with good structural integrity is achieved at 1900 K, whereas further increasing the temperature (to 2000 K) led to microstructural instability and near-overburning. In addition, holding time exhibits a clear saturation effect, with variations in holding time showing no significant impact on sintering morphology or density. Therefore, sintering temperature is the dominant factor determining sintering quality. The atomic level insights provided by this work reveal the nonlinear temperature dependence and time saturation effect of UN nanoparticle sintering, and provide a theoretical basis for the prediction, design, and optimization of nuclear fuel sintering process.

Background/Objectives: Chronic Kidney Disease (CKD) is a global public health burden with a rising prevalence driven by population aging. Existing prediction models, such as the Kidney Failure Risk Equation (KFRE), often lack generalizability across ethnicities and comprehensive systemic indicators. This study aimed to develop and validate a machine learning model for predicting CKD progression by integrating traditional risk factors with novel composite indicators reflecting systemic health. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS, n = 2500) was used for model training. External validation was performed using independent cohorts from the English Longitudinal Study of Ageing (ELSA, n = 1200) and the Health and Retirement Study (HRS, n = 1500). Multiple machine learning algorithms, including XGBoost, were employed. Feature engineering incorporated composite indicators such as the frailty index (FI), triglyceride–glucose (TyG) index, and aggregate index of systemic inflammation (AISI). Results: The XGBoost model achieved an area under the curve (AUC) of 0.892 in the training set and maintained robust performance in external validation (AUC 0.867 in ELSA, 0.871 in HRS), outperforming the KFRE (AUC 0.745). SHAP analysis identified the FI as the most influential predictor. Decision curve analysis confirmed the model’s clinical utility. Conclusions: This machine learning model demonstrates high accuracy and cross-ethnicity validity, offering a practical tool for early intervention and personalized CKD management. Future work should address limitations such as the retrospective design and expand validation to underrepresented regions.

Background: Pulmonary fibrosis (PF) currently lacks effective therapeutic interventions. Roxadustat, an oral small-molecule inhibitor of hypoxia-inducible factor prolyl hydroxylase, has been shown in several studies to attenuate the progression of fibrotic diseases. However, its therapeutic efficacy in PF remains to be fully elucidated. The aim of this study was to evaluate roxadustat’s therapeutic benefits on PF as well as the underlying mechanisms of action. Methods: Bleomycin was administered intraperitoneally to establish a PF mouse model. H&E staining, Masson staining, and immunohistochemistry (IHC) were used to assess histopathological and fibrotic changes. Changes in the expression levels of inflammatory mediators, including IL-1β, TGF-β1, and TNF-α, were examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Network pharmacology combined with transcriptomic analysis was employed to identify potential target genes and associated signaling pathways. Subsequently, RT-qPCR and Western blot analyses were carried out to experimentally validate the predicted targets and pathways and to verify the protective effects of roxadustat in PF mice. Results: Roxadustat markedly ameliorated bleomycin-induced pulmonary fibrosis in mice. The therapeutic effect was evidenced by a reduction in alveolar damage, thinner alveolar septa, diminished infiltration of inflammatory cells, and decreased collagen deposition. Concomitantly, the expression levels of inflammatory mediators, including IL-1β, TGF-β1, and TNF-α, were significantly lowered. Integrated network pharmacology and transcriptomic analyses revealed the involvement of critical signaling pathways, specifically nuclear factor-kappa B (NF-κB) and peroxisome proliferator-activated receptor (PPAR). Experimental validation further demonstrated that roxadustat downregulated the expression of key genes (S100A8, S100A9, and Fos) in murine lung tissues. It also suppressed the protein ratios of phosphorylated p65 to total p65 and phosphorylated IκBα to total IκBα. Moreover, roxadustat treatment upregulated PPARγ protein expression. Conclusions: These data indicate that roxadustat ameliorates bleomycin-induced PF in mice, an effect associated with modulation of the NF-κB and PPAR signaling pathways. The findings provide a preclinical rationale for further investigation of roxadustat as a potential treatment for PF.

In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, nozzle numbers, heights of nozzle combinations, and oil flow rates. In addition, an artificial neural network (ANN) model based on two algorithms is developed for predicting thermal performance under various operating conditions. The flat jet nozzle shows the lowest maximum winding temperature of 120.3 °C and a superior heat transfer coefficient of 3028.6 W/m²-K compared to both full cone nozzles. The power consumption for the flat jet nozzle is higher at 123.9 W compared to other nozzle types. The combination of four flat jet nozzles shows improved oil spray distribution and enhanced cooling compared to combinations of two and six flat jet nozzles. Further, the thermal performance of oil spray cooling with four flat jet nozzles improves when height and oil flow rate are increased. Oil spray cooling with the best configuration shows a winding temperature, heat transfer coefficient, and injection pressure of 98.9 °C, 3408.6 W/m²-K and 4.86 bar, respectively, at a flow rate of 20 LPM. The proposed neural network model with a Levenberg–Marquardt (LM) training variant and logarithmic–sigmoidal (Log) transfer function shows the lowest prediction error within ±2%.

Plastic pollution raises concerns for health and the environment. Plastics are not biodegradable but gradually erode to microplastic and nanoplastic particles spreading almost everywhere. Nanoplastics exhibit colloidal behavior. Thereby, their analysis can be accomplished by refractometry, preferably by an on-chip tool. We present a study of such colloids using a microfabricated Fabry–Pérot cavity with curved mirrors, which holds a capillary micro-tube used both for fluid handling and light collimation, resulting in an optically stable microresonator. Despite the numerous scatterers within the sample, the sub-millimeter scale cavity provides the advantages of reduced interaction length while maintaining light confinement. This significantly reduces optical loss and hence keeps resonance modes with quality factors (resonant frequency/bandwidth) above 1100. Therefore, small quantities of colloids can be measured by the interference spectral response through the shift in resonant wavelengths. The particles’ Brownian motion potentially causing perturbations in the spectra can be overcome either by post-measurement cross-correlation analysis or by avoiding it entirely by taking the measurements at once by a wideband source and a spectrum analyzer. The effective refractive index of solutions with solid contents down to 0.34% could be determined with good agreement with theoretical predictions. Even lower detection capabilities might be attained by slightly altering the technique to cause particle aggregation achieved solely by light.