Search Results (1,309)

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with progressive evolution and high mortality. Magnesium, copper and zinc are essential biometals involved in numerous biological processes in all organs of the human body. A lower level of zinc and magnesium and a higher cooper/zinc ratio are frequently encountered in patients with idiopathic pulmonary fibrosis but also in other forms of pulmonary fibrosis. These imbalances are involved in the main pathogenic mechanisms of idiopathic pulmonary fibrosis: alveolar epithelial cell lesions, oxidative stress, inflammation, fibroblast and myofibroblast proliferation, mitochondrial activity, excessive extracellular matrix accumulation, high collagen production, alveolar macrophage dysfunctions, and apoptosis. A multitude of experimental and clinical studies have shown the importance of these bivalent cations for the synthesis or activity of some important endogenous active substances (fatty acids, eicosanoids, sirtuin1, p53 protein, interleukins, growth factors, some enzymes, and others) involved in one form or another in the pathogenesis of IPF. There are no randomized clinical trials yet, but some clinical and experimental results suggest that the association of zinc and magnesium with pirfenidone and nintedanib could be beneficial and should be assessed as soon as possible after the onset of this disease. The correction of hypomagnesemia and hypozincemia, whenever they exist, must be performed as soon as possible after the diagnosis of fibrosis. Full article

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally segmented temperature sequences. Among all evaluated predictors, the Bidirectional Long Short-Term Memory (BiLSTM) network achieved the highest prediction fidelity, yielding an RMSE of 0.018 °C, a 35.7% improvement over the conventional Long Short-Term Memory (LSTM) (RMSE = 0.028 °C) due to its ability to simultaneously encode forward and backward temporal dependencies inherent in cyclic electrochemical operation. While CatBoost exhibited the strongest performance among classical regressors (RMSE = 0.022 °C), outperforming Random Forest, Gradient Boosting, Support Vector Regression, XGBoost, and LightGBM, it remained inferior to BiLSTM because it lacks the capacity to represent bidirectional electrothermal dynamics. This performance hierarchy confirms that LIB thermal evolution is not dictated solely by historical load sequences; it also depends on forthcoming cycling patterns and entropic interactions, which unidirectional and memoryless models cannot capture. The resulting hybrid physics-data-driven framework provides a reliable surrogate for real-time LIB thermal estimation and can be directly embedded within BMS to enable proactive intervention strategies such as predictive cooling activation, current derating, and early detection of hazardous thermal conditions. By coupling physics-based decomposition with deep sequential learning, this study establishes a validated foundation for next-generation LIB thermal-management platforms and identifies a clear trajectory for future work extending the methodology to module- and pack-level systems suitable for industrial deployment. Full article

The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework. Full article

To elucidate the long-term spatiotemporal patterns and key drivers factors, this study employed a meta-analysis of data from soil containing Potentially Toxic Elements (Cd, As, Cr, Hg, and Pb) in Chinese farmland soils from 2003 to 2025. The geoaccumulation index, the potential ecological risk index, and standard deviation ellipses were used to assess the spatiotemporal evolution of heavy metal accumulation and ecological risk, while the Random forest–SHapley Additive exPlanations (RF-SHAP) method was employed to identify driving mechanisms. At the national scale, Cd and Hg are significantly enriched relative to the background values, whereas As, Cr, and Pb remained at relatively low levels, with enrichment ranked as Cd > Hg > Pb > Cr > As. Cd and Hg indicated mild pollution, but the Sichuan Basin emerged as a hotspot, where Cd reached moderate pollution and showed strong ecological risk, and Hg also exhibited high ecological risk. Over the past two decades, the contamination center shifted from coastal to southwestern inland regions, with an expanded and more dispersed distribution. Since 2017, Cd and Hg pollution levels have stabilized, suggesting that the aggravating trend has been preliminarily curbed. Industrial waste and wastewater discharge, irrigation and fertilization were identified as the primary anthropogenic factors of soil heavy metal accumulation, while climatic factors (temperature, precipitation, and solar radiation) and soil physicochemical properties (pH, clay content, and organic matter) played fundamental roles in spatial distribution and accumulation. Our findings call for targeted predictive research and policies to manage heavy metal risks and preserve farmland sustainability in a changing climate. Full article

Against the backdrop of China’s “dual carbon” strategy and regional integration, enhancing green innovation efficiency (GIE) has become a core issue for the Yangtze River Delta Urban Agglomeration (YRDUA) in achieving sustainable and high-quality development. This study employs the Super EBM model to measure the GIE of 41 cities in the YRDUA from 2012 to 2022 and further integrates a modified gravity model with social network analysis to uncover the structural complexity and spatial directionality of its spatial association network. In addition, the Exponential Random Graph Model (ERGM) is applied to explore the formation mechanisms of the green innovation efficiency network. Results show the following: (1) GIE presents a fluctuating upward trend, with the mean rising from 0.747 in 2012 to 0.906 in 2022 and disparities gradually narrowing, but provincial gradients persist, implying potential “Matthew effect” risks. (2) Network density continues to increase, with S-density rising from 0.0061 in 2012 to 0.0335 in 2022; supporting and basic connections serve as key drivers of network complexity, whereas the significant decline of edge connections may weaken the network’s extensibility. (3) Node connections display preference and attachment, causing polarization; transitivity and triadic cooperation rise markedly, increasing by 41.89% and 40.86%, respectively, reflecting strong self-organization. (4) Reciprocity and agglomeration drive network formation, and economic and technological differences promote it, while disparities in innovation input and government roles vary across periods. Geographic distance hinders formation, though its effect is weakening. These findings enhance the methodological approaches to sustainability research and provide insights for optimizing regional cooperation and advancing green integration in the YRDUA. Full article

Sequence prediction is widely applied and has significant theoretical and practical application value in fields such as meteorology and medicine. Traditional models, such as LSTM(Long Short-Term Memory) and GRU(Gated Recurrent Unit), may perform better than this model when dealing with short-term dependencies, but their performance may decline on long sequences and complex data, especially in cases where sequence fluctuations are significant. However, the Transformer requires a large amount of computing resources (parallel computing) when dealing with long sequences. Aiming to solve the problems existing in sequence prediction models, such as insufficient modeling ability of long sequence dependencies, insufficient interpretability, and low efficiency of multi-element heterogeneous information fusion, this study embeds sequential data into the knowledge graph, enabling the model to associate context information when processing complex data and providing more reasonable decision support for the prediction results. Given the historical sequence and the predicted future sequence, three groups of sequence lengths were set in the experiment. And MAE (Mean Absolute Error)and MSE (Mean Square Error) are used as indicators for sequence prediction. In sequence prediction, dynamic evolution is conducive to enhancing the ability of the prediction model to capture the changing patterns of the current time series data and significantly improving the reliability of the prediction results. Experiments were conducted using five datasets from different application fields to verify the effectiveness of the prediction model. The experimental results show that based on the randomization of the prediction time step, the prediction model proposed in this study significantly improves the expression performance of stationary sequences. It has addressed the shortcomings of these traditional methods, such as maintaining good performance in the case of short sequences with large fluctuations. Full article

Classical thermodynamics (CT) has become integrated into everyday life, especially through its applications in engineering. In contrast, out-of-equilibrium thermodynamics (OET) is often viewed as a fundamental science that seems distant from daily experiences. While “energy” is a familiar term in households, “entropy,” which refers to degraded energy, remains enigmatic. This gap in understanding has significant implications for developing effective sustainability practices. CT typically emphasizes the efficiency of individual systems that produce work, often overlooking the entropy production that occurs within larger, interconnected systems. This paper aims to establish a philosophical framework that transforms OET into what is referred to as “lived thermodynamics.” This framework is grounded in pragmatism, particularly drawing from the early synthesis of thermodynamics and evolutionary theory proposed by Charles S. Peirce. A central aspect of this approach involves shifting the focus from traditional “systems” to out-of-equilibrium assemblages. In these assemblages, the physical trends of entropy production are often interrupted and redirected by evolutionary innovations and random events. The evolving envelope of open systems within these assemblages manifests an increasing rate of entropy production. This synthesis of thermodynamics and evolutionary theory builds on Lotka’s pioneering contributions and contemporary theories, particularly Vermeij’s work on the evolution of power. The framework introduces a sustainability criterion based on entropy. By applying this criterion, OET can evolve into “lived thermodynamics,” fostering a holistic understanding of energy use in devices and technological systems while considering the broader implications of entropy production in the out-of-equilibrium assemblages in which we live. Full article

Pediatric kidney transplantation (KTx) offers the best outcomes for children with end-stage renal disease (ESRD), offering dramatic improvements in survival, quality of life, growth, and developmental outcomes compared to dialysis. Modern regimens centered on tacrolimus, mycophenolate mofetil, and risk-adapted induction have substantially reduced acute rejection and improved graft survival. This viewpoint summarizes the evolution of pediatric immunosuppression, current practice trends, and emerging strategies aimed at minimizing toxicity while preserving long-term graft function. Recent data show increasing use of T-cell-depleting induction, selective application of IL-2 receptor antagonists, and gradual adoption of steroid-sparing and mTOR-based protocols. Nevertheless, progress is limited by a scarcity of pediatric randomized trials, continued reliance on extrapolated adult evidence, infection risk, long-term metabolic complications, and adherence challenges during adolescence. Insights from recent trials including steroid minimization, everolimus-based regimens, and selective Belatacept use highlight opportunities for more individualized, risk-adapted therapy. Future efforts must prioritize precision approaches supported by biomarkers, multicenter collaboration, and long-term follow-up. Overall, contemporary trends support a shift toward tailored immunosuppression that balances efficacy with safety to optimize outcomes in pediatric KTx recipients. Full article

Background: Septic shock remains a critical challenge with high mortality, particularly in refractory cases requiring high doses of vasopressors. Hemoadsorption with the oXiris^® membrane, capable of simultaneously removing endotoxins, cytokines, and damage-associated molecular patterns (DAMPs), represents a personalized therapeutic strategy targeting the underlying pathophysiology. However, clinical evidence on its impact remains limited and lacks consensus. This study aims to analyze the effects of oXiris^® therapy on hemodynamic, inflammatory, and perfusion parameters in a real-world cohort of patients with septic shock. Methods: We conducted a retrospective cohort study in a surgical Intensive Care Unit (ICU) at a tertiary hospital, including 45 adult patients with septic shock treated with continuous renal replacement therapy using the oXiris^® membrane for at least 48 h. The institutional protocol involved filter changes at least every 24 h during the first 48 h of therapy. Hemodynamic variables, vasopressor doses, and biochemical markers were collected at baseline (T0), 24 h (T1), and 48 h (T2). The primary objective was to describe the evolution of these parameters. Secondary objectives included analysis of 30-day mortality and identification of prognostic factors. Results: The cohort consisted of 45 patients (80.0% male, median age 71 years), with a predominance of abdominal infectious focus (71.1%). A significant reduction in median norepinephrine requirements was observed from T0 to T2 (p < 0.00001), along with a significant increase in mean arterial pressure (MAP) (p < 0.00001). Key markers of perfusion and inflammation also improved, with a significant decrease in arterial lactate (p < 0.00001) and procalcitonin (p = 0.00082) at 48 h. No significant changes were observed in the Sequential Organ Failure Assessment (SOFA) score. The observed mortality rate in the ICU was 31.1%, lower than the median predicted mortality by Simplified Acute Physiology Score II (SAPS II) (37%). Baseline Charlson Comorbidity Index (CCI), creatinine, arterial lactate, and SOFA score were independent predictors of mortality. Conclusions: In this cohort of septic shock patients, therapy with oXiris^®, applied with a frequent filter exchange protocol, was associated with a significant reduction in vasopressor requirements and an improvement in key hemodynamic, perfusion, and inflammatory markers. The observed ICU mortality was lower than predicted by severity scores. These findings support the role of oXiris^® as a personalized adjuvant therapy in specific septic shock phenotypes and underscore the need for prospective randomized trials to confirm these benefits. Full article

Background: Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are clonal stem cell disorders in which disrupted post-transcriptional regulation contributes to aberrant hematopoiesis and leukemic transformation. The miRNA biogenesis machinery, which comprises Drosha, DGCR8, Dicer, TARBP2, and AGO1, ensures the precise maturation of miRNAs that control lineage commitment and proliferation. However, the extent to which alterations in this pathway reshape hematopoietic gene networks during myeloid disease evolution remains largely unexplored. Methods: Bone marrow samples from newly diagnosed, untreated MDS and AML patients and matched healthy controls were analyzed for the expression of five key miRNA biogenesis genes using quantitative real-time PCR. Statistical comparisons, correlation matrices, and ROC analyses were performed to characterize gene-expression differences. These results were integrated with multigene logistic modeling, decision-curve analysis, and exploratory random forest/SHAP approaches to evaluate molecular interactions and diagnostic relevance. Results: DROSHA, DICER1, and TARBP2 were significantly downregulated in both MDS and AML, suggesting impaired miRNA maturation and a loss of global post-transcriptional control. DGCR8 expression increased across higher-risk MDS groups, suggesting compensatory activation of the Microprocessor complex, whereas AGO1 levels remained relatively stable, consistent with partial maintenance of RISC function. Correlation analyses revealed a co-regulated DROSHA–TARBP2–AGO1 module. ROC, logistic, and machine learning models identified DGCR8 and DICER1 as the strongest diagnostic discriminators. The integrated five-gene signature achieved high discriminative performance (AUC ≈ 0.98) and showed promise but remains preliminary potential for clinical application. Conclusions: Our findings suggest that defects in miRNA biogenesis disrupt hematopoietic homeostasis, reflecting common mechanisms in MDS and AML. The dysregulation of DICER1, DGCR8, and TARBP2 offers insights into miRNA-driven leukemogenesis and may pave the way for miRNA-based diagnostic and therapeutic strategies, pending validation in larger cohorts. Although transcript-level data are provided, future studies should include functional validation to determine the impact on downstream miRNA processing and hematopoietic pathways. Full article

Classical branching-process theory, developed by Galton and Watson in the nineteenth century and later refined by Fisher and Haldane, provides the formal framework for quantifying the fate of new mutants, new viral and bacterial pathogens, new colonization of invasive species, etc. It is a powerful tool to quantify and predict the effect of differential reproductive success on the speciation potential of evolutionary lineages. Here, I revisit the conceptual framework of the branching process, detail its mathematical development over time, tie up a few historical loose strings, illustrate the calculation of the exact extinction probability for the Poisson-distributed reproductive success with the Lambert function (which is often missing in the ecological and evolutionary literature), and highlight the potential applications of the branching process in modern ecology and evolutionary biology, especially in deriving the extinction probability and extinction time. I also highlight a few misconceptions about human demography in the US that can be readily dismissed by applying probability tools such as branching processes. Full article

We present a narrative review focusing on pulses’ geographical origin and distribution, their impact on human evolution and history, and their influence on human health. Pulses, including dry peas, beans, and lentils, are renowned for their richness in chemical antioxidants. Despite containing antinutrients, processing techniques preserve their health advantages. Epidemiological research has consistently demonstrated that the consumption of pulses is associated with favorable effects on metabolism. This evidence is further supported by molecular and clinical research, which has elucidated potential nutrigenomic mechanisms and effects on gut microbiota composition underlying their health benefits. However, the literature lacks randomized controlled clinical trials investigating the effects of pulses on health outcomes. Despite this limitation, our review provides valuable insights into the potential beneficial effects of pulses in ameliorating metabolic disorders and reducing the risk of dementia and Alzheimer’s disease. Acknowledging the current limitations, we identify areas for further research to generate additional evidence. Specifically, well-designed randomized controlled trials are needed to thoroughly assess the efficacy of pulses in preventing metabolic diseases. Addressing these research gaps will enhance our understanding of the health benefits associated with pulse consumption and facilitate evidence-based dietary recommendations to improve public health outcomes. Full article

This paper examines the changes in the level of informal and shadow economy, mapping their evolution within the EU and measuring their implications on economic growth. The study also addresses the issue of conceptual differences in the methodology for measuring these phenomena. We used a two-dimensional methodological approach, combining theoretical and empirical analysis. Initially, the bibliometric analysis—conducted exclusively on the Web of Science Core Collection to ensure methodological rigour, international comparability, and high-quality, standardised data—reveals the evolution of the subject and the inconsistencies in the conceptualisation and measurement of phenomena associated with the shadow economy. Subsequently, the normative analysis highlighted the most relevant norms, directives, and projects developed and applied at the European Union level to prevent and combat tax evasion activities. Finally, the empirical dimension of this study was conducted through structural equation modelling and fixed and random effects regressions, using data from the EU 27 member states for the period 2000–2022. Our results reveal a potential relationship between the level of scientific research and the prevalence of the shadow economy within EU countries and indicate a negative effect of the informal economy on economic growth, as undeclared work produces goods and services that are consumed in the informal economy and hinders economic growth. Since the level of the shadow economy has not decreased proportionally with the increase in the GDP per capita, we conclude that the efforts to combat the shadow economy are insufficient, and tax administration needs to be more drastic and efficient. Full article

Accurate prediction of meteorological drought is essential for climate adaptation and sustainable water management in arid regions. Using the Standardized Precipitation Evapotranspiration Index (SPEI) derived from 1962–2021 meteorological observations, this study analyzed multi-scale drought evolution in the Arid Area of Northwest China (AANC) and revealed a distinct shift from wetting to drying after the 1997 abrupt warming. Correlation analysis indicated that the rapid temperature rise significantly enhanced evapotranspiration, offsetting the humidification effect of precipitation. To improve predictive performance, a Stacking ensemble framework was developed by integrating Elastic Network, Random Forest, and Prophet + XGBoost models, with the outputs of the base learners serving as inputs to a meta-regression layer. Compared with single models (NSE ≤ 0.742), the integrated model achieved superior accuracy (NSE = 0.886, MAE = 0.236, RMSE = 0.214), and its residuals followed a near-normal distribution, indicating high robustness. Future projections for 2022–2035 show consistent declines in SPEI₁, SPEI₃, SPEI₆, SPEI₁₂, and SPEI₂₄, suggesting that the AANC will experience increasingly frequent and severe droughts as warming-induced evaporation continues to outweigh the humidification effect of precipitation. This integrated framework enhances drought predictability and provides theoretical support for climate risk assessment and adaptive water management in arid environments. Full article

Cross-border greenfield investment in the financial services sector is increasingly understood not as isolated flows, but as a complex, dynamic global system. This systemic perspective is essential for understanding its holistic structure and evolution amidst globalisation and digital transformation. This paper utilises financial services greenfield investment projects from 100 major economies from 2003 to 2021 to construct the Global Financial Services Greenfield FDI Network (GFS-GFN). By combining Social Network Analysis (SNA) and Temporal Exponential Random Graph Models (TERGMs), we systematically investigate its dynamic evolutionary features and endogenous mechanisms. The findings reveal the following: (1) System-wide, the network exhibits persistent expansion, “small-world” properties, and a pronounced “rich club” effect among source countries. (2) Nodally, the structure has evolved from a US-UK “dual-core” to a multipolar configuration, as emerging hubs like China, the UAE, and Singapore rapidly approach the traditional centres. (3) Structurally, the network has fragmented from Euro-American dominance into five major communities, forming a diverse, complementary pattern. Network evolution is primarily driven by endogenous mechanisms. Investment relationships widely exhibit reciprocity, preferential attachment, transitive closure, and marked path dependence. Full article