Search Results (21)

Airborne particulate matter (PM) triggers oxidative stress and inflammation in pulmonary tissues, contributing to chronic respiratory diseases. This study evaluated the antioxidant and anti-inflammatory effects of a combined extract of Haematococcus pluvialis (H. pluvialis) and walnut shell (HW extract) and its protective efficacy against PM_2.5-induced pulmonary inflammation. Extracts mixed at different ratios (10:0–0:10, w/w) were tested using 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging, cell-based assays, HPLC quantification, molecular docking, and a PM_2.5-induced pulmonary inflammation mouse model. The optimized 6:4 mixture showed the strongest antioxidant activity (RC₅₀ = 0.61 ± 0.14 μg/mL) and significantly reduced nitric oxide (NO) and cyclooxygenase-2 (COX-2) expression without cytotoxicity. HPLC confirmed the presence of astaxanthin (1.714 μg/mg) and quercetin (0.722 μg/mg). Docking simulations indicated strong COX-2 binding affinities (−9.501 and −8.753 kcal/mol) through hydrogen bonding and hydrophobic interactions. In vivo, HW extract reduced leukocyte infiltration, serum IL-6 levels, and pulmonary expression of COX-2, interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) while improving alveolar structure. These results suggest that HW extract exerts synergistic antioxidant and anti-inflammatory actions via dual-site COX-2 modulation, providing a promising natural therapeutic approach for mitigating PM_2.5-induced respiratory inflammation. Full article

Background/Objectives: Time-to-event endpoints such as Overall Survival (OS), Progression-Free Survival (PFS), and Event-Free Survival (EFS) are central in phase III oncology trials. Hazard ratios from Cox proportional hazards models and log-rank tests are the standard analytic tools, supplemented by Kaplan–Meier estimates. However, these methods depend on proportional hazards to deliver unbiased estimates of treatment effects and large-sample assumptions, and may perform poorly under heavy censoring or non-proportional hazards. We introduce the univariate martingale residual (UMR) as a new endpoint and summary measure that enables exact inference through randomization testing. Methods: The UMR reflects the difference between observed and expected events at the subject level. Average UMRs per treatment arm provide an absolute measure of excess events. A randomization-based testing framework is used to compare treatment arms and compute exact p-values without proportional hazards or asymptotic assumptions. Performance is assessed through simulations and demonstrated using real oncology trial data. Results: UMRs offered robust and interpretable treatment summaries under heavy censoring, non-proportional hazards, and quasi-complete separation, where Cox-based estimates were unstable or undefined. The exact UMR-based randomization test maintained Type I error control and was competitive or more powerful than the log-rank test when proportional hazards were violated. Conclusions: The UMR provides an intuitive, assumption-free summary of treatment effects and supports exact inference. It represents a practical and robust alternative to hazard-ratio-based methods in phase III oncology trials, especially in complex survival settings. Full article

The therapeutic use of non-steroidal anti-inflammatory drugs (NSAIDs) is limited by gastrointestinal and renal adverse effects caused by non-selective COX-1 and COX-2 inhibition. To address this issue, a new series of naproxen–azetidinone hybrids was rationally designed and synthesized to enhance COX-2 selectivity and reduce off-target toxicity. The synthesis involved esterification, hydrazide formation, Schiff base condensation, and intramolecular cyclization with chloroacetyl chloride. Structural characterization was achieved through FT-IR, ¹H NMR, and ¹³C NMR analyses. In silico ADMET profiling confirmed compliance with Lipinski’s rule and predicted favorable gastrointestinal absorption. Molecular docking revealed high COX-2 binding affinities (−11.93 to −9.72 kcal/mol), while MM/GBSA analysis identified compound N4_c (ΔG = −62.27 kcal/mol) as the most stable complex, surpassing meloxicam and naproxen. DFT (B3LYP/6-31G(d,p)) frontier molecular orbital analysis indicated a narrow HOMO–LUMO gap (ΔE = 2.97 eV) for N4_c, suggesting high electronic reactivity and strong enzyme interaction. Molecular dynamics simulations confirmed complex stability. In vivo anti-inflammatory testing using an egg-white-induced rat paw edema model showed that N4_d, N4_e, and N4_f achieved higher inhibition (19.22%, 16.98%, and 16.98%) than naproxen (4.3%). These results highlight 2-azetidinone–naproxen hybrids as promising selective COX-2 inhibitors with enhanced pharmacokinetic and electronic properties. Full article

Snack bars are convenient, ready-to-eat foods with various natural ingredients and may serve as functional foods, offering bioactive phytochemicals. In this study, tomato-based snack bars enriched in plant proteins were evaluated for their antioxidant, antidiabetic, anti-obesity, and anti-inflammatory properties by in vitro test, comparing different protein sources (pea and RuBisCO) and drying methods (microwave vacuum and oven). The rubisco bars exhibited the highest levels of polyphenols (10.12 ± 0.27 mg GAE/g) and flavonoids (5.65 ± 0.47 mg CE/g), and demonstrated superior antioxidant capacity in DPPH, ORAC, and FRAP assays, particularly when microwaved. Rubisco bars also exhibited better inhibition activity of dipeptidyl-peptidase IV and pancreatic lipase, suggesting potential antidiabetic and anti-obesity effects. In contrast, pea bars displayed notable anti-inflammatory effects by reducing tumor necrosis factor (TNF)-α-induced cyclooxygenase-2 (COX-2) expression in intestinal cells. Both protein types were digestible, though rubisco bars released more peptides during simulated gastrointestinal digestion. While these in vitro findings provide insights into the functional potential of tomato-based snack bars, further studies, including in vivo investigations, are required to confirm their health-promoting effects and to evaluate physiologically relevant doses. Overall, these findings highlight the potential of tomato-based snack bars as sustainable, nutrient-rich functional foods with potential health-promoting properties. Full article

The Cox proportional hazards (PH) model is widely used because it models the covariates to the hazard through a log-linear effect. However, exploring flexible effects becomes desirable within the Cox PH framework when only a monotonic relationship between covariates and the hazard is assumed. This work proposes a partial-likelihood-based goodness-of-fit (GOF) test to assess the log-linear effect assumption in a univariate Cox PH model. Rejection of log-linearity suggests the need to incorporate monotonic and non-log-linear covariate effects on the hazard. Our simulation studies show that the proposed GOF test controls type I error rates and exhibits consistency across various scenarios. We illustrate the proposed GOF test with two datasets, breast cancer data and lung cancer data, to assess the presence of log-linear effects in the Cox PH model. Full article

Particulate matter (PM) is a significant pollutant that induces oxidative stress, inflammation, and structural skin damage, contributing to premature aging and reduced skin integrity. In this study, PM was applied topically to human ex vivo skin tissues to simulate real-world exposure, while test compounds were delivered using the culture medium to mimic systemic absorption or applied topically for direct surface treatment. Culture medium-based treatments included indomethacin, L-ascorbic acid, and rapamycin, whereas topical treatment involved retinol and epigallocatechin gallate (EGCG). PM exposure increased hydrogen peroxide (H₂O₂), interleukin 6 (IL-6), matrix metalloproteinase 1 (MMP-1), cyclooxygenase-2 (COX-2), and prostaglandin E₂ (PGE-2), while decreasing collagen type I and hyaluronic acid (HYA). Culture medium-based treatments improved collagen and reduced MMP-1 and COX-2 expression, with L-ascorbic acid and rapamycin lowering PGE-2, and indomethacin and rapamycin restoring HYA. L-ascorbic acid uniquely reduced IL-6. Topical treatments, including retinol and EGCG, effectively reduced H₂O₂ and MMP-1 and restored collagen type I. While both agents exhibited antioxidant activity, retinol further reduced IL-6, emphasizing its anti-inflammatory role. These results highlight the complementary protective effects of systemic-like and topical treatments in mitigating PM-induced skin damage. Future research should optimize protocols and validate efficacy under real-world conditions to enhance skin protection in polluted environments. Full article

Asiatic acid, a triterpenoid compound, has been shown to have anti-inflammatory activity through the inhibition of the formation of cyclooxygenase-2 (COX-2) in vitro and in vivo. This study was conducted to determine the binding stability and the inhibitory potential of asiatic acid as an anti-inflammatory candidate. The study involved in vitro testing utilizing a colorimetric kit as well as in silico testing for the pharmacophore modeling and molecular dynamic (MD) simulation of asiatic acid against COX-2 (PDB ID: 3NT1). The MD simulations showed a stable binding of asiatic acid to COX-2 and an RMSD range of 1–1.5 Å with fluctuations at the residues of Phe41, Leu42, Ile45, Arg44, Asp367, Val550, Glu366, His246, and Gly227. The total binding energy of the asiatic acid–COX-2 complex is −7.371 kcal/mol. The anti-inflammatory activity of the asiatic acid inhibition of COX-2 was detected at IC₅₀ values of 120.17 µM. Based on pharmacophore modeling, we discovered that carboxylate and hydroxyl are the two main functional groups that act as hydrogen bond donors and acceptors interacting with the COX-2 enzyme. From the results, it is evident that asiatic acid is a potential anti-inflammatory candidate with high inhibitory activity in relation to the COX-2 enzyme. Full article

With the in-depth research on high-entropy alloys (HEAs), most of the current research uses experimental methods to verify the effects of the main elements of HEAs on the mechanical properties of the alloys. However, this is limited by the long experimental period and the influence of many external factors. The computer simulation method can not only effectively save costs and shorten the test cycle, but also help to discover new materials and broaden the field of materials. Therefore, in this paper, the physical properties (such as lattice constant, density and elastic constant) of AlCo_xCrFeNi (x = 0, 0.25, 0.5, 0.75, 1) HEAs were calculated based on the first-principles calculation method and virtual crystal approximate modeling method. It is found that AlCo_xCrFeNi HEAs have the best hardness and toughness properties, with a Co content of 0.5~0.7. The research results can provide theoretical guidance for the preparation of HEAs with optimal mechanical properties. Full article

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast. Full article

Neutropenia is the most frequent dose-limiting toxicity reported in patients with metastatic breast cancer receiving palbociclib. The objective of this study was to investigate the pharmacokinetic–pharmacodynamic (PK/PD) relationships for toxicity (i.e., absolute neutrophil count, ANC) and efficacy (i.e., progression-free survival, PFS). A semi-mechanistic PK/PD model was used to predict neutrophils’ time course using a population approach (NONMEM). Influence of demographic and clinical characteristics was evaluated. Cox proportional hazards models were developed to evaluate the influence of palbociclib PK on PFS. A two-compartment model with first-order absorption and a lag time adequately described the 255 palbociclib concentrations provided by 44 patients. The effect of the co-administration of proton-pump inhibitors in fasting conditions increased palbociclib clearance by 56%. None of the tested covariates affected the PD parameters. Model-based simulations confirmed the concentration-dependent and non-cumulative properties of palbociclib-induced neutropenia, reversible after treatment withdrawal. The ANC nadir occurred approximately at day 24 of each cycle. Cox analyses revealed a trend for better PFS with increasing palbociclib exposure in older patients. By characterizing palbociclib-induced neutropenia, this model offers support to clinicians to rationally optimize treatment management through patient-individualized strategies. Full article

Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lack reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients, respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure of less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on the Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability of segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p = 0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty, to effect ratio, only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with a log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain in its predictions for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation, without considering segmentation, uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing). Full article

Two main problems are studied in this article. The first one is the use of the extrusion process for controlled thermo-mechanical degradation of polyethylene for recycling applications. The second is the data-based modelling of such reactive extrusion processes. Polyethylenes (high density polyethylene (HDPE) and ultra-high molecular weight polyethylene (UHMWPE)) were extruded in a corotating twin-screw extruder under high temperatures (350 °C < T < 420 °C) for various process conditions (flow rate and screw rotation speed). These process conditions involved a decrease in the molecular weight due to degradation reactions. A numerical method based on the Carreau-Yasuda model was developed to predict the rheological behaviour (variation of the viscosity versus shear rate) from the in-line measurement of the die pressure. The results were successfully compared to the viscosity measured from offline measurement assuming the Cox-Merz law. Weight average molecular weights were estimated from the resulting zero-shear rate viscosity. Furthermore, the linear viscoelastic behaviours (Frequency dependence of the complex shear modulus) were also used to predict the molecular weight distributions of final products by an inverse rheological method. Size exclusion chromatography (SEC) was performed on five samples, and the resulting molecular weight distributions were compared to the values obtained with the two aforementioned techniques. The values of weight average molecular weights were similar for the three techniques. The complete molecular weight distributions obtained by inverse rheology were similar to the SEC ones for extruded HDPE samples, but some inaccuracies were observed for extruded UHMWPE samples. The Ludovic^® (SC-Consultants, Saint-Etienne, France) corotating twin-screw extrusion simulation software was used as a classical process simulation. However, as the rheo-kinetic laws of this process were unknown, the software could not predict all the flow characteristics successfully. Finally, machine learning techniques, able to operate in the low-data limit, were tested to build predicting models of the process outputs and material characteristics. Support Vector Machine Regression (SVR) and sparsed Proper Generalized Decomposition (sPGD) techniques were chosen to predict the process outputs successfully. These methods were also applied to material characteristics data, and both were found to be effective in predicting molecular weights. More precisely, the sPGD gave better results than the SVR for the zero-shear viscosity prediction. Stochastic methods were also tested on some of the data and showed promising results. Full article

Zeolitic imidazolate frameworks (ZIFs) are interesting materials for use in several aspects: energy storage material, gas sensing, and photocatalysis. The thermal stability and pyrolysis process are crucial in determining the active phase of the material. A deep understanding of the pyrolysis mechanism is in demand. Therefore, the thermodynamics and combustion process with different heating rates was examined, and the kinetic parameters were computed employing thermogravimetric tests. Based on the TG analysis of combustion, pyrolysis moves to the high-temperature region with an increase in heating rate. The decomposition process can be separated into the dehydration (300–503 K) and the pyrolysis reaction (703–1100 K). Three points of the decomposition process are performed by dynamical analysis owing to shifts of slopes, but the combustion process has only one stage. The Zeolitic imidazolate framework’s structure properties were examined using TDDFT-DFT/DMOl³ simulation techniques. Dynamical parameters, for instance, the possible mechanism, the pre-exponential factor, and the apparent activation energy are obtained through comparison using the Kissinger formula. The thermodynamics analysis of the Zn_1-xCo_x-ZIF-8 materials is an effective way to explore the temperature influence on the process of pyrolysis, which can benefit several environment purifications, photocatalyst, and recent applications. Full article

The Cox-Merz rule is an empirical relationship that is commonly used in science and industry to determine shear viscosity on the basis of an oscillatory rheometry test. However, it does not apply to all polymer melts. Rheological data are of major importance in the design and dimensioning of polymer-processing equipment. In this work, we investigated whether the Cox-Merz rule is suitable for determining the shear-rate-dependent viscosity of several commercially available high-density polyethylene (HDPE) pipe grades with various molecular masses. We compared the results of parallel-plate oscillatory shear rheometry using the Cox-Merz empirical relation with those of high-pressure capillary and extrusion rheometry. To assess the validity of these techniques, we used the shear viscosities obtained by these methods to numerically simulate the pressure drop of a pipe head and compared the results to experimental measurements. We found that, for the HDPE grades tested, the viscosity data based on capillary pressure flow of the high molecular weight HDPE describes the pressure drop inside the pipe head significantly better than do data based on parallel-plate rheometry applying the Cox-Merz rule. For the lower molecular weight HDPE, both measurement techniques are in good accordance. Hence, we conclude that, while the Cox-Merz relationship is applicable to lower-molecular HDPE grades, it does not apply to certain HDPE grades with high molecular weight. Full article

In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the challenge mentioned above, a hybrid method for predicting the reliability index and the Remaining Useful Life (RUL) of engineered systems under time-varying external conditions is proposed in this paper. The proposed method is competent in reflecting the influence of time-varying external conditions on the degradation behaviour of engineered systems. Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. During the reliability prediction, the Fourier Grey Model (FGM) is employed with the time series models for long-term forecasting of the external conditions. The results show that the method that is proposed in this paper is competent for the RUL prediction as compared with baseline approaches. Full article