Search Results (242,836)

Background and Objectives: Systemic inflammatory biomarkers have emerged as potential prognostic indicators in metastatic colorectal cancer (mCRC). However, the prognostic robustness of inflammatory indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP), C-reactive protein-to-albumin ratio (CAR), and Glasgow Prognostic Score (GPS) remains incompletely characterized. In this study, we aimed to evaluate the prognostic significance of NLR, PLR, CRP, CAR, and GPS for progression-free survival in metastatic colorectal cancer in a cohort of patients from Romania. Materials and Methods: This retrospective observational study included 148 patients diagnosed with mCRC. Inflammatory biomarkers were determined from baseline laboratory parameters. Progression-free survival (PFS) was the primary endpoint. Statistical analyses included correlation testing, Kaplan–Meier survival analysis, Cox proportional hazards regression, Firth penalized Cox regression, restricted cubic spline modeling, time-dependent receiver operating characteristic (ROC) analysis, LASSO penalized regression, multiple imputation, and parsimonious multivariable Cox models adjusted for major clinicopathologic confounders. Results: Median PFS was 21 months (95% CI 19–24). In univariable Cox analyses, elevated NLR (HR 1.98, 95% CI 1.11–3.51, p = 0.020), PLR (HR 1.89, 95% CI 1.25–2.85, p = 0.002), CRP (HR 1.45, 95% CI 1.15–1.83, p = 0.002), and CAR (HR 1.44, 95% CI 1.05–1.98, p = 0.022) were associated with shorter PFS. Restricted cubic spline analysis demonstrated a significant nonlinear association between NLR and PFS (p = 0.0025). After multiple imputation, NLR remained associated with shorter PFS (HR 2.04, 95% CI 1.13–3.68, p = 0.018). However, in a multivariable model adjusted for major clinicopathologic confounders, this association was not retained (HR 1.41, 95% CI 0.81–2.43, p = 0.221) and time-dependent ROC analyses demonstrated its limited discriminatory performance. Conclusions: Although some inflammatory markers were associated with shorter PFS in univariable analyses, the prognostic effect of NLR was attenuated after adjustment and was not consistently confirmed across all analyses. Full article

This study presents a novel technological framework that integrates web-based 3D modeling and digital prototyping into interdisciplinary design education. Addressing the gap between traditional theoretical assessment and modern industry demands, the research investigates the implementation of interactive micro-websites and high-fidelity 3D product models as standard deliverables. Using a quasi-experimental design, the proposed digital workflow was tested on 53 final-year graphic design students at the University of Zagreb, divided into three groups based on the end users of their digital prototypes: real industry clients, peers, or academic mentors. The systemic reliability of the technological framework was measured through the technical quality of the final output (grades) analyzed via ANOVA, while user engagement with the digital process was tracked longitudinally. Results indicate that the implemented technological pipeline produced consistently high-quality outputs across all cohorts, with the client-facing group achieving the highest technical scores (M = 4.37; SD = 0.57). The lack of statistically significant variance between groups highlights a “ceiling effect,” demonstrating that the structured digital workflow itself is operationally stable and ensuring top-tier technical performance and prepress accuracy regardless of the evaluator. The study concludes that integrating advanced 3D web technologies and interactive public deliverables into the curriculum provides a scalable, industry-aligned technological model that successfully prepares design engineers for complex professional environments. Full article

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (${\gamma }_1=0.4$, ${\gamma }_2=0.2$, ${\gamma }_3=0.1$) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow. Full article

This paper develops a theoretical framework for corporate financial resilience under incomplete-market conditions, in which firm-specific equity derivatives are structurally unavailable or only weakly developed. Using the Romanian capital market and the Bucharest Stock Exchange (BSE) as a focal context rather than as the paper’s sole relevance, the study links Tobin’s q, liquidity policy, capital structure, ESG governance, and the domestic quasi-risk-free benchmark ($R_f^{ROM}$) to explain how firms may partly support financial flexibility when direct hedging instruments are missing. This is a conceptual framework paper: it does not provide empirical tests or validated firm-level results but instead formulates empirically testable propositions (P1–P4) and a future empirical research agenda. Building on selective hedging theory, Tobin’s q investment theory ESG finance and organisational resilience research, the framework identifies six assumptions of the classical model that are violated and four limitations affecting q measurement on the BSE. Within thin and illiquid markets, Tobin’s q is treated as a noisy, imperfect valuation signal rather than as a precise decision threshold. The paper contributes by delimiting the scope conditions under which classical q-based and selective-hedging assumptions weaken in derivative-constrained markets by reframing financial flexibility as a conditional resilience mechanism rather than a hedge substitute and by specifying falsifiable propositions for future empirical testing in the Romanian capital-market context. Full article

Peptic ulcer is a multifactorial and debilitating gastrointestinal disorder affecting about 10 million people worldwide. The effectiveness of many anti-ulcer drugs is often limited by adverse effects, thereby making the need for safer, natural alternatives necessary. In this study, experimental rats were orally gavaged for 14 days with 1.25, 2.5, or 5.5 mg/kg body weight of lyophilized goat weed (Ageratum conyzoides) juice extract (ACJE). Gastric ulceration was induced in fasted animals by oral administration of ethanol (96%, 5 mL/kg). Animals were subsequently euthanized to evaluate the ulcer index, stomach histology, and biochemical markers of toxicity and oxidative stress. Untreated, induced rats had significantly (p < 0.001) higher ulcer scores (9-fold) and gastric juice secretion (1.5-fold), as well as lower gastric juice pH (1.5-fold). These effects were markedly reversed by ACJE pretreatment. Rats administered 2.5 mg/kg ACJE demonstrated similar protective benefits to those of ranitidine, including significant reductions in ulcer severity, gastric juice volume, and acidity. Furthermore, ulcer-associated dyslipidemia and changes in LDL and HDL cholesterol levels were mitigated in ACJE-pretreated animals. The gastroprotective effect of ACJE was validated by histological findings and the restoration of oxidative stress in rats. In silico analysis revealed that ACJE phytochemicals bind strongly to urease and pepsin, with higher docking scores compared to standard drugs. Overall, ACJE shows promising gastroprotective potential, necessitating more mechanistic research. Full article

Lactic acid bacteria exhibit high adaptability to their environment due to their wide variety of enzymes. Despite extensive knowledge of bacterial cell walls, the subcellular localization of β-galactosidase in many probiotic lactic acid bacteria, including Limosilactobacillus fermentum, remains unclear. Determining the cellular localization of such enzymes may improve insight into bacterial metabolic mechanisms and support the development of efficient downstream processes, as well as applications. In this study, three cell disruption strategies (mechanical homogenization and chemical disruption with different agents) were applied to assess the subcellular localization of β-galactosidase from the Ll. fermentum LF08 strain. Enzyme activity was measured in a ferment broth, a supernatant and cell-associated fractions. No and very low β-galactosidase activity was detected in the ferment broth and the supernatant, respectively, when either chemical or mechanical treatment was applied, whereas the main enzyme activity was assayed in the cell suspension fraction. Combined lysozyme and CTAB treatment resulted in a 21.4-fold increase in β-galactosidase activity in the supernatant fraction (2.14 U/mL), compared with CTAB treatment alone (0.10 U/mL). Bioinformatic analyses provided additional significant information to propose the potential cell wall association (maybe the outer side of the cell wall) of the subcellular localization of β-galactosidase. This feature may support understanding of the interactions between probiotic bacteria and host tissues, as well as the development of probiotic immobilized cell systems for applications such as the elimination of lactose, designing novel functional foods. Full article

In managed pressure drilling (MPD), wellbore flow states cannot be obtained in real time, so kick intervention decisions rely on the empirical judgment of engineers, which introduces a significant lag. The central hypothesis of this study is that fusing a physics-constrained transient two-phase flow model with real-time surface measurements through data assimilation can reconstruct the unobservable downhole flow state and, on this basis, enable quantitative and earlier prediction of the safe intervention timing than empirical judgment alone. To this end, this paper proposes a method for real-time inversion of wellbore flow states and safety intervention timing prediction based on the Ensemble Kalman Filter (EnKF). Using a transient wellbore gas–liquid two-phase flow model as the EnKF model operator, the method continuously assimilates real-time casing pressure, standpipe pressure (SPP), and pit gain data. This process dynamically corrects model prediction bias while maintaining multiphase flow physical constraints. Thus, the method achieves high-precision dynamic inversion of wellbore pressure profiles and gas holdup distributions. On this basis, the authors use the inverted states as initial conditions to calculate safety casing pressure with the multiphase flow model. The method then predicts intervention timing by combining three trigger conditions: safety casing pressure, pit gain, and the density difference between the inlet and outlet. The authors validated the method using kick scenarios from Well L and Well Z in the Shunbei block. The results showed that the mean absolute errors (MAEs) for casing pressure inversion were 0.113 MPa and 0.135 MPa, respectively. The MAEs for SPP were 1.324 MPa and 0.954 MPa. The MAEs for pit gain were 0.174 m³ and 0.114 m³. The inverted spatiotemporal distribution of gas holdup reflected the entire process of gas migration and expansion in the wellbore. Prediction results for intervention timing showed that the method issued early warning signals approximately 53 min and 29 min earlier than actual field operations. This method provides a quantitative decision-making basis with safety redundancy for MPD field operations. Full article

Background: Image-based dietary assessment provides a more intuitive approach for nutritional monitoring and health management. However, in multi-category bowl-based meals, food boundary adhesion, spatial stacking, and staple-food occlusion by upper-layer dishes still affect the accuracy of volume, weight, and nutritional composition prediction. Methods: This study proposes a nutrition prediction method for bowl-based foods by integrating semantic segmentation, multi-view three-dimensional reconstruction, and occlusion compensation. The improved DBP-FDSNet was used to extract food-category masks from top-view RGB images, while detail enhancement, boundary-assisted supervision, and spatial position encoding were incorporated to improve the segmentation quality of food boundaries and adhesion regions. The visible food surface inside the bowl was reconstructed using a bowl instance model and RGB-TSDF-based multi-view fusion, and the two-dimensional semantic results were mapped into the height-field parameter domain for category-level volume integration. For partially occluded, severely occluded, or completely invisible staple foods, a layered compensation strategy was introduced to reduce staple-food volume prediction errors and the erroneous assignment of upper-layer food volume. Food weight and whole-bowl Calories, Fat, Carbohydrate, and Protein were finally predicted using food density and a nutritional composition database. Results: DBP-FDSNet achieved a meanIntersectionoverUnion ($mIoU$) of 80.51% and a BoundaryF1 Score ($bF1$) of 85.73%. At the whole-bowl level, the MeanAbsolutePercentageError ($MAPE$) values for Calories, Fat, Carbohydrate, Protein, and total food mass were 13.23%, 18.51%, 14.18%, 13.35%, and 10.85%, respectively. Conclusions: The method improves the stability of category-level volume and nutritional composition prediction in complex bowl-based meal scenarios, providing a feasible solution for image-based dietary assessment and intelligent nutrition management. Full article

Personal Protective Equipment (PPE) compliance is critical in powerline and renewable energy construction projects, yet non-adherence remains common and difficult to monitor manually. This paper presents an automated PPE detection system using an Artificial Intelligence based object detection model applied to actual site overhead drone imagery. The aim was to identify key non-compliance categories such as the non-use of hardhats, reflective vests, long pants, T-shirts and long sleeve shirts. A custom dataset was developed from aerial and ground footage and enhanced through standard annotation and augmentation techniques. Model performance was evaluated using mAP, precision, recall, and confidence-based metrics, demonstrating reliable detection across most PPE classes despite environmental and distance-related challenges. The study shows the potential of AI-assisted, drone-based monitoring to enhance safety oversight on powerline and renewable energy construction sites. It further outlines future work to improve dataset diversity, resolution, and real-time deployment. Full article

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article

Background/Objectives: Rumex obtusifolius L. (Polygonaceae) has been traditionally used to treat various disorders, including hepatic and gastrointestinal diseases. However, the phytochemical constituents of its roots and their potential protective effects against skeletal muscle atrophy remain poorly understood. This study aimed to isolate and characterize bioactive constituents from R. obtusifolius roots and evaluate their protective effects against dexamethasone (DEX)-induced muscle atrophy in C2C12 myotubes. Methods: LC–MS-guided phytochemical investigation of the ethanol extract of R. obtusifolius roots, followed by successive column chromatography and HPLC purification, resulted in the isolation of four naphthalene-type glycosides. Their structures were elucidated using 1D and 2D NMR spectroscopy, HR-ESIMS, and chemical transformation. The protective effects of compounds 1 and 4 against dexamethasone (DEX)-induced muscle atrophy were evaluated by assessing myotube morphology, myogenic and atrophy-related protein expression, and PI3K/Akt/mTOR signaling. Results: A new naphthalene malonylglucoside, nepodin-8-O-β-D-(6′-O-malonyl)-glucopyranoside (1), together with three known glycosides (2–4), was identified. Among the isolated compounds, compound 1 significantly attenuated DEX-induced muscle atrophy in a concentration-dependent manner by increasing myotube diameter and improving myotube morphology. It restored the expression of the myogenic markers MyoD and myogenin while suppressing the atrophy-related proteins MuRF1 and MAFBX. Furthermore, compound 1 reversed DEX-induced suppression of the PI3K/Akt/mTOR signaling pathway, indicating recovery of anabolic signaling. Conclusions: This study reports a new naphthalene malonylglucoside (1) from R. obtusifolius roots and demonstrates that compound 1 protects against DEX-induced skeletal muscle atrophy through restoration of myogenic differentiation and activation of the PI3K/Akt/mTOR pathway. These findings suggest that compound 1 is a promising natural lead compound for the development of therapeutics targeting muscle wasting disorders. Full article

Reliance on mineral fertilization in papaya cultivation raises sustainability concerns and drives demand for validated organic alternatives. This study tested whether integrating humic substances (HS) and sugarcane filter cake (FC) would stimulate photosynthetic physiology, upregulate carbon metabolism gene expression, and increase fruit yield in ‘Golden’ papaya while outperforming conventional NPK fertilization. A 12-month field experiment was conducted in a randomized complete block design with a factorial arrangement of four HS doses (0, 90, 180, and 270 mL plant⁻¹) combined with two FC doses (0 and 60 kg plant⁻¹) plus an NPK control, measuring photosynthetic pigments, gas exchange, relative expression of rbcL, ACC oxidase, invertase, relative growth rate, and fruit yield. Combined HS and FC increased chlorophyll a by up to 205%, chlorophyll b by 277%, and carotenoids by 208% relative to unamended controls. Gene expression was strongly induced: rbcL reached 202-fold, invertase 156-fold, and ACC oxidase 84.8-fold above control values. Photosynthetic rate followed a quadratic dose-response peaking near 90 mL plant⁻¹ HS. Fruit yield nearly doubled under the optimal combined treatment (115 t ha⁻¹) compared with unamended controls (62 t ha⁻¹) and NPK fertilization (66 t ha⁻¹). These results confirm that HS and FC act synergistically as dual-purpose amendments, improving soil fertility while biostimulating papaya physiology through coordinated upregulation of photosynthetic capacity and carbon partitioning toward reproductive sinks. Full article

Internalized weight bias—the self-directed endorsement of weight-related stereotypes—has emerged as a psychologically potent correlate of health outcomes in individuals with overweight and obesity, yet its relationship with sleep remains largely unexplored. In a cross-sectional manner, 495 Greek adults with a history of overweight/obesity were assessed regarding sleep quality and duration, internalized weight bias (Modified Weight Bias Internalization Scale; WBIS-M), and expressed anti-fat attitudes (Anti-Fat Attitudes Questionnaire, AFA: Dislike, Fear of Fat, Willpower). Insomnia prevalence, assessed with the Athens Insomnia Scale (AIS), was high at 57.6%—nearly doubling across ascending WBIS-M tertiles (39.9% to 73.1%). In hierarchical linear regression models, AIS score remained independently associated with WBIS-M after adjustment for depression, anxiety, BMI, and a comprehensive range of sociodemographic and clinical covariates (B = 0.058; 95% CI: 0.036–0.079; p < 0.001), with the fully adjusted model explaining 58.5% of total variance in WBIS-M. AFA subscales did not remain significant in the model post-full adjustment, and sleep duration failed to show independent association with either bias dimensions. The sleep–weight bias association was therefore specific to the internalized dimension and to sleep quality, rather than quantity. These findings highlight a clinically relevant and dimension-specific link between insomnia symptoms and internalized weight stigma, and suggest that routine sleep assessment may be warranted in individuals with a history of overweight or obesity presenting with elevated internalized weight bias—and vice versa. Full article

Pseudomonas aeruginosa is an opportunistic pathogen causing healthcare-associated infections. Colistin is a last-resort antibiotic for multidrug-resistant Gram-negative bacteria. Resistance arises through mutations in two-component systems (TCS) regulating the arn operon. Data on colistin resistance in P. aeruginosa from Pakistan remain limited. A total of 3189 clinical samples (urine, blood, sputum, pus, wound swabs) were cultured. P. aeruginosa was identified by Gram staining, biochemical tests (catalase, oxidase, API 20E), and oprL gene amplification. Antibiotic susceptibility was determined by disk diffusion and MIC strips. Resistance genes (PhoP, PhoQ, PmrA, PmrB, mcr-1, oprD) were detected by PCR and Sanger sequencing. Wild-type protein structures were retrieved from PDB; mutant structures were predicted using AlphaFold3. ANP (phosphoaminophosphonic acid-adenylate ester) was docked using MOE 2019.0102. Of 3189 samples, 384 (12.0%) yielded P. aeruginosa. Wound/pus (38.0%) and surgical wards (30.0%) were the predominant sources. Colistin and polymyxin B showed 99.0% susceptibility (MIC₅₀/MIC₉₀ = 1 µg/mL). High resistance was observed for Piperacillin–Tazobactam (96.4%), Aztreonam (70.6%), and Gentamicin (64.2%). oprD was the most prevalent gene (87.5%), followed by PmrB (54.0%), PhoQ (44.0%), PhoP (36.0%), PmrA (18.0%), and mcr-1 (8.0%). Docking revealed the strongest binding in wild-type PhoQ (1ID0; −12.0 kcal/mol, LYS392), wild-type PmrB (2JSO; −9.8 kcal/mol, ASP37), and wild-type PhoP (2PKX; −9.1 kcal/mol, LYS87/ARG111). Mutant proteins showed reduced binding affinities and dispersed interaction networks. Mutant PhoP formed 16 contacts (strongest −4.3 kcal/mol) versus wild-type PhoP with 13 contacts (−9.1 kcal/mol). Colistin remains highly effective against P. aeruginosa in this setting (99.0% susceptibility). The presence of mcr-1 (8.0%) and high oprD prevalence (87.5%) require continued surveillance. Mutations in TCS proteins reduce ANP binding affinity and alter interaction specificity, suggesting that ATP-competitive inhibitors targeting these kinases merit further investigation and experimental validation. Full article