Search Results (1,066)

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Capacitive sensors are platforms that enable label-free, real-time detection at low non-perturbing voltages. These sensors do not rely on Faradaic processes, thereby eliminating the need for redox-active species and simplifying system integration for point-of-care diagnostics. However, their sensitivity in high-ionic-strength solutions, such as bodily fluids, is limited due to a reduced Debye length and non-specific interactions. The present review highlights advances in material integration, surface modification, and signal enhancement techniques to mitigate the challenges of deploying capacitive sensors in biofluids (sweat, saliva, blood, serum). This work further expands on the promise of such sensors for advancing liquid biopsies and highlights key technical challenges in translating capacitive systems to clinics. Full article

Incorporation of lanthanide (Ln) and actinide (An) ions into the human body poses significant chemotoxic and radiotoxic risks, necessitating effective decorporation strategies. This study investigates the displacement of biologically relevant ligands from trivalent ions of europium, Eu(III), and curium, Cm(III), in artificial biofluids by various complexing agents, i.e., ethylenediaminetetraacetic acid (EDTA), ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), diethylenetriaminepentaacetic acid (DTPA), and spermine-based hydroxypyridonate chelator 3,4,3-LI(1,2-HOPO) (HOPO). Utilizing a modified unified bioaccessibility method (UBM) to simulate gastrointestinal conditions, we conducted concentration-dependent displacement experiments at both room and body temperatures. Time-resolved laser-induced fluorescence spectroscopy (TRLFS) supported by ²H nuclear magnetic resonance (NMR) spectroscopy and thermodynamic modelling revealed the complexation efficacy of the agents under physiological conditions. Results demonstrate that high affinity, governed by complex stability constants and ligand pK_a values, is critical to overcome cation and anion competition and leads to effective decorporation. Additionally, there is evidence that cyclic ligands are inferior to linear ligands for this application. HOPO and DTPA exhibited superior displacement efficacy, particularly in the complete gastrointestinal tract simulation. This study highlights the utility of in vitro workflows for evaluating decorporation agents and emphasizes the need for ligands with optimal binding characteristics for enhanced chelation therapies. Full article

Due to advances in molecular technologies and the expanding knowledge of biomarkers, their use in patient screening, diagnosis, prognosis, and targeted therapy is continuously increasing. Biomarker characteristics play a crucial role across all areas of medical research/practice. Biomarkers often reflect changes in the biochemical composition of biofluids, which can be qualitatively and quantitatively analyzed using methods such as high-performance liquid chromatography (HPLC) at various stages of clinical intervention. This study focuses on establishing physiological reference ranges for selected biochemical and metabolomic indicators by analyzing blood serum samples from domestic sheep. A total of sixty samples are examined using standard biochemical assays and HPLC, resulting in the determination of experimental reference values for twenty-one biochemical and eight metabolomic parameters. Reliable and reproducible preclinical testing is essential before any diagnostic method can be introduced into clinical use. A thorough understanding of the safety and efficacy of such methods in animal models is a prerequisite for initiating human trials. Species selection and the definition of physiological biomarker ranges are therefore critical components in the development of effective preclinical protocols. This work contributes to the foundation needed for further clinical testing by establishing reference values for relevant biomarkers in a commonly used animal model. Full article

This paper explores the complex dynamics of mixed convective flow in a Casson fluid saturated in a non-Darcy porous medium, focusing on the influence of gyrotactic microorganisms, internal heat generation, and multiple convective mechanisms. Casson fluids, known for their non-Newtonian behavior, are relevant in various industrial and biological contexts where traditional fluid models are insufficient. This study addresses the limitations of the standard Darcy’s law by examining non-Darcy flow, which accounts for nonlinear inertial effects in porous media. The governing equations, derived from conservation laws, are transformed into a system of no linear ordinary differential equations (ODEs) using similarity transformations. These ODEs are solved numerically using a finite differencing method that incorporates central differencing, tridiagonal matrix manipulation, and iterative procedures to ensure accuracy across various convective regimes. The reliability of this method is confirmed through validation with the MATLAB (R2024b) bvp4c scheme. The investigation analyzes the impact of key parameters (such as the Casson fluid parameter, Darcy number, Biot numbers, and heat generation) on velocity, temperature, and microorganism concentration profiles. This study reveals that the Casson fluid parameter significantly improves the velocity, concentration, and motile microorganism profiles while decreasing the temperature profile. Additionally, the Biot number is shown to considerably increase the concentration and dispersion of motile microorganisms, as well as the heat transfer rate. The findings provide valuable insights into non-Newtonian fluid behavior in porous environments, with applications in bioengineering, environmental remediation, and energy systems, such as bioreactor design and geothermal energy extraction. Full article

Novel therapeutic approaches for wound healing have included biomaterials from the Atlantic cod (Gadus morhua L.), with promising results in wound management. The use of extracellular vesicles (EVs), which can be isolated from cod biofluids, remains to be studied. EVs play key roles in cellular communication, and their use both as biomarkers and as therapeutic agents is widely reported in human pathologies, particularly with respect to mesenchymal stem cells. This pilot study characterized the total proteomic cargo content of EVs from cod serum and mucus and assessed the EVs’ potential for regenerative activity in wound-healing processes, using human and mouse fibroblast and keratinocyte in vitro scratch injury models. The pro-regenerative potential of both cod serum EVs and mucus EVs was identified, with differing capacities for accelerating wound closure in fibroblast and keratinocyte cells. This was further supported by varying effects of the cod serum EVs and mucus EVs on cellular vimentin and FGF-2 levels. The serum EV and mucus EV protein cargoes differed with respect to abundance of protein hits and associated enriched functional GO and KEGG pathways, but both were associated with immune, stress and wound-healing processes. Cod EVs may present as innovative therapeutic options for regenerative medicine applications, and our reported findings provide valuable insights for future in-depth studies. Full article

This study aimed to predict the potential habitats of Luciola unmunsana using a species distribution model (SDM). Luciola unmunsana is an endemic species that lives only in South Korea, and because its females do not have genus wings and are less fluid, it is difficult to collect, so research related to its distribution and restoration is relatively understudied. Therefore, this study predicted the potential habitats of Luciola unmunsana across South Korea using the single model Maximum Entropy (MaxEnt) and a multi-model ensemble model to prepare basic data necessary for a conservation and habitat restoration plan for the species. A total of 39 points of occurrence were built based on public data and prior research from the Jeonbuk Green Environment Support Center (JGESC), the Global Biodiversity Information Facility (GBIF), and the National Institute of Biological Resources (NIBR). Among the input variables, climate variables were based on the shared socioeconomic pathway (SSP) scenario-based ecological climate index, while nonclimate variables were based on topography, land cover maps, and the Enhanced Vegetation Index (EVI). The main findings of this study are summarized below. First, in predicting Luciola unmunsana potential habitats, the EVI, water network analysis, land cover, and annual precipitation (Bio12) were identified as good predictors in both models. Accordingly, areas with high vegetation activity in their forests, adjacent to water resources, and stable humidity were predicted as potential habitats. Second, by overlaying the predicted potential habitats and highly significant variables, we found that areas with high vegetation vigor within their forests, proximity to water systems, and relatively high annual precipitation, which can maintain stable humidity, are potential habitats for Luciola unmunsana. Third, literature surveys used to predict potential habitat sites, including Geumsan-gun, Chungcheongnam-do, Yeongam-gun, Jeollabuk-do, Mudeungsan Mountain, Gwangju-si, Korea, and Gijang-gun, Busan-si, Korea, confirmed the occurrence of Luciola unmunsana. This study is significant in that it is the first to develop a regional SDM for Luciola unmunsana, whose population is declining due to urbanization. In addition, by applying various environmental variables that reflect ecological characteristics, it contributes to more accurate predictions of the potential habitats of this species. The predicted results can be used as basic data for the future conservation of Luciola unmunsana and the establishment of habitat restoration strategies. Full article

Enhancing the thermal performance of building façades is vital for reducing energy demand in hot desert climates, where envelope heat gain increases cooling loads. This study investigates the integration of biomimicry into opaque ventilated façade (OVF) systems as a novel approach to reduce façade surface temperatures. Thirteen bio-inspired façade configurations, modeled after strategies observed in nature, were evaluated using computational fluid dynamics simulations to assess their effectiveness in increasing airflow and reducing inner skin surface temperatures. Results show that all proposed biomimetic solutions outperformed the baseline OVF in terms of thermal performance, with the wide top mound configuration achieving the greatest temperature reduction—up to 5.9 °C below the baseline OVF and 16.4 °C below an unventilated façade. The study introduces an innovative methodology that derives façade design parameters from nature and validates them through simulation. These findings highlight the potential of nature-based solutions to improve building envelope performance in extreme climates. Full article

Tear film alterations are commonly associated with ocular pathology. The tear film plays a vital role in maintaining the optical properties of the cornea and contains essential elements required for healing and preserving the integrity of the ocular surface. As a biological fluid, the tear film is easily collected using non-invasive techniques, making it a promising candidate for analysis and often referred to as an ideal biofluid. Several studies have attempted to identify biomarkers in the tear film that could be linked to systemic or ocular disorders, with the goal of developing tools for diagnosis or even early prevention. The quality and quantity of the tear film are influenced by hormonal status, emotional experiences related to social and familial events, and the work environment. Systemic disorders are often reflected at the ocular level through alterations in the tear film. Obesity is a well-recognized public health concern, extensively studied and investigated, much like other common systemic conditions. The presence of low-grade, chronic inflammation associated with excess body weight has been validated in several studies. The strategies for preventing obesity induced dry eye disease are based on regular physical activity, maintaining adequate hydration through sufficient fluid intake, weight loss, and the supplementation of essential fatty acids. This narrative literature review aims to highlight the tear film alterations associated with obesity. The article is intended for ophthalmologists, general practitioners, nutritionists, and researchers. Full article

Some additives currently used to enhance drilling mud’s rheological qualities have a substantial economic impact on society. Carboxymethyl cellulose (CMC) and calcium carbonate (CaCO₃) are currently imported. Food crops have influences on food security; hence, this research explored the potential of utilizing cow bone powder (CBP), a bio-waste product and a renewable resource, as an environmentally friendly fluid-loss additive for drilling applications, in comparison with CaCO₃. Both samples (CBP and CaCO₃) were evaluated to determine the most efficient powder sizes (coarse, medium, and fine powder), concentrations (5–15 g), and aging conditions (before or after aging) that would offer improved rheological and fluid-loss control. The results obtained showed that CBP had a significant impact on mud rheology when compared to CaCO₃. Decreasing the particle size (coarse to fine particles) and increasing the concentration from 5 to 15 g positively impacted mud rheology. Among all the conditions analyzed, fine-particle CBP with a 15 g concentration produced the best characteristics, including in the apparent viscosity (37 cP), plastic viscosity (29 cP), and yield point (25.5 lb/100 ft²), and a gel strength of 16 lb/100 ft² (10 s) and 28 lb/100 ft² (10 min). The filtration control ability of CaCO₃ was observed to be better than that of the coarse and medium CBP particle sizes; however, fine-particle-size CBP demonstrated a 6.1% and 34.6% fluid-loss reduction at 10 g and 15 g concentrations when compared to respective amounts of CaCO₃. The thermal behavior of the Mud Samples demonstrated that it positively impacted rheology before aging. In contrast, after aging, it exhibited a negative effect where samples grew more viscous and exceeded the API standard range for mud properties. Therefore, CBP’s excellent rheological and fluid-loss control ability makes it a potential, sustainable, and economically viable alternative to conventional materials. This superior performance enhances the thinning properties of drilling muds in stationary and circulating conditions. Full article

Background: Aquaporins (AQPs) are integral membrane proteins that facilitate water transport across biological membranes. While their role is well-characterized in various tissues, their function in the oral cavity remains poorly understood. Saliva is an easily accessible, non-invasive biofluid that contains stable extracellular RNA and can reflect both systemic and local physiological or pathological processes, making it a promising source for RNA analyses. This study investigates AQP mRNA levels in human saliva. Methods: Saliva samples were collected from patients of a dental practice and analyzed using quantitative PCR to detect AQP levels. An in silico analysis of AQPs in cells of the oral cavity were performed. Baseline data of the patients were recorded. Results: Our findings demonstrate the presence of multiple AQP subtypes in human saliva. AQP5 was the most abundant, followed by AQP9 and AQP1. The levels of several AQPs showed intercorrelation, whereas AQP3 appeared to be independently regulated and did not correlate with the other AQPs. Conclusions: This study demonstrates that differential AQP mRNA levels can be detected in human saliva. These findings suggest that salivary AQP mRNA may serve as surrogate markers for altered AQP levels in cells of the oral cavity. In the future, such patterns of AQP levels could potentially be used to identify or monitor pathological conditions affecting the oral mucosa or salivary glands. Further studies are required to validate this approach and to understand its diagnostic relevance. Full article

The microscale solid and pore structures of waste is crucial for the bio-hydro-mechanical behaviors of landfilled municipal solid waste (MSW). The quantitative analysis of the structural characteristics of MSW is still limited. In this study, borehole MSW samples at different depths (i.e., 0 m, 2.5 m, 5 m, 7.5 m, 10 m, and 12.5 m) were drilled from a landfill. The waste composition and basic physical properties of these samples were tested in laboratory. Solid and pore structural characteristics were studied through computed tomography (CT) analysis. The results indicate that the ratio of cellulose content to lignin content (i.e., C/L) decreased from 0.85 to 0.47 with increasing depth. For solid particles, two-dimensional (2D) particles constituted the greatest fraction (60.22~72.16%), which showed a decrease with increasing depth. The deeper sample tended to have more fine particles. For pores, the void ratio decreased from 1.68 to 1.10 with increasing depth, with more small pore channels. Meanwhile, the average pore diameter coefficient (λ) decreased from 0.209 to 0.190, the pore angle (θ_e) decreased from 29.6° to 17.8°, the tortuosity (τ) increased from 1.129 to 1.184, and the connectivity (c_e) decreased from 12.0 to 4.1. These quantitative findings can further the understanding of fluid flow behaviors in landfilled waste. Full article

It has been proven that the performance of fluid catalytic cracking (FCC), as the most important oil refining process for converting low-value heavy oils into high-value transportation fuels, light olefins, and feedstocks for petrochemicals, depends strongly on the quality of the feedstock. For this reason, characterization of feedstocks and their relationships to FCC performance are issues deserving special attention. This study systematically reviews various publications dealing with the influence of feedstock characteristics on FCC performance, with the aim of identifying the best characteristic descriptors allowing prediction of FCC feedstock cracking capability. These characteristics were obtained by mass spectrometry, SARA analysis, elemental analysis, and various empirical methods. This study also reviews published research dedicated to the catalytic cracking of biomass and waste oils, as well as blends of petroleum-derived feedstocks with sustainable oils, with the aim of searching for quantitative relationships allowing prediction of FCC performance during co-processing. Correlation analysis of the various FCC feed characteristics was carried out, and regression techniques were used to develop correlations predicting the conversion at maximum gasoline yield and that obtained under constant operating conditions. Artificial neural network (ANN) analysis and nonlinear regression techniques were applied to predict FCC conversion from feed characteristics at maximum gasoline yield, with the aim of distinguishing which technique provided the more accurate model. It was found that the correlation developed in this work based on the empirically determined aromatic carbon content according to the n-d-M method and the hydrogen content calculated via the Dhulesia correlation demonstrated highly accurate calculation of conversion at maximum gasoline yield (standard error of 1.3%) compared with that based on the gasoline precursor content determined by mass spectrometry (standard error of 1.5%). Using other data from 88 FCC feedstocks characterized by hydrogen content, saturates, aromatics, and polars contents to develop the ANN model and the nonlinear regression model, it was found that the ANN model demonstrated more accurate prediction of conversion at maximum gasoline yield, with a standard error of 1.4% versus 2.3% for the nonlinear regression model. During the co-processing of petroleum-derived feedstocks with sustainable oils, it was observed that FCC conversion and yields may obey the linear mixing rule or synergism, leading to higher yields of desirable products than those calculated according to the linear mixing rule. The exact reason for this observation has not yet been thoroughly investigated. Full article

This work presents a one-dimensional (1D) model for simulating the behavior of an FCC riser reactor processing bio-oil. The FCC riser is modeled as a plug-flow reactor, where the bio-oil feed undergoes vaporization followed by catalytic cracking reactions. The bio-oil droplets are represented using a Lagrangian framework, which accounts for their movement and evaporation within the gas-solid flow field, enabling the assessment of droplet size impact on reactor performance. The cracking reactions are modeled using a four-lumped kinetic scheme, representing the conversion of bio-oil into gasoline, kerosene, gas, and coke. The resulting set of ordinary differential equations is solved using a stiff, second- to third-order solver. The simulation results are validated against experimental data from a full-scale FCC unit, demonstrating good agreement in terms of product yields. The findings indicate that heat exchange by radiation is negligible and that the Buchanan correlation best represents the heat transfer between the droplets and the catalyst particles/gas phase. Another significant observation is that droplet size, across a wide range, does not significantly affect conversion rates due to the bio-oil’s high vaporization heat. The proposed reduced-order model provides valuable insights into optimizing FCC riser reactors for bio-oil processing while avoiding the high computational costs of 3D CFD simulations. The model can be applied across multiple applications, provided the chemical reaction mechanism is known. Compared to full models such as CFD, this approach can reduce computational costs by thousands of computing hours. Full article

In recent years, there has been a surge in the extrusion-based 3D printing of materials for various biomedical applications. This work presents a novel methodology for optimizing extrusion-based 3D bioprinting of a gelatin/siloxane hybrid material for biomedical applications. A systematic approach integrating rheological characterization, computational fluid dynamics simulation (CFD), and machine-learning-based image analysis, was employed. Rheological tests revealed a shear stress of 50 Pa, a maximum viscosity of 3 × 10⁵ Pa·s, a minimum viscosity of 0.089 Pa·s, and a shear rate of 15 rad/s (27G nozzle, 180 kPa pressure, 32 °C temperature, 30 mm/s velocity) for a BIO X bioprinter. While these parameters yielded constructs with 54.5% similarity to the CAD design, a multi-faceted optimization strategy was implemented to enhance fidelity, computational fluid dynamics simulations in SolidWorks, coupled with a custom-develop a binary classifier convolutional neuronal network for post-printing image analysis, facilitated targeted parameter refinement. Subsequent printing optimized parameters (25G nozzle, 170 kPa, 32 °C, 20 mm/s) achieved a significantly improved similarity of 92.35% CAD, demonstrating efficacy. The synergistic combination of simulation and machine learning ultimately enabled the fabrication of complex 3D constructs with a high fidelity of 94.13% CAD similarity, demonstrating the efficacy and potential of this integrated approach for advanced biofabrication. Full article