Search Results (99)

Extracellular vesicles (EVs) from ovarian cancer cells are valuable sources for candidate biomarker studies, but conventional static flask culture yields limited material and is difficult to scale reproducibly. We evaluated a serum-free CELLine AD 1000 bioreactor workflow for producing EVs from four ovarian cancer-related (OC-related) cell lines (OVCAR4, CaOV3, PA1, SW626) and human dermal fibroblasts (HDFa) as a non-cancer control. Cells were adapted to CDM-HD serum-free medium and maintained for eight weeks with twice-weekly conditioned-medium collection. EVs were isolated by differential ultracentrifugation followed by size-exclusion chromatography and characterized by nanoparticle tracking analysis, imaging flow cytometry, Western blotting, and transmission and scanning electron microscopy. Across longitudinal harvests, OC-related cultures generally produced higher EV particle concentrations and A280-based bulk protein estimates than HDFa, while individual cell lines showed distinct production profiles and membrane-associated growth patterns. A parallel OVCAR4 T-175 flask, maintained in its original serum-containing medium, provided a contextual reference indicating higher per-collection EV particle recovery with the bioreactor, although this was not a matched culture-format comparison. EV-enriched preparations contained vesicle-like particles, with modal diameters of approximately 96–128 nm. Using imaging flow cytometry, the CD9 signal was higher in OC-related EVs and CD63 was most prominent in HDFa; CD9 and CD63 were also detected in OC-related EV lysates by Western blotting. Because one bioreactor was operated per cell line, these findings should be interpreted as preliminary and descriptive rather than statistically comparative. Overall, this study provides a practical serum-free CELLine AD 1000 workflow for generating characterized OC-related EV material for downstream analytical studies. Full article

Ribbed Smoked Sheet (RSS) rubber production in southern Thailand relies extensively on rubber-wood combustion, potentially generating substantial emissions of fine particulate matter and polycyclic aromatic hydrocarbons (PAHs). Despite the economic importance of this sector, systematic characterization of biomass-derived PAHs under tropical monsoonal and coastal conditions remains limited. This study investigates the particle size distribution, concentration levels, seasonal variability, and atmospheric dynamics of PAHs associated with RSS production in Chumphon Province, Thailand. Size-segregated particulate matter was collected using an eight-stage Andersen cascade impactor at two contrasting sites during a seven-month monitoring period from March to September 2025, covering the transition from the late dry/summer season to the rainy season in southern Thailand. Fifteen priority PAHs were quantified by high-performance liquid chromatography with fluorescence detection. Results indicate that emissions from rubber-wood combustion inside smokehouses exhibit a unimodal distribution dominated by submicron particles (MMAD = 0.85 µm; GSD = 2.71). Ambient aerosols displayed a bimodal structure, with an accumulation-mode peak (~0.6 µm) associated with combustion processes and a coarse-mode peak (~4 µm) linked to mechanical and marine aerosol sources. Particle-bound PAHs were predominantly associated with fine particles (~0.59 µm), although seasonal hygroscopic growth under high humidity shifted modal diameters toward ~1.8 µm during the rainy season. PAH profiles were dominated by 3–4 ring compounds characteristic of biomass combustion. Total PAH concentrations exhibited a strong positive correlation with monthly RSS production, while precipitation demonstrated an exponential scavenging effect. Monsoonal circulation and sea spray aerosol (SSA) interactions were identified as key regulators of gas–particle partitioning, transport pathways, and removal efficiency. The findings reveal that the coastal atmosphere of southern Thailand functions as a dynamic multiphase system in which emission intensity, hygroscopic particle growth, monsoonal transport, and wet deposition collectively govern PAH behavior. This study provides the first integrated assessment linking rubber-sheet production dynamics to size-resolved PAH distributions under tropical coastal conditions and offers a scientific foundation for emission mitigation strategies in biomass-dependent agro-industrial regions. Full article

Background: Local recurrence of rectal cancer is a challenging problem for patients and clinicians. Surgical resection is associated with good outcomes if R0 margins are achieved; however, it is often complex, requires suitable patient fitness, and is associated with long term physical and psychological consequences. Meanwhile, continuing technical advances in radiotherapy have enabled the delivery of highly conformal treatment, thereby enabling dose escalation or pelvic reirradiation to be safely considered—either as definitive management or in the neoadjuvant setting—for patients with locally recurrent rectal cancer. Pelvic reirradiation may refer to patients who have received primary rectal radiotherapy with the aim of neoadjuvant downstaging or reducing the risk of locoregional recurrence, versus radiotherapy for a previous unrelated non-rectal pelvic malignancy. Methods: A literature search of pelvic reirradiation for non-metastatic, locally recurrent rectal cancer was conducted for full text articles published over the last 20 years. Additional papers were identified within the references of these papers. Studies focusing on non-rectal cancers, and patients having primary radiotherapy for locally recurrent rectal cancer were excluded. Due to the heterogenicity of the data, no meta-analysis was performed. Results: A total of 15 papers were included, containing a cohort of 840 patients. Several reirradiation modalities were reported, including external beam radiotherapy, brachytherapy, stereotactic ablative radiotherapy and heavy particle therapy (carbon ion). Carbon ion radiotherapy was the most common reirradiation treatment modality utilised with a median cumulative dose of 70.4 Gray (Gy). Treatment response, defined as either complete or partial improvement in tumour size, was only reported in seven studies, and varied from 14 to 88%. Overall 3-year survival was also variable with rates reported between 18 and 85%. These observations may be due to variation in patient selection, treatment intent, and technique. Pelvic reirradiation was associated with acceptable toxicity, low rates of G3+ toxicity, and improved symptom control. Conclusions: Our review describes the multitude of approaches to pelvic reirradiation for locally recurrent rectal cancer. Reviewing the radiobiological and patient outcomes is challenging in view of the degree of heterogeneity in patient selection, treatment approach, and reported outcomes. However, there is consensus that pelvic reirradiation—either for long term control or to downstage prior to definitive surgery—is feasible with potential utility in this setting. Full article

Background: Magnetic Particle Imaging (MPI) is an emerging biomedical imaging modality that detects superparamagnetic iron oxide nanoparticles (SPIONs), providing high contrast, sensitivity, and quantification capabilities without ionizing radiation, making it particularly suitable for cancer diagnostics. Considerable engineering efforts are underway to translate MPI technology to clinical settings. Most of these MPI scanners feature a cylindrical bore geometry similar to that of other clinical imaging modalities, which limits their potential application primarily to head scanning. Methods: We have developed a single-sided MPI scanner designed to expand the modality’s applicability to other regions of the human body through a unique hardware design developed in our previous work. Imaging experiments were performed on an anatomical breast phantom containing implanted SPION point sources placed at anatomically plausible locations for breast tumors. These point sources served as artificial tumors for evaluating the system’s suitability for breast imaging applications. Results: The scanner successfully detected and clearly resolved the implanted SPION tumors in two orthogonal imaging planes. Tumor positioning was independently validated by ultrasound imaging, confirming MPI’s accurate localization. In addition, sensitivity measurements demonstrated a detection limit of 4.0 $\mathsf{\mu }\mathrm{g}$ of iron, below the estimated 4.8 $\mathsf{\mu }\mathrm{g}$ sensitivity threshold required for breast tumor detection with electronic depth scanning up to 3.5 cm deep. Conclusions: Together, these results demonstrate the capability of a single-sided MPI geometry for breast imaging applications. Imaging an anatomical breast-shaped volume presents significant challenges for MPI due to the size and accessibility constraints of conventional hardware. The results presented highlight the advantages of this approach and support its potential to extend MPI from small-animal imaging to clinically relevant applications. Full article

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making. Full article

Microbial imbalance and the spread of pathogenic microorganisms pose severe threats to human health and ecological security. Traditional microbial detection methods suffer from several drawbacks such as long detection time, low sensitivity, and insufficient specificity. As an emerging fluorescent probe, carbon dots (CDs) offer an innovative direction for microbial labeling and detection due to their ultra-small particle size, unique optical properties, excellent biocompatibility, and facile surface modifiability. Herein, this article reviews the research progress of CDs on microbial labeling and detection. The content covers a brief introduction of CDs and explores the main recognition strategies including non-covalent interactions and biomolecule-mediated targeted binding. It also elaborates on the application status of multi-modal sensing technologies for microbial detection, such as CDs-based fluorescent sensing, electrochemical sensing, and surface-enhanced Raman scattering (SERS) sensing. Additionally, the challenges faced in current research, such as achieving simultaneous detection of multiple pathogens and in vivo dynamic tracking, are analyzed, and the development prospects of CDs in fields like clinical diagnosis and public health monitoring are prospected. This review aims to provide comprehensive references for further research and application of CDs in the field of microbial detection. Full article

Iron powder represents a promising carbon-free, sustainable fuel, yet its practical utilisation in combustion has not yet been realised. Achieving stable, efficient iron-only flames is challenging, and the environmental impact of hybrid iron-hydrocarbon combustion, including particle emissions, is not fully understood. This study investigates hybrid methane–iron powder flames to assess iron’s role in modifying gas and particle phase emissions and its potential as a sustainable energy carrier. The combustion of iron was investigated at both the single particle and powder flow scales. Experimental diagnostics combined high-speed and microscopic imaging, ex situ particle sizing, in situ gas analysis, and aerosol measurements using an Aerodynamic Particle Sizer (APS™) and a Scanning Mobility Particle Sizer (SMPS™). For single particle combustion, high-speed imaging revealed rapid particle heating, oxide shell growth, cavity formation, micro-explosions, and nanoparticle release. For powder combustion, at 0.5 g/min and 1.26 g/min, the experiment yielded oxidation fractions of 15.15% and 23.43%, respectively, and increased CO₂ emissions by 0.22–0.35 vol% relative to methane–air flames, while NOx changes were negligible. Aerosol analysis showed a supermicron mode at ~2 µm and submicron ultrafine particles of 89% <100 nm with a modal diameter of ~56 nm. The observed ultrafine particle emissions highlight the need to evaluate health, material-loss, and fuel-recycling implications. Burner optimisation or premixed strategies could reduce CO₂ emissions while enhancing iron oxidation efficiency. Full article

Composite structures are generally more susceptible to impact damage than non-composite structures, and early identification of damage is the primary goal of structural health monitoring (SHM). If such damage remains undetected or reaches a critical size, it can lead to sudden collapse and catastrophic failure. Modern SHM methods aim to preserve the integrity of composite structures through continuous inspection, monitoring, and damage assessment, including detection, localization, quantification, classification, and prognosis. These methods use sensor-based technologies to assess vibration, extension, and acoustic and thermal emission. This paper provides a review of various computational methods including physics-based methods (signal processing techniques, modal analysis, and finite element model updating) and optimization methods (inverse problems, particle swarm optimization, topology optimization, genetic algorithms, time series analysis, and hybrid techniques), alongside machine learning methodologies employing neural networks as well as deep learning for damage identification in composite structures. These computational and learning-based techniques are widely applied in the development of algorithms, optimization strategies, and hybrid frameworks for SHM. The review further summarizes the applications, advantages, and limitations of each method according to structure type and damage characteristics. The key emphasis of this review is on integrating computational approaches, as well as machine learning, to enhance the efficiency of damage identification. The conclusion is drawn based on an overview of the literature, focusing on the contributions of different computational methods and machine learning for damage identification in composites. Full article

Cancer continues to pose a major global health burden, with conventional therapeutic modalities such as surgical resection, chemotherapy, radiotherapy, and immunotherapy often hindered by limited tumor specificity, substantial systemic toxicity, and the emergence of multidrug resistance. The rapid advancement of nanotechnology has introduced functionalized nanomaterials as innovative tools in the realm of precision oncology. These nanoplatforms possess desirable physicochemical properties, including tunable particle size, favorable biocompatibility, and programmable surface chemistry, which collectively enable enhanced tumor targeting and reduced off-target effects. This review systematically examines recent developments in the application of nanomaterials for cancer therapy, with a focus on several representative nanocarrier systems. These include lipid-based formulations, synthetic polymeric nanoparticles, inorganic nanostructures composed of metallic or non-metallic elements, and carbon-based nanomaterials. In addition, the article outlines key strategies for functionalization, such as ligand-mediated targeting, stimulus-responsive drug release mechanisms, and biomimetic surface engineering to improve in vivo stability and immune evasion. These multifunctional nanocarriers have demonstrated significant potential across a range of therapeutic applications, including targeted drug delivery, photothermal therapy, photodynamic therapy, and cancer immunotherapy. When integrated into combinatorial treatment regimens, they have exhibited synergistic therapeutic effects, contributing to improved efficacy by overcoming tumor heterogeneity and resistance mechanisms. A growing body of preclinical evidence supports their ability to suppress tumor progression, minimize systemic toxicity, and enhance antitumor immune responses. This review further explores the design principles of multifunctional nanoplatforms and their comprehensive application in combination therapies, highlighting their preclinical efficacy. In addition, it critically examines major challenges impeding the clinical translation of nanomedicine. By identifying these obstacles, the review provides a valuable roadmap to guide future research and development. Overall, this work serves as an important reference for researchers, clinicians, and regulatory bodies aiming to advance the safe, effective, and personalized application of nanotechnology in cancer treatment. Full article

Extracellular vesicles are critical mediators of intercellular signaling. Recent studies have revealed that, in addition to vesicular structures, smaller non-vesicular nanoparticles—termed exomeres and supermeres—also participate in intercellular communication. Detailed characterization of these nanoscale entities within biological systems is essential for elucidating their structural and functional roles. Due to their sub-50 nm dimensions, high-resolution imaging modalities such as atomic force microscopy and electron microscopy are currently the primary techniques available for their visualization. In the present study, we employed low-voltage scanning electron microscopy to investigate the size of exomeres and supermeres isolated from human blood plasma via high-speed ultracentrifugation. Platelet-poor plasma was obtained from the blood of six healthy donors (two women and four men, aged 21–46 years). By ultracentrifugation (170,000× g for 4 h), the plasma was purified of extracellular vesicles. Two fractions were sequentially isolated: one containing exomeres (170,000× g for 20 h) and one containing supermeres (370,000× g for 20 h). The particles were examined using a Zeiss Auriga microscope with no sputter coating at an accelerating voltage of 0.4–0.5 kV. The images obtained from the fractions showed particles 10–50 nm in diameter, both individual particles and aggregated structures. The fractions were also slightly contaminated with larger particles, supposedly extracellular vesicles. Examining the fractions using a dynamic light scattering device additionally revealed the presence of particles 10–18 nm in size. It should be noted that the fractions obtained did indeed contain particles measuring 10–50 nm, which corresponds to the size of exomeres and supermeres. Low-voltage scanning electron microscopy allows for examination of the structure of exomeres and supermeres in blood plasma fractions. However, it should be noted that without the use of immunological identification, this method does not allow exomeres and supermeres to be distinguished from accompanying particles. It should also be noted that because the size of exomeres and supermeres is close to the detection threshold of low-voltage scanning electron microscopy, in such studies it is generally only possible to detect the size of these particles. Full article

As a key component of aero transmission systems, internal splines suffer from problems of low efficiency and poor precision in traditional lapping processes due to geometric deformation and high hardness after heat treatment. To address this, this study proposes an ultrasonic vibration lapping technology, which combines the synergistic mechanism of high-frequency vibration and free abrasive particles to achieve efficient and precise machining of small-sized hardened internal splines. By establishing an abrasive grain impact trajectory model and a rolling abrasive grain material removal model, the mechanisms of micro-cutting and impact removal of abrasive particles under ultrasonic vibration are revealed. Based on the local resonance theory, a longitudinal ultrasonic vibration system is designed, and its resonant frequency is optimized through finite element modal analysis. An ultrasonic lapping experimental platform is built, and heat-treated 9310 internal spline samples are used for experimental verification. The results show that, compared with traditional manual lapping, ultrasonic vibration lapping significantly improves the tooth profile and tooth lead deviations. After measurement, following ultrasonic vibration lapping, both the total tooth profile deviation and tooth lead deviation of the internal spline meet the Grade 6 accuracy requirements specified in GB/T 3478.1-2008 Cylindrical straight-tooth involute splines (Metric Module, Tooth Side Fit)—Part 1: General. This study confirms that ultrasonic vibration lapping can effectively correct the geometric accuracy of tooth surfaces and suppress thermal damage, and provides an innovative solution for the high-quality repair of aero transmission components. Full article

Due to the rapid advancement of technology, lithium carbonate has become a crucial raw material for battery storage applications. Brines remain the primary source, while lithium carbonate production from ores is limited. Therefore, expanding resources, identifying potential deposits, and characterizing existing sources are essential. Direct lithium detection via MLA is challenging due to its atomic number being below 6; however, it can be indirectly identified through lithium-bearing biotite. This study characterizes lithium-bearing biotite in nepheline syenite ore, considering biotite as the primary lithium source. Analytical methods included MLA, modal mineralogy, XRD, ICP-OES, XRF, SEM-BSE, and EDS. The ore contained 4% biotite, with a liberation degree exceeding 70% in particles finer than 500 µm. Biotite formed binary, ternary, and complex associations with K-feldspar, nepheline, and albite. Finer particle sizes increased biotite liberation while reducing associations; no binary biotite–nepheline associations were detected below 75 µm. EDS spectra confirmed biotite as the sole lithium-bearing mineral. Full article

Starch particles (SPs) were extracted from underutilized wild yam (Dioscorea remotiflora) tubers using two methods: (1) acid hydrolysis (AH) alone and (2) acid hydrolysis assisted by ultrasound (AH-US). The SPs were chemically modified through esterification (using acetic anhydride [AA] and lauroyl chloride [LC]) and crosslinking (with citric acid [CA] and sodium hexametaphosphate [SHMP]). They were subsequently characterized by their yield, amylose content, and structural and physical properties. The yield of particles was 17.5–19.7%, and the residual amylose content was 2.8–3.2%. Particle sizes ranged from 0.46 to 0.55 µm, which exhibited mono-modal and bi-modal distributions for AH and AH-US treatments, respectively. Following chemical modification, yield notably increased, especially with substitution by LC (33.6–36.5%) and CA (32.6–38.7%). Modified SPs exhibited bi-modal particle distributions with micro- and nanoparticles and variable peak intensities depending on the chemical compound used. Unmodified SPs displayed irregular morphologies, showing disruptions (AH) or aggregation (AH-US). Chemical substitutions altered morphologies, leading to amorphous surfaces (CA: AH), clustering (LC), or fragmentation into smaller particles (SHMP) under AH-US treatment. FT-IR analysis indicated a decrease in hydroxyl groups’ peak area (A(-OH)), confirming the substitution of these groups in the starch structure. Crosslinking with CA resulted in the highest degree of substitution (AH: 0.43; AH-US: 0.44) and melting enthalpy (ΔH_f: 343.0 J/g for AH-US), revealing stronger interactions between SPs from both methods. These findings demonstrate that the extraction treatment of D. remotiflora SPs and the type of chemical modifier significantly influence the properties of SPs, underscoring their potential applications as natural biocarriers. Full article

Lubricant condition analysis is a valuable diagnostic tool for assessing engine performance and ensuring the reliable operation of diesel engines. While traditional diagnostic techniques—such as Fourier transform infrared spectroscopy (FTIR)—are constrained by slow response times, high costs, and the need for specialized personnel. In contrast, dielectric spectroscopy, impedance analysis, and soft computing offer real-time, non-destructive, and cost-effective alternatives. This review examines recent advances in integrating these techniques to predict lubricant properties, evaluate wear conditions, and optimize maintenance scheduling. In particular, dielectric and impedance spectroscopies offer insights into electrical properties linked to oil degradation, such as changes in viscosity and the presence of wear particles. When combined with soft computing algorithms, these methods enhance data analysis, reduce reliance on expert interpretation, and improve predictive accuracy. The review also addresses challenges—including complex data interpretation, limited sample sizes, and the necessity for robust models to manage variability in real-world operations. Future research directions emphasize miniaturization, expanding the range of detectable contaminants, and incorporating multi-modal artificial intelligence to further bolster system robustness. Collectively, these innovations signal a shift from reactive to predictive maintenance strategies, with the potential to reduce costs, minimize downtime, and enhance overall engine reliability. This comprehensive review provides valuable insights for researchers, engineers, and maintenance professionals dedicated to advancing diesel engine lubricant monitoring. Full article

Bone is a preferred site for disseminated tumor cells, yet the molecular mechanisms that prepare the skeletal microenvironment for metastatic colonization are only beginning to be understood. At the heart of this process are extracellular vesicles (EVs), nano-sized, lipid-encapsulated particles secreted by cancer cells and stromal components. This review consolidates current findings that position EVs as key architects of the bone-metastatic niche. We detail the biogenesis of EVs and their organotropic distribution, focusing on how integrin patterns and bone-specific ligands guide vesicle homing to mineralized tissues. We then outline the sequential establishment of the pre-metastatic niche, driven by EV-mediated processes including fibronectin deposition, stromal cell reprogramming, angiogenesis, neurogenesis, metabolic reconfiguration, and immune modulation, specifically, the expansion of myeloid-derived suppressor cells and impaired lymphocyte function. Within the bone microenvironment, tumor-derived EVs carrying microRNAs and proteins shift the balance toward osteoclastogenesis, inhibit osteoblast differentiation, and disrupt osteocyte signaling. These alterations promote osteolytic destruction or aberrant bone formation depending on tumor type. We also highlight cutting-edge imaging modalities and single-EV omics technologies that resolve EV heterogeneity and identify potential biomarkers detectable in plasma and urine. Finally, we explore therapeutic approaches targeting EVs, such as inhibition of nSMase2 or Rab27A, extracorporeal EV clearance, and delivery of engineered, bone-targeted vesicles, while addressing translational challenges and regulatory considerations. This review offers a roadmap for leveraging EV biology in predicting, preventing, and treating skeletal metastases by integrating advances across basic biology, bioengineering, and translational science. Full article