Industrial anomaly detection is an important research topic in the field of computer vision. Although widely studied, anomaly detection methods based on supervised learning have long faced challenges due to the scarcity of anomaly samples. To overcome this limitation, recent efforts have shifted toward reconstruction-based methods, which typically operate by first generating pseudo-anomalies and then reconstructing them. However, the pseudo-anomalies generated by current methods lack the requisite similarity and localization, and the reconstruction networks struggle to balance image fidelity with the accurate reconstruction of anomalous regions. To tackle these issues, this paper proposes an unsupervised anomaly detection framework called RDEAD. The core components of RDEAD are an Edge-based pseudo-anomaly generation strategy (EPA) and a distillation-based dual-encoder reconstruction network (YNet). EPA accurately generates pseudo-anomalies on the target object that closely resemble real anomalies in shape. YNet employs an encoder that has been distilled to provide the decoder with discrepancy features of anomalies, which are then used to reconstruct the anomalous regions accurately. Additionally, YNet incorporates an encoder feature fusion module (EFFM) to effectively integrate the features from dual encoders, enhancing detection performance. Experimental results on several widely used industrial datasets fully demonstrate the effectiveness of the proposed RDEAD method.
The release behavior of diltiazem-loaded microcapsules prepared from gelatin B and gum arabic was evaluated experimentally and mechanistically in distilled water and simulated saliva. Release amounts were quantified by liquid chromatography over 5-720 min and used directly as input for kinetic and mechanistic modeling. Kinetic analysis was performed using different models, and model performances were compared using the Akaike and Bayesian information criteria (AIC, BIC). The Weibull model provided the best fit in both media (R2 = 0.989 for distilled water, R2 = 0.994 for simulated saliva). A two-compartment mechanistic model developed in SimBiology/MATLAB yielded release rate constants of 0.0111 min-1 (water) and 0.0026 min-1 (saliva). Agreement between the Weibull mechanistic model and experimental results was R2 = 0.989 for distilled water (RMSE = 0.524 mg) and R2 = 0.994 for simulated saliva (RMSE = 0.348 mg). Model validation was performed using Visual Predictive Check (VPC) with N = 200 virtual individuals, confirming that all observed data points fell within the 5%-95% percentile prediction interval. The validated model was further extended to a sublingual pH variability scenario (pH 6.2-7.4), demonstrating that saliva pH influences cumulative release through its effect on the unionized fraction of diltiazem.
Introduction: Oil pulling therapy has gained increasing attention as a natural oral hygiene practice; however, evidence regarding its clinical effectiveness remains limited and inconclusive. This study aimed to evaluate the effects of oil pulling therapy on dental plaque regrowth and tooth discoloration compared with chlorhexidine. Materials and Methods: One hundred systemically healthy dental students were randomly allocated to five groups: chlorhexidine, coconut oil, black cumin seed oil, terebinth oil, and distilled water. Following professional prophylaxis, participants refrained from mechanical oral hygiene for four days and used their assigned intervention twice daily. Plaque accumulation was assessed using the Turesky modification of the Quigley-Hein Plaque Index, gingival inflammation using the Gingival Index, and tooth color using CIELAB color difference measurements. Data were analyzed using one-way ANOVA or Kruskal-Wallis test with appropriate post hoc tests, depending on the distribution of data. Results: Plaque scores differed significantly among groups (p < 0.001), with chlorhexidine showing superior plaque inhibition compared with all oil-based interventions and distilled water. Gingival index values were lowest in the chlorhexidine group, although differences among oil groups were not statistically significant. Tooth discoloration was significantly greater with chlorhexidine than with all oil-based interventions (p < 0.001). Conclusions: Oil pulling therapies demonstrated lower anti-plaque efficacy than chlorhexidine but resulted in less tooth discoloration. These findings suggest that oil pulling may serve as an adjunct rather than an alternative to conventional plaque control.
The aim of this study was to evaluate the surface roughness and optical characteristics of three ceramics with resin matrix for computer-aided design and computer-aided manufacturing, after different surface treatment protocols associated or not with the application of film deposition by plasma-enhanced chemical vapor deposition (PECVD) after different in vitro aging procedures. A total of 720 specimens were prepared and divided into groups: mechanical polishing (MP), sealant (S), MP + PECVD, and S + PECVD. The in vitro aging procedures were thermocycling, erosive challenge, and immersion in dye solution (I): distilled water (IW), black tea (IBT), and red wine (IRW). The response variables were surface roughness (Ra), color change (ΔE00), translucency parameter (TP), and contrast ratio (CR). For multiple comparisons, the three-way analysis of variance test was used, followed by Tukey's post hoc test (α = 0.05). Aging increased roughness in all ceramics, whereas the surface sealant reduced Ra more effectively than MP, even with PECVD. The red wine solution showed the highest staining potential, followed by black tea and distilled water (p > 0.05). In the IRW, there was a significant reduction (>50%) in the ΔE00 values in both surface treatments associated with PECVD for all ceramics. Aging caused changes in the TP and CR values of the ceramics (more opaque). Film deposition by PECVD was beneficial in reducing color change when immersed in black tea and red wine.
The purpose of this study was to evaluate the effect of immersion in butyric acid (BA, pH 4.1) or phosphate-buffered saline (PBS, pH 7.0) on the properties of Clinker (CL) with particle sizes of 2 to 30 µm or < 2 µm associated with zirconium oxide and manipulated with distilled water (DW) or liquid with additives (LA) compared with Bio-C Repair (BCR) and Biodentine (BIO). Dentin tubes were prepared and filled with materials. After 24 hours, the specimens were immersed in BA or PBS (n = 5) for 7 and 28 days. Micro-computed tomography was used to evaluate volumetric change, porosity, and material/dentin interface. Surface analysis was performed by scanning electron microscopy (SEM). Statistical analyses included Kruskal-Wallis and Dunn, Mann-Whitney, Wilcoxon, unpaired t-test, paired t-test, and ANOVA and Tukey tests (α = 0.05). All groups exhibited volumetric changes similar to BCR and BIO (p > 0.05). BA significantly increased porosity (approximately 8%) compared with PBS (approximately 2%), except for CL 2 to 30 µm with LA (p < 0.05). After 28 days in BA, all groups showed increased porosity and gaps at the interface compared with baseline values (p < 0.05). CL 2 to 30 µm with DW showed greater porosity and interface gaps than the other groups (p < 0.05). SEM analysis revealed that all groups showed hydroxyapatite formation on the material surface in PBS, and structural loss in BA. Findings: Acidic pH damages the material/dentin interface, increases porosity, and promotes dimensional changes in calcium silicate cements. Distilled water without additives increases the Clinker's porosity, interface gaps, and volume loss.
Restricting nutrients in eggs may hinder the growth of commercial chickens, which could result in higher embryonic mortality and poor growth performance, indicating the significance of in ovo injection of dietary nutrients to support embryonic and post-hatch growth in poultry. This study investigated the in ovo supplementation of a dietary supplement on egg weight, embryonic development, hatching, and chick quality traits of broiler chickens. A total of 300 hatching eggs were randomly pre-assigned to a specific in ovo injection protocol (T-1, T-2, T3, and T-4) and incubated under standard conditions. At embryonic day (ED) 12, the eggs were either injected with distilled water or a dietary supplement (containing a mixture of vitamins, trace minerals, and amino acids). The injection treatment consisted of T-1 (non-injected eggs), T-2 (eggs injected with 5 mL of distilled water), T-3 (eggs injected with 5 mL of a solution containing 0.04% of the dietary supplement), and T-4 (eggs injected with 5 mL of a solution containing 0.08% of the dietary supplement). The results revealed that the experimental treatment had no effect on egg weight or egg weight loss during embryogenesis (p > 0.05). The lowest weight of yolk-free body mass (YFBM-w), yield of yolk-free body mass (YFBM-Y), embryo length (Em-L), embryo width (Em-W), tibia length (TL), and wing length (WL) at ED 16 were identified in T-4 (p < 0.05). At ED 19, the highest Em-L and eye width (Ey-w) were identified in T-3 (p < 0.05). The highest chick weight at hatch (CWAH) and chick yield (CY) were identified in T-4 (p < 0.05). While the chick length and appearance score were lowest in T-4, the chick eye score was lowest in T-2 (p < 0.05). The embryonic mortality, hatchability, navel, and leg scores of chicks were similar among the treatments (p > 0.05). It was concluded that the in ovo injection of a mixture of dietary nutrients could improve embryonic traits during the latter part of embryogenesis, chick weight, and chick yield at hatch; however, it may possess a strong negative effect on hatchability, embryonic mortality, and chick quality traits.
Automatic waste classification is an important enabling technology for cleaner cities, source-level recycling, and low-cost smart-bin systems. Although modern convolutional neural networks achieve strong recognition performance, their deployment on affordable edge devices remains constrained by memory footprint, computational cost, and response latency. This paper presents an edge-oriented compact CNN framework for waste image classification, combining a high-accuracy MobileNetV4 reference model with three lightweight student architectures: EfficientNet-Lite0, LCNet-0.5, and MobileNetV3-Small-0.5. All models are evaluated on TrashNet under a unified preprocessing, training, and size-accounting protocol, allowing a clear comparison of accuracy-efficiency trade-offs. On the main stratified train/validation/test split, the MobileNetV4 teacher achieves 97.09% top-1 accuracy, while the compact students retain strong performance with substantially smaller footprints: EfficientNet-Lite0 reaches 93.99% with 3.38 M parameters, LCNet-0.5 reaches 94.18% with only 0.61 M parameters, and MobileNetV3-Small-0.5 reaches 87.73% with 0.57 M parameters. A complementary stratified five-fold evaluation, including both knowledge-distilled and non-distilled student variants, provides a robust assessment of model behavior across data partitions and confirms LCNet-0.5 as the most suitable sub-megabyte candidate under the proposed size-accuracy selection rule. The selected LCNet-0.5 model achieves a macro-F1 score of 0.9247 on the main TrashNet test split and is integrated into a self-contained Raspberry Pi 3 Model B+ prototype that performs local camera-to-display inference with an observed end-to-end latency of approximately 1.0 s per image. Cross-dataset evaluation on RealWaste further shows that the compact model can be adapted effectively to cluttered real-world imagery through short fine-tuning. Overall, the results demonstrate that careful lightweight architecture selection, supported by knowledge distillation analysis and edge-prototype validation, can deliver accurate, compact, and practically deployable waste classifiers for resource-constrained environments.
The aim of this study was to compare in-vitro changes and properties of conventional chairside-dispensed, 3D-printed and milled interim fixed dental protheses using two different cements. Identical three-unit fixed dental prostheses (FDP) were fabricated from conventional dispensed (1x), milled (1x) and 3D-printed (2x) materials and cemented onto standardized Co-Cr-Mo alloy molars using either conventional or resin cement. Ten FDPs of each group were stored in distilled water and artificially aged for a simulated wear period of 5 years (1.2 × 106 cycles, 50 N force) through thermocycling (TCML). An additional ten FDPs of each group served as the control group, stored in distilled water without aging. Fracture resistance was analyzed before and after TCML (α = 0.05). Survival rates: All FDPs made from conventional and subtractive materials survived TCML, while those made from additive materials did not. Fracture resistance: The median of the fracture resistance values for conventional material was 1096 N (24 h water storage, conventional cementation), 1117 N (TCML, conventional cementation), 1762 N (24 h water storage, adhesive cementation) and 1400 N (TCML adhesive cementation). Significant differences were observed between conventional and additive materials, but not for the subtractive material. The median of the fracture resistance values for subtractive material was 988 N (24 h water storage, conventional cementation), 1065 N (TCML, conventional cementation), 1486 N (24 h water storage, adhesive cementation) and 1204 N (TCML adhesive cementation). Significant differences were found between subtractive and additive materials. The median of the fracture resistance values of the additive materials was 953 N and 754 N (24 h water storage, conventional cementation), 1090 N and 876 N (24 h water storage, adhesive cementation) and 706 N and 373 N (TCML adhesive cementation), showing significant differences both among them and compared to the other materials. Conventionally and subtractively fabricated temporaries do not show significant differences in fracture resistance, although they do outperform additively fabricated temporaries, which exhibit significantly lower fracture resistance. Since none of the tested additively manufactured specimens survived TCML when cemented with Temp Bond NE, premature failure of these materials can be expected in clinical use. This study informs clinicians on the fracture resistance of chairside, milled, and 3D-printed provisional FDPs with different cements, aiding material selection to improve durability and predictability of provisional restorations in clinical practice.
The present investigation focuses on investigating the Amaranthus viridis L. aqueous extract (AVWE) as a prolonged corrosion inhibitor when applied to the surface of Stainless steel410 (SS-410) in an acidic solution (0.5 M HCl), and the inhibitory efficacy of AVWE was utilised in the formulation of a bio-coating that comprises the AVWE inhibitor along with other additives. AVWE was prepared by Soxhlet extraction using distilled water as the solvent to selectively isolate polar phytochemicals responsible for corrosion inhibition. The extraction was carried out under reflux for 12 h, employing 400 mL of solvent for 200 g of dried plant material previously dried in a water bath. The obtained semi-solid extract was transferred to a clean vial and preserved in a desiccator for further use. The inhibition efficiency of the AVWE inhibitor was analysed by various techniques, including gravimetric analysis, electrochemical impedance spectroscopy (EIS), UV-visible absorption spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), and liquid chromatography mass spectroscopy (LC-MS) analysis. Following the completion of studies on the inhibitory effects of AVWE, a bio-coating was formulated with the AVWE and other additives such as resin, metal oxide, and diluents. ASTM D3359 and durability techniques were used to test the adhesion and sustainability of the formulated coating, respectively. UV-visible and IR spectroscopy validated the formation of a protective film on the metal surface. Morphological findings obtained from SEM, EDX, and AFM techniques indicated that the addition of the green inhibitor resulted in a reduction in the surface corrosion of SS-410. A gravimetric analysis demonstrated a corrosion inhibition efficiency of 97.22%, whereas electrochemical impedance spectroscopy (EIS) indicated an inhibition efficiency of 95.12% for SS-410 at an inhibitor concentration of 500 ppm in 0.5 M HCl. LC-MS analysis identifies inhibitor phytochemicals that adsorb on metal. ASTM D 3359 and durability test revealed that the formulated coating is environmentally sustainable and protects SS-410 from corrosion. Stainless steel, the most commonly used engineering material in a wide range of applications, suffers from severe corrosion and gradual deterioration. The effects of corrosion on the economy are widespread and create significant challenges in various sectors. Most of the products used in corrosion mitigation processes are harmful to the environment. Natural inhibitors such as plant extracts and biopolymers are more effective alternatives. These inhibitors are safe, inexpensive, renewable, and eco-friendly. In view of their environmental friendliness and cost-effectiveness, recent investigations have focused on plant-based "green" inhibitors for SS-410 in acidic media (HCl). Electrochemical and surface analyses have demonstrated that Colebrookea oppositifolia extract achieves an inhibition efficiency of 95% for SS410 in 0.5 M HCl, whereas Pouzolzia zeylanica L. extract exhibits an inhibition efficiency of 94.9% under the same conditions. Therefore, AVWE wa Conclusion: The present study proposes a method for employing AVWE, a naturally occurring substance known for its anti-corrosion properties, on stainless steel-410 surfaces. The primary findings indicate that exposure to 0.5 M hydrochloric acid leads to a significant reduction in the corrosion of stainless steel-410 when treated with AVWE. Furthermore, a higher concentration of AVWE demonstrates an enhanced inhibitory effect compared to a lower concentration. The inherent inhibitory properties of AVWE indicate its potential as a promising candidate for the development of environmentally friendly corrosion-resistant coatings.
Large-scale datasets impose substantial training costs on machine learning models. Dataset distillation addresses this issue by synthesizing compact datasets that can achieve performance comparable to that of the original data. However, text dataset distillation remains challenging: the discrete nature of text renders traditional gradient-matching methods ineffective, while embedding optimization approaches are often inefficient and exhibit limited generalization. To address these challenges, this paper proposes an LLM-native distillation framework based on dual-agent collaboration. Our framework decomposes the distillation process into two stages: selection and improvement. The Selector identifies high-quality samples through multi-dimensional scoring, while the Improver enhances data density and clarity under semantic consistency constraints and a candidate generation mechanism. The entire pipeline is automated through a self-iterative cycle of generation and selection, and leverages scoring signals to drive agent self-reward iteration, thereby avoiding differentiable optimization and trajectory matching. Experiments show that starting from a random subsampling, using only 2.5% of original data and only three iterations, the distilled dataset enables Llama-2-7B, Mistral-7B, and Qwen2.5-7B to match full-dataset performance on MMLU and Winogrande. It also improves training stability, convergence efficiency, and cross-model generalization, especially among architecturally similar models, while maintaining a competitive distillation cost of 143 GPU h. Overall, this study provides a practical solution for efficient, automated, and general-purpose text dataset distillation.
Medical image analysis is a cornerstone of modern healthcare, yet conventional single-modal deep learning often struggles with the unique physical constraints and structural variability inherent in data acquired from diverse medical sensors. Recently, Vision-Language Models (VLMs) have sparked a paradigm shift by bridging the semantic gap between visual sensor signals and clinical narratives. Following the PRISMA guidelines, 167 representative studies are systematically synthesized in this review to provide a comprehensive roadmap of VLM technological evolution and clinical utility. First, rather than treating VLMs as generic feature extractors, their underlying mechanisms are uniquely distilled into seven core operational principles, which are then explicitly mapped to downstream applications such as few-shot diagnosis, prompt-driven segmentation, and multi-task foundation models. To facilitate intuitive evaluation, a rigorous quantitative cross-comparison of current benchmark architectures is presented. Crucially, this review goes beyond highlighting successes by critically assessing prevalent clinical bottlenecks, including zero-shot segmentation failures, multi-modal hallucinations in diagnosing rare diseases, and the prohibitive computational complexity associated with 3D volumes and gigapixel whole slide images. Finally, a novel, forward-looking framework is proposed: the transition from static "image-text alignment" to dynamic "multi-source sensor-driven intelligence". By addressing both physical sensor constraints and algorithmic limitations, this survey offers actionable insights for developing trustworthy, sensor-aware clinical diagnostic agents.
Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs) trained with masked language modeling (MLM) exhibit strong zero-shot fitness prediction;we provide a interpretive lens by regarding natural evolution as implicit reward maximization and MLM as inverse reinforcement learning (IRL), in which extant sequences act as expert demonstrations and pLM log-odds serve as fitness estimates. Building on this perspective, we introduce EvoIF, a lightweight model that integrates two complementary sources of evolutionary signal: (i) evolutionary profiles from retrieved homologs and (ii) inverse folding profiles distilled from inverse folding logits. EvoIF fuses sequence-structure representations with these profiles via a compact transition block, yielding calibrated probabilities for log-odds scoring. On ProteinGym (217 mutational assays; > 2.5M mutants), EvoIF and its MSA-enabled variant achieve competitive performance while using only 0.15% of the training data and fewer parameters than recent large models. Ablations confirm that evolutionary and inverse folding profiles are complementary, improving robustness across function types, MSA depths, taxa, and mutation depths. Code is archived on Zenodo at https://doi.org/10.5281/zenodo.20139484.
The clinical translation of nanozymes is hindered by the passive and uncontrollable catalytic properties, necessitating active and precise spatiotemporal regulation. Inspired by the electrostatic preorganization theory of efficient natural enzymes, this review provides a systematic analysis of electric field-regulated nanozymes. Compared to conventional physical stimuli, this approach offers a tunable and complementary framework for active precise therapy. Diverse electrical input modes provide adaptable driving forces for precisely regulating nanozyme catalysis across various biomedical scenarios. Furthermore, at the atomic scale, the underlying mechanisms are elucidated, demonstrating how the electric field inputs optimize the d-band center, surface charges, band structure, and active sites to promote substrate adsorption and lower reaction energy barriers, thereby enhancing the catalytic effect. To maximize electric field-nanozyme coupling, design principles for a complete charge pathway are distilled: optimizing intrinsic field response, directional charge rectification, and achieving low-loss transport. Subsequently, the applications of electric field-regulated nanozymes in precision therapy are summarized, including on-demand spatiotemporal activation, quantitative dosage regulation, and active microenvironment remodeling. Finally, this review highlights the immense potential of interdisciplinary integration in overcoming the biosafety and mechanism bottlenecks of this regulation strategy. These insights provide a new perspective for advancing electric field-regulated nanozymes toward precision therapy.
This project aimed to evaluate the anti-melanogenic characteristics of Nasturtium officinale (N. officinale) by assessing the impact of both aqueous and hydroalcoholic extracts on the inhibition of cellular and mushroom tyrosinase enzymes, as well as the suppression of the melanin synthesis in B16F10 melanoma cells. The aerial components of N. officinale were subjected to extraction using distilled water: ethanol (7:3) through the maceration technique. The extract's phenolic compounds were quantified employing the Folin-Ciocalteu method. The evaluation of the safety profile of the extracts on B16F10 cells was done by the MTT assay. Subsequently, the melanin concentration in B16F10 cells, alongside the inhibitory effects on both mushroom and cellular tyrosinase, was assessed following treatment with the aforementioned extracts. The aqueous and hydroalcoholic extracts exhibited no significant toxicity on B16F10 when compared to Phosphate-Buffered Saline (PBS). Additionally, there was no notable difference in the cytotoxic effects of extracts on the B16F10 cell line. Both extracts resulted in inhibition of cellular and mushroom tyrosinase, along with a decrease in melanin levels in B16F10 in a concentration-dependent manner. Ultimately, the total phenolic content in the aqueous and hydroalcoholic extracts was found to be approximately 14 and 30 mg/g of gallic acid, respectively. This in vitro investigation offers evidence supporting the skin brightening properties of N. officinale as an anti-melanogenic agent. Given its safety profile and absence of toxic effects on melanoma cells, it may be incorporated into the formulation of skin-brightening products following preclinical tests.
Parkinson's disease (PD) is the second-most diagnosed age-related neurodegenerative disorder globally. PD pathology causes dysregulation of motor movement and for many, mild cognitive impairment (PD-MCI). The most recommended global screening exam to detect PD-MCI is the Montreal Cognitive Assessment© (MoCA). Traditionally, the MoCA is scored according to guidelines and compared against a standardized cutoff, but clinical professionals additionally draw upon their observations of the patient's performance to determine the score. To better understand how clinicians use the MoCA in real-world clinical settings, we employed the qualitative descriptive approach to identify performance patterns professionals utilize to assess the cognitive health of a person with PD. We curated retrospective data from nine people with PD-MCI to PD-Dementia. Each patient had one completed MoCA exam and one neuropsychological report containing health data. The assessments were organized into three groups of three and used in semi-structured interviews with six clinical professionals to gather at minimum two clinical opinions for each. Three coders distilled, based on consensus, three clinically meaningful patterns from the interviews composed of features emphasized as vital by the interviewees for determining a person's cognitive health. The derived features were from a patient's performance on sections of the MoCA exam, sociodemographic and health data from the neuropsychological report, and dependent relationships between the assessments. Our study leveraged the popular MoCA exam to explore its real-world clinical use. Extracting these patterns clinicians recognized provides deeper insights into how they interpret cognitive health creating a blueprint for future efforts to tailor the exam for detecting cognitive impairment in people with PD.
We report on the synthesis and photocatalytic activity of Er3+ and Er3+/Yb3+ (co-)doped TiO2 nanoparticles (NPs) prepared via microwave-assisted non-aqueous sol-gel (M) and hydrothermal (H) methods. Both routes yielded anatase NPs, with rare earth doping reducing crystallite size and increasing surface area, while M synthesis achieved higher dopant incorporation. Photocatalytic performance was assessed on minocycline under simulated solar light. Co-doping with 3% Er and 6% Yb using M synthesis (T3Er6Yb-M) led to nearly complete minocycline degradation in 60 min, outperforming commercial TiO2 NPs (Aeroxide P25). The superiority of this sample derives not only from the presence of more surface hydroxyl groups, and a larger surface area, but also from upconversion effect. The infrared (IR) to ultraviolet (UV)/visible (Vis) upconversion was confirmed by photoluminescence measurements and corroborated by photocatalytic tests under simulated solar light with a UV cut-off filter (400-1680 nm), when the co-doped catalyst maintained 42% degradation activity. As proof of concept, T3Er6Yb-M was immobilized (20 wt%) into silica-titania porous microspheres (MICROSCAFS®; MS) to improve the robustness. The supported catalyst (T3Er6Yb-M@MS) removed 95% of minocycline from distilled water in 30 min under simulated solar light. Moreover, it achieved 94% minocycline removal from municipal wastewater in 60 min and 55% from pharmaceutical effluent in 120 min, where turbidity and competing species reduced performance. Overall, this study demonstrates a solar-light-active photocatalyst with a strong performance in both laboratory and real wastewater systems. While matrix effects remain a challenge, the integration of rare-earth co-doping and immobilization on MICROSCAFS® offers a promising pathway toward sustainable water treatment.
Identification tests based on gas chromatography (GC) with a flame ionization detector (GC-FID) are specified for sage oil (SO) and sage water (SW) in the Japanese Standards of Quasi-Drug Ingredients. In the current methods, a packed column (PC) is used for both products, causing prolonged analysis and limited separation efficiency. In addition, sage extract (SE) is designated as the reference standard for the SW identification test; however, its major constituents remain uncharacterized. The capillary column (CC) has become the standard in recent GC procedures, making PC replacement with CC desirable for SO and SW. In this study, we aimed to improve CC-based GC-FID identification tests for SO and SW. The separation behavior of the SO marker components, including α-pinene, thujone, and camphor, was first examined using GC-MS, which demonstrated good separation using CC. Rapid GC conditions were subsequently evaluated, achieving a substantial reduction in analysis time while maintaining sufficient separation and detection sensitivity. The analysis of SW and SE revealed that α-thujone, camphor, and 1,8-cineole were the common major components. However, dilution with ethanol occasionally hindered the visual recognition of marker components. The application of hexane liquid-liquid extraction (Hex-LLE) as a sample pretreatment effectively removed these interfering components, enabling the clear identification of marker compounds even in aqueous samples. These results demonstrate that the combination of CC-based GC-FID and Hex-LLE provides an effective and practical approach for the identification of SO and SW. The method may be applicable to other plant-derived essential oils and aromatic distilled waters.
This study reports the preparation of potassium hydroxide-impregnated corn-cob-derived activated carbon (AC500-K1.1) as an efficient adsorbent for the rapid removal of the azo dye tartrazine (TZ) from aqueous solutions. The raw corn-cob (CC) powder was chemically activated at various KOH-to-biomass ratios and activation temperatures to optimise surface chemistry and porosity. Under optimal conditions (1.1 ratio at 500 °C), the prepared activated carbon exhibited an amorphous structure, a BET surface area of 276.34 m2 g⁻1, and a well-developed porous morphology. FTIR, XRD, FESEM-EDX, TEM, TGA, and BET characterisation confirmed the formation of abundant oxygen-containing acidic functional groups (which can also significantly enhance dye adsorption through electrostatic attraction and hydrogen-bond interactions). The batch adsorption studies revealed that the optimum adsorbent dosage was 0.04 g, with a contact time of 60 min, an initial dye concentration of 100 mg L-1, a pH of 7, and a temperature range of 15-40 °C. The adsorption of TZ onto AC500-K1.1 was best represented by the Freundlich isotherm model (R2 ≈ 0.9986), suggesting heterogeneous and multilayer adsorption, whereas the kinetic behavior was adequately described by the pseudo-first-order model. Thermodynamic parameters demonstrated that the adsorption process was spontaneous and exothermic (ΔH° =  - 8.157 kJ mol⁻1). Moreover, the prepared adsorbent exhibited excellent performance, achieving a maximum experimental adsorption capacity of 675.88 mg g⁻1 and a removal efficiency of 95.07% under the optimized conditions. All of them were shown to exhibit much higher stability in regeneration experiments with up to 10 adsorption-desorption cycles and in recycling experiments. For the TZ-spiked real water samples (distilled water, tap water, and river water), significant dye uptake (> 73%) was observed with AC500-K1.1 and was marginally affected by ionic strength. This process establishes its practical applicability for actual real effluent decontamination. Molecular dynamics simulations performed using BIOVIA Materials Studio indicated that van der Waals interactions play a major role in the adsorption of TZ on activated carbon, with additional contributions from electrostatic interactions, hydrogen bonding, and π-π stacking. RDF and adsorption energy analyses further supported the spontaneous and stable adsorption configuration of TZ molecules on the carbon surface. These results provide molecular-level evidence supporting the experimentally observed adsorption behavior.
A high-fat diet (HFD) with excessive sugar intake contributes to obesity and type 2 diabetes, leading to increased use of non-nutritive sweeteners (NNS). However, evidence on their metabolic effects remains inconsistent, especially between natural and synthetic NNS. This study compared the effects of a natural NNS, monk fruit extract (MFE), with sucrose and synthetic sucralose, at equivalent sweetness on body weight and glucose homeostasis in HFD-induced obese mice. Male C57BL/6 mice were fed an HFD for 8 weeks to induce obesity, then randomized to receive distilled water (control), sucrose (266 g/L), MFE (1.1 g/L; ~50% mogroside V), or sucralose (0.36 g/L) in drinking water for another 8 weeks. Body weight, food and water intake, and oral glucose tolerance tests (OGTT) were assessed. At sacrifice, serum biochemistry, organs, tissues, and duodenal expression of sweet taste receptors (T1R2/T1R3) and glucose transporters (SGLT-1/GLUT2) were analyzed. The results showed that sucralose and sucrose further increased body weight gain, whereas MFE did not promote additional weight gain despite increased food intake. MFE significantly reduced fasting blood glucose, while sucralose impaired glucose tolerance, reflected by increased OGTT area under the curve. No significant differences in visceral adipose tissue weights or duodenal T1R2, T1R3, SGLT-1, and GLUT2 expression were observed among groups. In conclusion, MFE, within ADI-equivalent doses, improved fasting glycemia without promoting further weight gain compared with the HFD-control, whereas sucralose exacerbated obesity-related impaired glucose tolerance. These findings suggest that natural sweeteners such as MFE may represent a safer alternative to added sugars and synthetic NNS for weight management and glycemic control for obese subjects.
This study investigated the effectiveness of sous-vide cooking (SVC) combined with proteolytic enzyme-rich fruit mashes (fig and papaya) in improving the quality characteristics of spent hen drumsticks. Five treatment groups were evaluated: control (DC), distilled water plus sous-vide (DDW), fig mash plus sous-vide (DF), papaya mash plus sous-vide (DP), and a combined fig-papaya mash plus sous-vide treatment (DFP). Physicochemical, technological, textural, microbiological, microstructural, and sensory properties were analyzed. Cooking loss ranged from 27.62 to 39.24%, with the lowest value observed in DDW and the highest in DF, while yield varied between 60.76 and 72.38%. Water-holding capacity ranged from 15.05 to 20.63%, with DDW and DP exhibiting superior retention properties. Fruit mash treatments significantly reduced meat pH and altered color characteristics compared with the control. Texture profile analysis demonstrated substantial tenderization effects, as hardness decreased from 25.83 N in the control to 7.21 N in DFP, accompanied by significant reductions in gumminess and chewiness. Although DF and DFP did not differ statistically in hardness, the combined treatment exhibited the lowest numerical value, indicating the greatest tenderizing effect. Microstructural examination confirmed pronounced degradation of muscle fibers and enlargement of interfibrillar spaces in enzyme-treated samples. Microbiological analyses showed that mash-assisted sous-vide treatments effectively maintained low microbial counts throughout processing. Sensory evaluation revealed no significant differences among treatments in odor, color, flavor, or overall acceptability, although texture scores were affected by treatment type. Overall, the combination of proteolytic fruit mashes and sous-vide cooking, particularly the fig-papaya treatment, proved to be an effective strategy for enhancing tenderness and technological quality of spent hen drumsticks while preserving consumer acceptability.