Search Results (40)

Effective arsenic removal is a challenge when smelting high-arsenic copper minerals (HACMs, As > 3.0 wt%). Current arsenic-removal methods for HACM smelting cannot effectively remove arsenic and do not satisfy environmental requirements. This study argues that two-stage filtration based on Fe-25Al porous material and oxygen-controlled roasting is an effective technique for HACM arsenic removal (As = 11.8 wt%). The use of two-stage filtration facilitated double interception: particles larger than 10 μm were mechanically intercepted by the pore channels, and submicron particles (0.1–10 μm) were intercepted by the filter cake. Specifically, in the second stage, the flue gas underwent gradient rapid cooling, and the arsenic in the flue gas rapidly condensed, resulting in efficient arsenic removal. The purity of the condensed product, As₂O_3, was greater than 99%. Moreover, adding sand to the roasted mineral increased the specific surface area from 0.484 m²/g to 0.590 m²/g, reducing the “bottleneck effect” of pores; the addition of carbon further increased the surface area to 2.457 m²/g, inhibiting the formation of arsenate. When the mineral feed rate increased from 50 kg/h to 80 kg/h, the oxygen partial pressure decreased; this effectively inhibited the formation of iron arsenate, and the arsenic removal efficiency increased from 70.20% to 95.61%. The optimized process achieved ≥94% arsenic removal efficiency and ≥76% sulfur-fixation efficiency, with low energy cost. Material balance analysis showed that after arsenic removal, the Cu/Si to Fe/Si ratio of the copper mineral reached 1.5, which is appropriate for immediate subsequent smelting. This study provides a new technological strategy for HACM arsenic removal. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

The article presents the first method based on high-performance liquid chromatography and ultraviolet detection (HPLC-UV) for the determination of timonacic (thioproline, 1,3-thiazolidine-4-carboxylic acid, tPro) in pharmaceutical tablets and face care products (creams, sera, foundations, suncreams). Sample preparation primarily involves solid-liquid extraction (SLE) of tPro with 0.2 mol/L phosphate buffer pH 6, derivatization with 0.25 mol/L 2-chloro-1-methylquinolinium tetrafluoroborate (CMQT), followed by polytetrafluoroethylene (PTFE) membrane filtration. The chromatographic separation of the stable UV-absorbing 2-S-quinolinium derivative is achieved within 14 min at 25 °C on a Zorbax SB-C18 (150 × 4.6 mm, 5 µm) column using gradient elution. The eluent consists of 0.1 mol/L trichloroacetic acid (TCA), pH 1.7, in a mixture with acetonitrile (ACN) delivered at a flow rate of 1 mL/min. The analyte is quantified by monitoring at 348 nm. The assay linearity was observed within 0.5–125 μmol/L. The limit of quantification (LOQ) was found to be 0.5 μmol/L. The accuracy ranged from 93.22% to 104.31% and 97.38% to 103.48%, while precision varied from 0.30% to 11.23% and 1.13% to 9.64% for intra- and inter-assay measurements, respectively. The method was successfully applied to commercially available on the Polish market pharmaceutical and cosmetic products. Full article

Giardia lamblia is a flagellated protozoan and the etiological agent of giardiasis, a leading cause of epidemic and sporadic diarrhoea globally. The clinical and public health relevance of giardiasis underscores the need for robust methodologies to investigate and manage this pathogen. This study reviews the main methodologies described in the literature for studying the life cycle of G. lamblia, focusing on isolation, purification, axenization, excystation, and encystation. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. Searches were performed in MEDLINE, ScienceDirect, and Web of Science Core Collection databases. A total of 43 studies were included, revealing 58 methods for isolation and purification, 7 for excystation, 2 for axenization, and 5 for encystation. Isolation and purification methods exhibited significant variability, often involving two phases: an initial separation (e.g., filtration and centrifugation) followed by purification using a density gradient for faecal samples or immunomagnetic separation for water samples. Method effectiveness differed depending on the sample source and type, limiting comparability across studies. In contrast, methods used for other life cycle stages were more consistent. These findings underscore the need for standardised methodologies to enhance the reproducibility and reliability of research outcomes in this field. Full article

Background and Objectives: Non-invasive imaging biomarkers for the early detection of chronic kidney allograft injury are needed to improve long-term transplant outcomes. T1 mapping by magnetic resonance imaging (MRI) has emerged as a promising method to assess renal structure and function. This study aimed to determine the potential of MRI as a diagnostic tool for evaluating graft function and structural changes in kidney grafts 1 year after transplantation. Materials and Methods: Thirty-four kidney transplant recipients were prospectively recruited, with 27 completing the follow-up at one year. Renal MRI at 3T was performed to acquire T1, T2, and apparent diffusion coefficient (ADC) maps. Clinical parameters, including estimated glomerular filtration rate (eGFR), albumin-to-creatinine ratio (ACR), protein-to-creatinine ratio (PCR), and histological IF/TA scores, were collected. MRI parameters were compared across the groups stratified by clinical and histological markers. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis. Results: At 1 year, T1 corticomedullary differentiation (CMD) values were significantly higher in patients with elevated ACR (≥3 mg/mmol), PCR (≥15 mg/mmol), and mild to moderate or severe IF/TA, reflecting a reduction in the corticomedullary gradient. T1 CMD demonstrated moderate-to-good diagnostic performance in detecting ACR (AUC 0.791), PCR (AUC 0.730), and IF/TA (AUC 0.839). No significant differences were observed in T2 or ADC values across these groups. T1 CMD also showed a significant positive correlation with ACR but not with eGFR, suggesting a closer association with structural rather than functional deterioration. Conclusions: T1 mapping, particularly T1 CMD, shows promise as a non-invasive imaging biomarker for detecting chronic allograft injury and monitoring renal function 1 year after kidney transplantation. Full article

Background: Chronic kidney disease (CKD) is a prevalent complication among individuals with type 2 diabetes (T2D), posing significant diagnostic challenges in resource-limited settings due to infrequent testing and missed hospital visits. This study aimed to develop a simple, effective ML model to identify T2D patients at high risk for reduced kidney function. Methods: We retrospectively analyzed data from 3471 T2D patients collected over a ten-year period at a university hospital in Bangkok, Thailand. Two models were developed using readily available clinical features: one including hemoglobin A1c (HbA1c) levels (the “with-HbA1c” model) and one excluding HbA1c levels (the “non–HbA1c” model). Three tree-based ML algorithms—decision tree, random forest, and extreme gradient boosting (XGBoost) algorithms—were employed. The outcome label was CKD, defined as an estimated Glomerular Filtration Rate (eGFR) < 60 mL/min/1.73 m² that persisted for more than 90 days. The model performance was evaluated using the AUROC. The feature importance was assessed using Shapley additive explanations (SHAP). Results: The XGBoost algorithm demonstrated a strong predictive performance. The “with-HbA1c” model achieved an AUROC of 0.824, while the “non–HbA1c” model attained a comparable AUROC of 0.819. Both models were well-calibrated. SHAP analysis identified age, HbA1c, and systolic blood pressure as the most influential predictors. Conclusions: Our simplified, interpretable ML models can effectively stratify the risk of reduced kidney function in patients with T2D using minimal, routine data. These models represent a promising step toward integration into clinical practice, such as through EHR-based alerts or patient-facing mobile applications, to improve early CKD detection, particularly in resource-limited settings. Full article

The current review examines various methods for preparing photocatalytic materials based on ceramic substrates, with a focus on incorporating metal oxides such as ZnO, CuO, and MgO. This study compares traditional mixing, co-precipitation, sol–gel, and autoclave methods for synthesizing these materials. Kaolin-based ceramics (DD3 and DD3 with 38% ZrO₂) from Guelma, Algeria, were used as substrates. This review highlights the effects of different preparation methods on the structural, morphological, and compositional properties of the resulting photocatalysts. Additionally, the potential of these materials for the photocatalytic degradation of organic dyes, specifically Orange II, was evaluated. Results indicated that ceramic/ZnO/CuO and ceramic/MgO powders prepared via traditional mixing and co-precipitation techniques exhibited significantly faster degradation rates under visible light than Cu layers deposited on ceramic substrates using solution gradient processes. This enhancement was attributed to the increased effective surface area and the size of the spherical nanoparticles obtained through these methods, which facilitated accelerated pollutant absorption. This study highlights the ease and cost-effectiveness of preparing robust layers on ceramic substrates, which are advantageous for photocatalytic applications due to their straightforward removal after filtration. Notably, DD3Z/MgO powders demonstrated superior catalytic activity, achieving complete degradation of the organic dye in just 10 min, whereas DD3Z/ZnO-CuO powders achieved 93.6% degradation after 15 min. Additionally, experiments using kaolin-based ceramics as substrates instead of powders yielded a maximum dye decomposition rate of 77.76% over 6 h using ZnO thin layers prepared via the autoclave method. Full article

Outer membrane vesicles (OMVs) are extracellular vesicles secreted by Gram-negative bacteria with diameters of 20–250 nm. OMVs contain various biologically active substances from their parent bacteria, such as proteins, lipids, and nucleic acids. Escherichia coli Nissle 1917 (EcN) is a Gram-negative probiotic that resides in the human intestine. EcN-derived OMVs are pivotal in modulating intestinal immune responses. However, few studies have addressed the heterogeneity of EcN-derived OMVs in terms of size, significantly limiting the research on their clinical applications. Currently, there are a lack of feasible methods for obtaining EcN-derived OMVs of different sizes. To address this knowledge gap, we developed a membrane filtration method to isolate EcN-derived OMVs of varying sizes. In this study, we first used gradient filtration to isolate high-purity EcN-derived OMVs and conducted a proteomic analysis. Subsequently, we used membrane filtration to separate the EcN-derived OMVs by size. We successfully obtained EcN-derived OMVs of three specific sizes: <50 nm, 50–100 nm, and 100–300 nm. We then performed proteomic analyses of these EcN-derived OMVs and compared their protein profiles. Finally, we compared the ability of each EcN-derived OMV type to induce RAW264.7 macrophages to secrete the pro-inflammatory factor interleukin (IL)-1β and the anti-inflammatory factor IL-10. The EcN-derived OMVs contained 646 different proteins overall; those of different sizes contained different protein types. Among them, the EcN-derived OMVs in the <50 nm group contained significantly fewer proteins (262 different types in total) than those in the 50–100 nm (1603 types) and 100–300 nm (1568 types) groups. Furthermore, the <50 nm group had fewer membrane proteins (40) than the 50–100 nm (215) and 100–300 nm (209) groups. We also found that RAW264.7 macrophages secreted different concentrations of IL-1β and IL-10 following co-incubation with the three EcN-derived OMV types. The 50–100 nm EcN-derived OMV group showed a stronger effect in terms of inducing inflammatory cytokine secretion compared to the other two groups. This study provides direct experimental evidence that EcN-derived OMVs of different sizes exhibit heterogeneous properties. Full article

Background/Objectives: Rare earth elements (REEs) are emerging environmental pollutants, with human exposure increasing due to recent industrial and technological activities. While most studies have focused on detecting REEs in human fluids, their presence in tissues remains understudied. Aortic valve degeneration is known to facilitate the adsorption of various chemical elements; however, the occurrence of REEs in human valves has not yet been investigated. This exploratory study aimed to determine the presence of REEs in the aortic valves of patients with aortic stenosis undergoing surgical valve replacement. It also analyzed potential correlations between REE levels in the valves, epicardial adipose tissue, serum, and selected disease markers. Methods: Samples of aortic valve, epicardial adipose tissue, and serum were collected from 20 adult patients undergoing elective aortic valve replacement. The concentrations of 14 REEs in these samples were measured using inductively coupled plasma mass spectrometry. Biochemical and clinical parameters of the patients were also considered to explore potential associations with the determined REE levels. Results: Total REEs, heavy REEs, and light REEs in aortic valves, epicardial fat, and serum were not intercorrelated. Moreover, for any sample type, they were not significantly related to the patient’s demographics (age and sex), clinical characteristics (body mass index, heart failure severity, and systolic pressure gradients), kidney function (estimated glomerular filtration rate), and biochemical markers (creatinine, lipoprotein(a), total cholesterol, HDL, LDL, and fibrinogen). Smoking was the only factor influencing REE burden in studied patients, with active smokers revealing 61% higher serum REE concentrations and past smokers exhibiting 133% higher REE valvular deposition. Conclusions: The findings suggest that REE accumulation in aortic valve tissues occurs independently of systemic and clinical parameters but may be promoted by smoking, highlighting the need to investigate the underlying mechanisms of REE deposition. Given the small sample size and the cross-sectional, hypothesis-generating design, these observations should be interpreted with caution and treated as preliminary. Larger, longitudinal studies are needed to validate these results and explore potential causal relationships. Further research should also include the tissue originating from individuals without aortic stenosis for comparison. A deeper understanding of the pathways and health risks associated with REEs in cardiovascular tissues may offer valuable insights into their broader implications for human health. Full article

This paper introduces a method that enhances the traditional slip casting technique’s potential to fabricate ceramic objects with variable thickness. The variability depends on the different filtration rates offered by plaster moulds of varying densities. Two sets of experiments are presented. They focused on identifying (1) the maximum workable density range of moulds made from plaster of Paris and (2) the range of thickness in the resulting ceramic casts. This was accomplished by creating four square flat moulds with different gypsum/water (G:W) ratios and their corresponding casts. Based on these findings, the second set of experiments focused on assembling graded plaster moulds with variable densities (G:W 1:3 to 2:1), resulting in ceramic tiles exhibiting a thickness gradient of 2 mm. These results suggest the possibility of producing double-curved ceramic objects (e.g., custom ceramic tiles or sanitaryware) with graded thickness, tailored to their desired structural and functional performance. Full article

Background/Objectives: Diabetes mellitus (DM) is a prevalent disease with an increased risk of complications. Identifying risk factors for mortality in these patients is crucial, as early recognition can facilitate prompt therapeutic intervention. Machine learning (ML) models have proved to be valuable tools in different scenarios of healthcare decision making. We aimed to develop and test an ML model to predict all-cause mortality in a large cohort of subjects with DM. Methods: We included 1969 consecutive patients with DM type 1 (T1DM, n = 255) and type 2 (T2DM, n = 1714). eXtreme Gradient Boosting (XGBoost) was used for the prediction of all-cause mortality in this cohort and the Shapley additive explanation (SHAP) was used to assess the importance of each feature of the classifier. The missing values were imputed using the Missforest methodology. Results: The all-cause mortality rate was 21% during 5.5 ± 1.1 years of follow-up. The ML model achieved 90% sensitivity and 87% specificity with an AUC of 0.88 and an accuracy of 88% for predicting all-cause mortality. The SHAP analysis identified a lower glomerular filtration rate (eGFR), duration of insulin therapy, and a lower level of hemoglobin as the first three factors that contribute to the higher mortality rate. Conclusions: ML models can become valuable tools in clinical practice due to their unique ability to simultaneously assess the cumulative influence of multiple parameters and discover high-order interactions. The application of such models in clinical practice could improve the early identification of subjects at risk for complications and mortality and prompt early therapeutical interventions. Full article

Background: We aimed to compare the procedural efficacy and long-term clinical results of a totally contrast-free Transcatheter Aortic Valve Implantation (TAVI) procedure (i.e., contrast dye was not used for either the pre-procedural assessment or during the procedure) to those of standard practice in patients with severe renal dysfunction. Methods: All consecutive patients with a glomerular filtration rate (GFR) ≤ 35 mL/min and severe aortic stenosis who were treated with transfemoral TAVI at our Institution were included in the registry. The zero-contrast patients underwent carbon dioxide angiography and a non-contrast CT scan for assessment of vascular access suitability, and aortic annulus sizing was performed by a TEE, and the procedural guidance was fluoroscopic and echocardiographic. Procedural outcomes were evaluated, and clinical long-term follow-up was performed for all included patients. Results: A total of 44 patients (median age, 85 (IQR, 80.75–87.00)) were included in the zero-contrast group (TEE guidance and general anesthesia in 37 (84%) patients), while 63 patients were included in the standard practice arm (82 ± 78 mL of contrast dye used). Procedural success was obtained in 100% of cases. There were no differences in procedural outcomes, including final mean aortic gradients (5.5 (IQR, 5.0–10.0) mmHg in the zero-contrast group vs. 6.0 (IQR, 5.0–10.0) mmHg in the standard practice group) and rate of at least a moderate paravalvular leak (0% vs. 1.6% in the zero-contrast and standard practice groups, respectively; p = 0.31). No differences in AKI during the hospital stay were observed. Over a median follow-up of 3.3 years, there was a significantly lower rate of AKI (1.2% vs. 25.9%, p < 0.001) and rehospitalizations (1.6% vs. 35.5%, p < 0.00) in standard practice group. Conclusions: We showed for the first time the feasibility and efficacy of a totally contrast-free strategy compared to standard practice in TAVI patients with severe renal dysfunction. Besides achieving comparable procedural results, the zero-contrast strategy showed a better long-term clinical outcome in reducing hospital readmissions for kidney function deterioration. Full article

Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7389 participants and 53 variables. Initially, unsupervised k-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (LightGBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised k-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF, was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery. Full article

Bacterial extracellular vesicles (bEVs) secreted by Gram-negative bacteria are referred to as outer membrane vesicles (OMVs) because they originate in the outer membrane. OMVs are membrane-coated vesicles 20–250 nm in size. They contain lipopolysaccharide (LPS), peptidoglycan, proteins, lipids, nucleic acids, and other substances derived from their parent bacteria and participate in the transmission of information to host cells. OMVs have broad prospects in terms of potential application in the fields of adjuvants, vaccines, and drug delivery vehicles. Currently, there remains a lack of efficient and convenient methods to isolate OMVs, which greatly limits OMV-related research. In this study, we developed a fast, convenient, and low-cost gradient filtration method to separate OMVs that can achieve industrial-scale production while maintaining the biological activity of the isolated OMVs. We compared the gradient filtration method with traditional ultracentrifugation to isolate OMVs from probiotic Escherichia coli Nissle 1917 (EcN) bacteria. Then, we used RAW264.7 macrophages as an in vitro model to study the influence on the immune function of EcN-derived OMVs obtained through the gradient filtration method. Our results indicated that EcN-derived OMVs were efficiently isolated using our gradient filtration method. The level of OMV enrichment obtained via our gradient filtration method was about twice as efficient as that achieved through traditional ultracentrifugation. The EcN-derived OMVs enriched through the gradient filtration method were successfully taken up by RAW264.7 macrophages and induced them to secrete pro-inflammatory cytokines such as tumor necrosis factor α (TNF-α) and interleukins (ILs) 6 and 1β, as well as anti-inflammatory cytokine IL-10. Furthermore, EcN-derived OMVs induced more anti-inflammatory response (i.e., IL-10) than pro-inflammatory response (i.e., TNF-α, IL-6, and IL-1β). These results were consistent with those reported in the literature. The related literature reported that EcN-derived OMVs obtained through ultracentrifugation could induce stronger anti-inflammatory responses than pro-inflammatory responses in RAW264.7 macrophages. Our simple and novel separation method may therefore have promising prospects in terms of applications involving the study of OMVs. Full article

During the consolidation period of vacuum preloading drainage of dredged mud slurry, the clogging behaviors of the filter drainage structural layers and the core boards of prefabricated vertical drains (PVD) determine the drainage capacities of PVD. However, currently, there is a lack of comprehensive research on the evaluation criteria for these two clogging behaviors. Therefore, based on typical dredged mud slurry, typical geomembranes, and raw material core boards with different bending forms, relevant macro and micro-scale experimental studies have been carried out in this study. The research results show that (1) with the application of the gradient ratio test method, the clogging behaviors of filter membranes of PVD under graded vacuum preloading can be effectively simulated. Also, in the design of graded vacuum preloading, characteristics of equivalent pore sizes and pore structures should be emphasized to investigate the suitability of filtration and drainage performance of PVD filter membranes. (2) The compressive yield strength of core board grooves is a key factor influencing the reduction rate of flow capacity. The reduction rate of flow capacity and well resistance increment can be used as comprehensive indicators reflecting the clogging behaviors of core boards, while the bending angles and bending rates of core boards can be used as specific technical indicators. (3) The proposed clogging evaluation criteria for PVD are as follows: a filter membrane gradient ratio (GR) > 4.0, a core board bending rate >60% and a core board bending angle < 45°, or a reduction rate of flow capacity of bending drainage board > 90% or well-resistance increment > 9. Also, these criteria can be incorporated into the control indicators for drainage performance of PVD used in such types of foundations. Full article