Search Results (177)

YKL-40 is a glycoprotein that may be present at elevated levels in many cancers and neurodegenerative diseases. It has been investigated in numerous studies as a potential biomarker for several conditions, including Alzheimer’s Disease (AD). In this study, a biosensor with Surface Plasmon Resonance imaging (SPRi) detection, sensitive to YKL-40, was constructed for the detection of this analyte in the blood plasma of AD patients. Extensive validation of the biosensor was performed. This included the determination of analytical parameters such as the biosensor’s response characteristics, detection and quantification limits, precision, accuracy, repeatability, selectivity, stability, and performance in natural samples. Validation parameters were primarily tested using standard solutions, while natural samples were employed to evaluate repeatability, stability, and assay accuracy in three groups of samples from different patients. A YKL-40-specific antibody was used as the receptor layer, immobilized on a gold plate using the EDC/NHS protocol on thiol 11-MUA. The biosensor exhibited a wide operating range (1–200 ng/mL), a low detection limit (LOD) of 2 pg/mL, and a quantification limit (LOQ) of 7 pg/mL. High precision and accuracy were confirmed by the calculated standard deviations (SD) and coefficients of variation (CV), which ranged from 0.0009 to 7.02 ng/mL and from 0.12% to 9.24%, respectively. The sensor also demonstrated good repeatability (CV = 4.995%) and was capable of detecting the analyte of interest in complex biological matrices. Its applicability was confirmed in a study using plasma from AD patients and two selected control groups: plasma from smokers and patients with prostatitis. This allowed the assessment of YKL-40 levels across different groups. The results were consistent with literature values, and statistical analysis confirmed the significance of concentration differences between groups. Furthermore, ROC curve analysis confirmed the diagnostic usefulness of the constructed YKL-40 test in the context of Alzheimer’s disease. Full article

A systematic study was conducted on the fatigue performance of Q500qENH weathering steel welded joints under low-temperature conditions of −40 °C in this paper. Low-temperature fatigue tests were conducted on V-groove butt joints and cross-shaped welded joints and S-N curves with a 95% reliability level were obtained. A comparative analysis with the Eurocode 3 reveals that low-temperature conditions lead to a regular increase in the design fatigue strength for both types of welded joints. Fracture surface morphology was examined using scanning electron microscopy, and combined with fracture characteristic analysis, the fatigue fracture mechanisms of welded joints under low-temperature conditions were elucidated. Based on linear elastic fracture mechanics theory, a numerical simulation approach was employed to investigate the fatigue crack propagation behavior of welded joints. The results indicate that introducing an elliptical surface initial crack with a semi-major axis length of 0.4 mm in the model effectively predicts the fatigue life and crack growth patterns of both joint types. A parametric analysis was conducted on key influencing factors, including the initial crack size, initial crack location, and initial crack angle. The results reveal that these factors exert varying degrees of influence on the fatigue life and crack propagation paths of welded joints. Among them, the position of the initial crack along the length direction of the fillet weld has the most significant impact on the fatigue life of cross-shaped welded joints. Full article

Water stress represents one of the most critical limiting factors affecting plant distribution, growth rate, biomass accumulation, and crop yield across diverse growth stages. Variations in species’ drought tolerance fundamentally shape global biodiversity patterns by influencing survival rates, distribution ranges, and community composition under changing environmental conditions. This study investigated the physiological responses of six plant species (Haloxylon ammodendron (H.A.), Nitraria tangutorum Bobr. (N.T.B.), Sympegma regelii Bge. (S.R.B.), Tamarix chinensis (T.C.), Potentilla fruticosa (P.F.R.), and Sabina chinensis (Linn.) Ant. (S.C.A.)) to varying water stress levels through controlled water gradient experiments. Four treatment levels were established: W1 (full water supply, >70% field water holding capacity); W2 (mild stress, 50–55%); W3 (moderate stress, 35–40%); and W4 (severe stress, 20–25%). Height growth and leaf mass per area decreased significantly with increasing water stress across all species. S.C.A. consistently exhibited the highest leaf mass per area among the six species, while H.A. showed the lowest values across all treatments. Leaf water content declined progressively with intensifying water stress, with T.C. and P.F.R. showing the most pronounced reductions (T.C.: 16.53%, 18.07%, and 33.37% under W2, W3, and W4, respectively; P.F.R.: 19.45%, 28.52%, and 36.08%), whereas N.T.B. and H.A. demonstrated superior water retention capacity (N.T.B.: 2.44%, 6.64%, and 9.76%; H.A.: 1.44%, 4.39%, and 5.52%). Water saturation deficit increased correspondingly with declining soil moisture. Diurnal leaf water potential patterns exhibited a characteristic V-shaped curve under well-watered (W1) and mildly stressed (W2) conditions, transitioning to a double-valley pattern with unstable fluctuations under moderate (W3) and severe (W4) stress. Leaf water potential showed linear relationships with air temperature and relative humidity, and a quadratic relationship with atmospheric water potential. For all six species, the relationship between pre-dawn leaf water potential and soil water content followed the curve equation y = a + b/x. Under water-deficient conditions, S.C.A. exhibited the greatest water physiological changes, followed by P.F.R. Both logarithmic and power function relationships between leaf and soil water potentials were highly significant (all F > 55.275, all p < 0.01). T.C. leaf water potential was the most sensitive to soil water potential changes, followed by S.C.A., while H.A. demonstrated the least sensitivity. These findings provide essential theoretical foundations for selecting drought-resistant plant species in arid regions of the Qaidam Basin. This study elucidates the response mechanisms of six distinct drought-tolerant plant species under water stress. It provides critical theoretical support for selecting drought-tolerant species, designing community configurations, and implementing water management strategies in vegetation restoration projects within the arid Qaidam Basin. Furthermore, it contributes empirical data at the plant physiological level to understanding the mechanisms sustaining species diversity in arid ecosystems. Full article

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring. Full article

The conductivity of direct and alternating current for graphite-like amorphous carbon films after annealing in vacuum at a temperature of 700 °C was studied. The I–V characteristics of such films are symmetrical. The I–V curve in logarithmic coordinates demonstrated the presence of two linear sections. A study of the frequency dependences of structures with a thin graphite-like amorphous carbon film showed a sharp increase in capacitance at low frequencies and a decrease in the high-frequency region. The increase in capacitance in the low-frequency region is explained by the Maxwell–Wagner polarization, which is observed in inhomogeneous dielectrics with conducting inclusions. The results of temperature measurements of resistance showed that at room temperatures, there is a mechanism of conduction of electrons with a variable jump length along localized states lying in a narrow energy band near the Fermi level. At the same time, with an increase in the injection current, an additional mechanism of hopping electrical transport with a variable jump length along localized states in the tail of the valence band arises, which leads to an increase in the conductivity of the films. Full article

Prognostic scores that help allocate resources and time to the most critical patients could have potentially improved the response to the SARS-CoV-2 pandemic. We assessed the performance of five risk scores in predicting death or transfer to the intensive care unit (ICU) or sub-intensive care unit (SICU) in hospitalised patients with SARS-CoV-2 infection, with the three aims of retrospectively analysing the effectiveness of these tools, identifying frail patients at risk of death or complications due to infection, and applying these tools in the event of future pandemics. A retrospective observational study was conducted by evaluating data from patients hospitalised with SARS-CoV-2 infection. Among 134 patients considered, 119 were enrolled. All patients were adults, with a mean age of 64 years, and were hospitalised in the Infectious Diseases Division. We compared the five scores using receiver operating characteristic curves and calculation of the areas under the curve (AUCs) to determine their predictive performance. Four of the five scores demonstrated a high accuracy in predicting mortality among COVID-19-positive patients, with AUCs between 0.749 and 0.885. However, only two of the five scores showed good performance in predicting transfer to the ICU or SICU, with AUCs ranging from 0.740 to 0.802. The 4C Mortality Score and COVID-GRAM presented the highest performance for both outcomes. These two scores are easy to apply and low cost. They could still be used in clinical practice as predictive tools for frail and elderly patients with SARS-CoV-2 infection, as well as in the event of future pandemics. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the major epidemiological oncological confronts with established risk factors, including male sex. Still, CRC is reported among the leading malignancies in the female population. The necessity for possible, easily accessible prognostic factors is required to improve patient outcomes. This study aimed to assess sex-related differences in nine-month four-stage CRC results of palliative systemic therapy. Methods: A total of 67 patients (39 males) with a median age of 70 (64–76) years were referred for first-line palliative chemotherapy due to end-stage colorectal cancer diagnosis. The CRC advancement was evaluated by computed tomography (CT) before and 9 months after chemotherapy. The demographical and clinical characteristics were evaluated for nine-month therapy outcomes, including mortality risk and CT scan results. Results: The nine-month mortality risk in female and male groups was indifferent, reaching 21% (6 patients) and 21% (8 patients), respectively (p = 0.935). Among survivors, therapy response was observed in 6 (21%) female and 20 (51%) male patients (p = 0.056). In multivariable analysis, the male sex (OR: 3.91, 95% CI: 1.09–14.05, p = 0.037) and RDW (OR: 0.61, 95% CI: 0.42–0.88, p = 0.008) were found to be significant for disease response to systemic therapy based on CT scan results. The ROC curve for predictive role yields a sensitivity of 71.1%, specificity of 57.8%, and an area under the curve (AUC) of 0.726. Conclusions: Our analysis points out the possible favorable role of the male sex on nine-month systemic therapy response in palliative CRC. The RDW-CV can be regarded as a possible indicator of chemotherapy response in colorectal cancer. The mortality risk within 9 months of systemic therapy is comparable between males and females. Full article

Background: Hemodialysis patients, due to impaired kidney function and compromised immune responses, face increased risks from SARS-CoV-2. Anti-nucleocapsid IgG (anti-IgG N) antibodies are a commonly used marker to assess prior infection in the general population; however, their efficacy for hemodialysis patients remains unclear. Methods: A retrospective study of 361 hemodialysis patients evaluated anti-IgG N antibodies for detecting prior SARS-CoV-2 infection. Antibody levels were measured using a chemiluminescence immunoassay (CLIA) over the four time points. Boxplots illustrated antibody distribution across sampling stages and infection status. Logistic regression and receiver operating characteristic (ROC) curve analysis determined diagnostic accuracy, sensitivity, specificity, and optimal cutoff values. Results: Among the 361 hemodialysis patients, 36 (10.0%) had SARS-CoV-2 infection. Sex distribution showed a trend toward significance (p = 0.05). Boxplot analysis showed that anti-IgG N levels remained low in non-infected patients but increased in infected patients, peaking at the third sampling. Anti-IgG N demonstrated high diagnostic accuracy (AUC: 0.973–0.865) but declined over time (p = 0.00525). The optimal cutoff at C1 was 0.01 AU/mL (sensitivity 1.00, specificity 0.94). Adjusted models had lower predictive value. Conclusions: Anti-IgG N antibodies showed high diagnostic accuracy for detecting prior SARS-CoV-2 infection in hemodialysis patients, though performance declined over time. These findings highlight the need for tailored diagnostic strategies in this vulnerable population. Full article

Background/Objectives: According to the guidelines established by the European Society of Vascular Surgery (ESVS), a minimum 2 mm diameter is advised for both the radial artery (RA) and cephalic vein (CV) to perform a radio-cephalic arteriovenous fistula (RC-AVF). However, studies have suggested that larger vein diameters, over 2.5 or 3 mm, or even smaller vessel diameters, above 1.6 mm, can yield satisfactory outcomes in both the medium and long term. This study aims to analyze how preoperative vascular mapping influences the long-term RC-AVF failure, considering adherence to guidelines. Methods: This retrospective, monocentric, and observational study enrolled 110 patients with ESKD who were admitted, between 2018 and 2024, to the Vascular Surgery Department at the Emergency County Hospital of Târgu Mureș for the creation of an RC-AVF. Demographic characteristics, comorbidities, preoperative vascular mapping data, and laboratory data were meticulously collected from the hospital’s electronic databases. Patients enrolled in the current study were categorized into two groups based on their adherence to guideline recommendations. Results: Patients whose RC-AVF was created outside guideline recommendations demonstrated smaller arterial (p < 0.001) and venous (p < 0.001) diameters. Additionally, a higher percentage of these patients were on hemodialysis via CVC at the time of RC-AVF creation (p = 0.041), as well as a higher incidence of 6-week AVF maturation failure (p = 0.012) and long-term AVF failure (p = 0.016). In ROC Curve analysis, a threshold of 2.75 mm was established for the RA (AUC: 0.647, p = 0.005) and 2.52 mm for the CV (AUC: 0.677, p = 0.001). Additionally, patients whose RC-AVF procedures adhered to guideline recommendations had a significantly lower risk of long-term RC-AVF failure (HR: 0.44, p = 0.012). This association lost significance after adjusting for cardiovascular risk factors and the presence of CVC at admission (HR: 0.69, p = 0.328). After full adjustment, only the CV remained an independent predictor of long-term successful RC-AVF (HR: 0.68, p = 0.026). In contrast, RA lost significance after adjusting for cardiovascular risk factors and the presence of CVC at admission (HR: 0.71, p = 0.086). Conclusions: In conclusion, this study reveals that only the diameter of the CV is correlated with the long-term failure of RC-AVF, independent of age, gender, diabetes, hypertension, active smoking, and the presence of a CVC at the time of AVF creation. Therefore, while adhering to the threshold diameters of the AR and CV, as recommended by the ESVS guidelines, facilitates the creation of a functional RC-AVF, we assert that additional cofactors, such as demographic data, usual cardiovascular risk factors, or CVC presence, must also be considered to achieve optimal long-term AVF. Full article

Background: Copper dyshomeostasis has been implicated in a subset of Alzheimer’s disease (AD) patients, characterized by elevated non-ceruloplasmin-bound copper (non-Cp Cu). However, traditional methods for estimating non-Cp Cu are indirect and analytically imprecise. This study introduces and validates a direct assay for exchangeable copper (ExcCu) by inductively coupled plasma-mass spectrometry (ICP-MS), compliant with Clinical and Laboratory Standards Institute (CLSI) guidelines. Methods: We performed analytical validation of the ExcCu assay following CLSI protocols (EP5, EP6, EP7, EP9, EP15, and EP28). ExcCu and other copper-related biomarkers were quantified in serum samples from 154 healthy controls, 82 AD patients, and 10 patients with Wilson disease (WD). Diagnostic performance was evaluated via receiver operating characteristic (ROC) curve analysis, and inter-method agreement was assessed using Bland–Altman plots. Results: The ExcCu assay demonstrated excellent linearity, precision (CV < 6%), and inter-laboratory reproducibility. Among AD patients, ExcCu levels were significantly elevated compared to controls (p < 0.001). ExcCu distinguished AD from controls with an AUC of 0.80 and a specificity of 95%. Compared to non-Cp Cu, ExcCu yielded no negative values and showed reduced bias. The relative exchangeable copper (REC) index was more effective in differentiating AD from WD (AUC = 0.88). Conclusions: The validated ExcCu assay overcomes the limitations of the traditional non-Cp Cu calculation, offering a reliable biomarker for copper-related AD subtypes. Its high specificity supports its use in patient stratification, potentially contributing to personalized approaches in AD diagnosis and therapy. Full article

The lithium-plating phenomenon induced by low-temperature fast charging of lithium-ion batteries severely compromises their performance and safety. However, current lithium-plating detection methods predominantly rely on complex hardware systems with insufficient sensitivity, presenting significant challenges for implementation in increasingly prevalent Vehicle-to-Grid (V2G) scenarios. This study proposes a novel bidirectional pulse-current charging method designed to mitigate lithium plating and retard battery aging through intermittent pulse-current application. Experimental results verify a 30–50% reduction in capacity fade rate under fast charging conditions (≥0.5 C rates). Furthermore, by leveraging pulse-current characteristics, we reveal strong correlations between the evolution patterns of charge/discharge internal resistance and lithium plating. An in situ detection criterion requiring no additional hardware is established: the L-shaped decline of charging internal resistance under high-rate conditions coupled with the disappearance of defined reverse-hump curves in discharge resistance profiles serve as precise indicators of lithium-plating onset. Validation through SEM and relaxation voltage differential analysis confirms 100% detection accuracy. This methodology combines rapid detection capability, non-destructive nature, and compatibility with V2G applications, providing new perspectives for enhancing lithium-ion battery longevity and lithium-plating detection. Full article

This paper presents the results of the photometric and spectral monitoring of the galaxy NGC 3516, which is an active galactic nucleus (AGN) of type Sy $1.5$ with a changing look. Observations were carried out at the Fesenkov Astrophysical Institute (FAI, Almaty, Kazakhstan) and the Shamakhy Astrophysical Observatory (ShAO, Shamakhy, Azerbaijan). Spectral monitoring of this galaxy in the wavelength range 4000–7000 Å began in 2020, while photometric observations have been conducted since 2014. During the observation period, estimates of the galaxy’s brightness in the B, V and $Rc$ filters were obtained, as well as measurements of the emission line and continuum fluxes. The light curve shows increased brightness of NGC 3516 in 2016 and 2019. The increase of emission line fluxes of H$\beta$ and H$\alpha$ and continuum began in 2019 and continued until spring 2020, when these characteristics reached their maximal values. A powerful X-ray flare took place on 1 April 2020. A new phase of brightening began in 2021 and has continued until 2025. After reaching their maxima in 2020, the emission fluxes of H$\beta$ and H$\alpha$ decreased by a factor of 1.5–2 and remained at a low level until 2022–2023, when they began to increase again. Medium-resolution spectra obtained on 20 April 2020, with the 1-meter “West” telescope (TSHAO) were used to study the broad components of the H$\beta$ and H$\alpha$ emission line profiles. Model calculations showed that the broad profile of the H$\alpha$ line consists of a central unshifted component and two (blue and red) components shifted symmetrically relative to the central component by a velocity of $v=980\pm 20$ km ${\mathrm{s}}^{-1}$. The H$\beta$ emission line was relatively weak, so the radial velocity of its components was determined with a large uncertainty: $900\pm 600$ km ${\mathrm{s}}^{-1}$. Full article

This work employs a deep learning method to develop a high-precision model for predicting the breakdown voltage (V_br) and forward I-V characteristics of silicon carbide Schottky barrier diodes (SiC SBDs). The model significantly reduces the testing costs associated with destructive experiments, such as breakdown voltage testing. Although the model requires a certain amount of time to establish itself, it supports linear variations in related variables once developed. A predicted model for V_br with an accuracy of up to 99% was successfully developed using 600 sets of input data after 200 epochs of training. After training for 1000 epochs, the deep learning-based model could predict not only point values like V_br but also curves, such as forward I-V characteristics, with a mean squared error (MSE) of less than 10⁻³. Our research shows the applicability and high efficiency of introducing deep learning into device characteristic prediction. Full article

Background: Elderly patients infected with SARS-CoV-2 are at higher risk of developing cytokine storm and severe outcomes; however, specific immunological and proteomic biomarkers for early prediction remain unclear in this vulnerable group. Methods: We enrolled 182 elderly COVID-19 patients from the Chinese PLA General Hospital between November 2022 and April 2023, categorizing them based on progression to respiratory failure requiring mechanical ventilation (defined as severe progression). Olink proteomic analysis was performed on admission serum from 40 propensity score-matched samples, with differentially expressed proteins (DEPs) validated by cytometric bead array (CBA) in 178 patients. To predict severe progression, a model was developed using a 70% training set and validated on a 30% validation set. LASSO regression screened features followed by logistic regression and receiver operating characteristic (ROC) analysis to optimize the model by incrementally incorporating features ranked by random forest importance. Results: Elderly patients progressing to severe COVID-19 exhibited early immune dysregulation, including neutrophilia, lymphopenia, monocytopenia, elevated procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), neutrophil-to-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII), as well as coagulation dysfunction and multi-organ injury. Proteomics identified a set of biomarkers, including tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), and revealed disruptions in signaling pathways, including the mTOR and VEGF signaling pathways. The optimal predictive model, which incorporated PCT, IL-6, monocyte percentage, lymphocyte count, and TRAIL, achieved an area under curve (AUC) of 0.870 (0.729–1.000) during validation. TRAIL levels negatively correlated with fibrinogen (p < 0.05). Conclusions: Elderly COVID-19 patients with severe progression demonstrate early immune dysregulation, hyperinflammation, coagulation dysfunction, and multi-organ injury. The model we proposed effectively predicts disease progression in elderly COVID-19 patients, providing potential biomarkers for early clinical risk stratification in this vulnerable population. Full article

Water pesticide contamination represents a major threat to ecological systems and public health, particularly in agricultural regions. Although conventional detection methods such as liquid chromatography and electrochemical analysis are highly accurate, they are expensive, require skilled operators, and cannot provide real-time results. This study developed a portable miniaturized electrochemical analysis platform based on cyclic voltammetry (CV) for rapid pesticide detection. The platform was compared with a commercial electrochemical analyzer and yielded similar performance in detecting chlorpyrifos at different concentrations. When ultrapure water was used as the background solution, the total area under the CV curve exhibited a linear correlation (R² = 0.89) with the pesticide concentration, indicating its potential as a characteristic index. When molecularly imprinted polymers were added, the platform achieved a limit of detection of 50 ppm, with the area under the CV curve maintaining a logarithmic linear relationship (R² = 0.98) with the pesticide concentration. These findings confirm the total area under the CV curve as the most reliable characteristic index for pesticide quantification. Overall, the proposed platform offers portability, straightforward operation, cost-effectiveness, and expandability, making it promising for on-site environmental monitoring. By incorporating GPS functionality, the platform can provide real-time pesticide concentration mapping, supporting its use in precision agriculture and water quality management. Full article