Search Results (1,155)

Climate variability is a major driver of belowground microbial assembly, yet its effects on rhizosphere microbiomes in perennial crops remain insufficiently resolved. We investigated how macroclimatic oscillations associated with the El Niño–Southern Oscillation (ENSO) influence bacterial communities in the rhizosphere of Coffea arabica. Using 16S rRNA amplicon sequencing across five sampling campaigns covering El Niño, La Niña, and Neutral phases in the Colombian Andes, together with multivariate and variance-partitioning analyses, we quantified the relative contributions of climatic and edaphic factors to rhizosphere community structure. PERMANOVA across three dissimilarity metrics showed that the ENSO explained 11–17% of β-diversity, exceeding the contribution of intra-annual seasonality (6–12%). Ordination analyses indicated moderate compositional differentiation with considerable overlap among ENSO groups, consistent with gradual community turnover under contrasting hydroclimatic conditions. Rainfall and soil pH emerged as the main edaphic correlates of community composition, although their independent effects were no longer significant after accounting for the ENSO phase and season. Despite these shifts, the rhizosphere remained dominated by Acidobacteriota, Actinobacteriota, and Proteobacteria, and a prevalence-defined core microbiome (genera detected in ≥85% of samples) was maintained across climatic phases and seasons. These results indicate that, within the explained fraction of variation, macroclimatic variability contributed more to rhizosphere bacterial turnover than local edaphic heterogeneity, while a conserved prevalence-defined bacterial core may contribute to taxonomic stability in climate-sensitive coffee systems. Full article

Carbon quantum dots (CQDs) and stimuli-responsive hydrogels are advanced functional materials whose hybridization yields CQD-enhanced stimuli-sensitive hydrogels, opening new interdisciplinary avenues for smart material applications. This review systematically summarizes the latest advances in these composites, focusing on synthetic strategies, structure–property modulation mechanisms, and practical applications. Distinct from existing reviews that either investigate CQDs or hydrogels independently or discuss their composites in a single research field, this work features core novelties in integration strategy, application scope and critical analysis: it systematically compares the advantages, limitations and applicable scenarios of three typical CQD–hydrogel integration approaches (physical entrapment, in situ synthesis, covalent conjugation), comprehensively covers the multi-field application progress of the composites and conducts in-depth cross-field analysis of their common scientific issues and technical bottlenecks. By incorporating CQDs, the composites achieve remarkable performance optimizations: 40% improved mechanical toughness, sub-ppm-level heavy metal-sensing sensitivity, and over 80% organic dye photocatalytic degradation efficiency, addressing pure hydrogels’ inherent limitations of insufficient strength and single functionality. These enhancements enable sophisticated applications in biomedical field (real-time biosensing, controlled drug delivery), environmental remediation (pollutant detection/degradation), energy storage, and flexible electronics. The synergistic interplay between CQDs and hydrogels facilitates precise single/multi-stimulus responsiveness (pH, temperature, light), a pivotal advance for precision medicine and intelligent environmental monitoring. Despite promising progress, the large-scale practical application of CQD–hydrogel composites still faces prominent challenges: the difficulty in scalable fabrication with the uniform dispersion of CQDs in hydrogel matrices, poor long-term stability of most composites under physiological cyclic stress (service life < 6 months in practical tests), and low accuracy in discriminating multi-stimuli in complex real-world matrices. Future research should prioritize biomass-based eco-friendly CQD synthesis, machine learning-aided multimodal responsive systems, and 3D bioprinting for scalable manufacturing. Full article

Background: This study evaluated the independent predictive value of preoperative Systemic Immune–Inflammation Index (SII) for postoperative wound infection (WI) in diabetic patients undergoing isolated Coronary Artery Bypass Grafting (CABG). Methods: A retrospective cohort of 300 diabetic patients (2024–2025) was analyzed. The primary outcome was 30-day postoperative WI. Preoperative SII was calculated from blood counts within 24 h before surgery. Multivariable logistic regression was performed using both a primary model (adjusting for age, BMI, and comorbidities) and an extended model including glycemic control (HbA1c), smoking status, operative duration, and transfusion requirements. Model discrimination was evaluated via Area Under the ROC Curve (AUC). Statistical power and sensitivity analyses were conducted to ensure the robustness of the findings. Results: WI occurred in 7% (n = 21). Preoperative SII was significantly lower in the WI group (958.48 ± 493.49 vs. 1293.56 ± 758.15, p = 0.047). SII remained an independent predictor in the adjusted model (Adjusted OR per 100-unit increase: 0.93; 95% CI: 0.86–1.00; p = 0.048). ROC analysis confirmed an inverse predictive pattern (AUC: 0.374, 95% CI: 0.312–0.436). Comparative analysis showed that SII provided superior additional insight compared to NLR and PLR in this population. Conclusions: Preoperative SII is an independent predictor for WI in diabetic CABG patients. However, given the modest discriminative performance (AUC: 0.374), it should be integrated into a broader clinical risk assessment. Contrary to conventional expectations, lower SII values indicated increased susceptibility, suggesting that immune exhaustion rather than hyperinflammation may drive infectious risk in diabetic patients. Full article

Sol–gel-based optical functional sensor coatings were developed for real-time monitoring of multiphase saponification reactions in microreactors. Various pH-sensitive indicator mixtures, including bromocresol green and bromocresol purple (BCG and BCP) and methyl red–methyl orange, were incorporated into sol–gel coatings and evaluated on test plates across pH range of 2–12. Coatings with BCG and BCP 1:3 demonstrated the most pronounced color change at high pH (11–12), with distinct hue (H) transitions providing a reliable measure of local pH. These optimized coatings were integrated into microreactor channels to track the passage of oil and NaOH slugs under varying flow rates. Hue analysis produced reproducible plateaus corresponding to NaOH-rich (H = 50°) and oil-rich (H = 41°) phases, enabling droplet-level resolution of slug flow and detection of flow-regime transitions. The sensor response was fully reversible, highlighting the robustness and reusability of the coatings. Unlike previous high-resolution fluorescence-based systems, this approach relies on simple visible-light imaging and low-cost data extraction, leaving the reaction chemistry unaltered. The results demonstrate that sol–gel coatings coupled with hue-based analysis provide a practical, noninvasive, and real-time monitoring strategy for multiphase reactions in microreactors, with potential for implementation in industrial or IoT-enabled process control systems. Full article

Owing to the high stability and low cost of nanozymes, they have been extensively investigated and reported. In this work, highly active CeO₂ nanoflowers were first prepared and then different metal elements were doped into the CeO₂ nanoflower matrix via a novel synthesis method to fabricate M-CeO₂ (M = Cu, Fe, Co, Mn, La) nanomaterials. Mn-CeO₂ with the highest peroxidase-like activity was selected via systematic screening, the as-prepared Mn-CeO₂ nanocomposites exhibited enhanced enzyme-like activity due to the strong metal-support interaction. This article explored the effects of doping ratio, pH, temperature, reaction time, and material concentration on its activity. A simple sensitive and selective colorimetric method was established and successfully used to detect hydrogen peroxide and ascorbic acid sensitively. When the hydrogen peroxide (H₂O₂) concentration is within the 2.0–120.0 μM range, the UV-visible absorbance at 652 nm was associated linearly with the H₂O₂ concentration, R² = 0.9959, LOD = 1.7 μM (S/N = 3). The absorbance of the reaction system showed a good linear relationship with the ascorbic acid (AA) concentration (1.0–40.0 μM, R² = 0.992), LOD = 0.98 μM (S/N = 3). This study provides an effective way to construct efficient nanozymes and their potential applications in sensing and detection. Full article

Background: Hypertension (HT) and atrial fibrillation (AF) are frequently coexisting conditions, with inflammation being a crucial pathophysiological mechanism shared by both. The systemic immune-inflammation index (SII), a newly introduced combined inflammatory marker, includes the parameters of platelets, neutrophils, and lymphocytes. Current literature lacks studies investigating the relationship between SII and newly developed AF in patients with controlled HT. Therefore, this study aimed to explore the association of SII with AF in hypertensive patients on anti-hypertensive therapy. Methods: This retrospective case–control study included 68 hypertensive patients with controlled blood pressure who underwent 24 h Holter monitoring. Patients were divided into two groups based on the presence of atrial fibrillation (AF-positive, n = 32; AF-negative, n = 36). SII values were calculated from complete blood counts. Demographic, clinical, and laboratory characteristics were compared between groups. Correlation analysis was performed to assess associations of SII with clinical parameters. Multivariable logistic regression identified independent predictors of AF. Optimal cut-off values for systemic immune-inflammation index and age were determined through receiver operating characteristic analysis. Results: Patients with AF were significantly older than those without AF (p < 0.01). The systemic immune-inflammation index was significantly higher in patients with atrial fibrillation (p = 0.02). Patients with AF also had higher heart rates (p = 0.04) and creatinine levels (p < 0.01). SII showed a moderate positive correlation with C-reactive protein (CRP) (r = 0.393, p < 0.01) and a weak correlation with heart rate (r = 0.251, p = 0.039). In multivariable analysis, SII (OR: 1.024, p = 0.04) and age (OR: 1.130, p < 0.01) was independently associated with AF. Receiver operating characteristic analysis determined an SII cut-off of 483.0 with a sensitivity of 53.1% and specificity of 52.8%. The age cut-off was found to be 63 years with sensitivity and specificity being 62.5% and 66.7%, respectively. Conclusions: The systemic immune-inflammation index was significantly elevated in newly diagnosed AF patients with well-controlled hypertension and was an independent predictor of AF. It is a simple, readily available biomarker that may assist in identifying hypertensive patients at high risk for the development of AF. These results should be validated in future studies, and the role of inflammation in the pathogenesis of atrial fibrillation should be further explored. Full article

The review describes advances in stimulus-sensitive carriers for chemotherapy of various organs, since selectivity in cytotoxicity against cancer and normal cells is a key factor in effective cancer treatment. Special attention is devoted to particle carriers composed of natural compounds, such as lipids, phospholipids, oligopeptides, and synthetic macromolecules, that are sensitive to internal or external stimuli, and delivered to targeted body tissue in a controlled manner. The stimuli discussed include the following: temperature, pH, enzymes, electromagnetic radiation, ultrasound, and redox potential. The description of stimulus-sensitive drug delivery, the methods for synthesizing polymers and copolymers, and the preparation of nano- and microparticles are briefly presented. A description of drug delivery systems (DDSs) with controlled release to specific organs, such as the breast, intestine, lung, prostate, etc., is preceded by a description of methods for preparing drug carriers. The review also covers DDSs at various stages of preclinical and clinical trials and summarizes the state of knowledge on this subject. Full article

Polymer–drug conjugates (PDCs) represent an advanced drug delivery strategy designed to address critical limitations of conventional therapeutics, including poor water solubility, rapid systemic clearance, and off-target toxicity. By covalently linking therapeutic agents to polymeric carriers through rationally designed linkers, PDCs enable improved pharmacokinetic profiles, enhanced stability, and controlled drug release. This review provides a comprehensive overview of the key design principles governing PDC systems, with a particular focus on the role of biofunctional polymers. Essential parameters for polymer selection, including biocompatibility, biodegradability, molecular weight, and functional group availability, are discussed in relation to their influence on drug loading, release kinetics, and biological performance. In addition, both natural and synthetic polymers are evaluated for their ability to improve solubility, modulate biodistribution, and reduce systemic toxicity. An overview of stimuli-responsive PDCs is provided, including pH-, redox-, and temperature-sensitive systems, which enable site-specific and spatiotemporally controlled drug release in response to pathological microenvironments. We emphasize the special role of bioactive polymers such as poly-lysine, hyaluronic acid, chitosan, and gelatin for their intrinsic biological activity, including receptor-mediated targeting, antimicrobial activity, and synergistic therapeutic effects. These properties support the development of dual-active conjugates with enhanced specificity and efficacy. Overall, this review underscores the transition of polymers from passive carriers to active therapeutic components and outlines current challenges and future perspectives for the clinical translation of next-generation PDCs. Full article

Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article

Background/Objectives: Multimodal depression detection research has traditionally relied on early or hybrid fusion strategies without systematically analyzing how dynamic fusion mechanisms interact with modality-specific pretraining. Although gated and attention-based architectures are increasingly adopted, their behavior is rarely examined within a structured fusion taxonomy framework. Methods: In this study, we conduct a controlled taxonomy-level evaluation of multimodal fusion strategies in a Japanese PHQ-9-annotated depression dataset. We compare four fusion paradigms (concatenation, summation, gated fusion, and cross-attention) across three integration stages, crossed with modality-specific affective pretraining configurations for visual (CMU-MOSI/MOSEI), acoustic (JTES), and textual (WRIME) encoders, yielding 512 experimental conditions. Results: The results reveal strong position-dependent effects of fusion strategy. Cross-attention fusion at the audio integration stage achieved the highest mean AUC (0.774) and PR-AUC (0.606), with statistically significant superiority over gated and concatenation-based fusion (Kruskal–Wallis $H=86.28$, $p<0.001$). In contrast, fusion effects at the text stage were non-significant in AUC but significant in PR-AUC, highlighting metric-sensitive behavior under class imbalance. Pretraining effects were modality-specific: SigLIP initialization produced significant positive transfer $(\mathrm{\Delta }=+0.018, p<0.001)$, whereas audio pretraining on JTES resulted in negative transfer $(\mathrm{\Delta }=-0.014, p=0.004)$, suggesting domain mismatch effects. Gate analysis further revealed condition-dependent modality dominance, including cases of semantic–geometric reversal under joint auxiliary augmentation. Conclusions: Our findings suggest that multimodal depression detection systems should not be interpreted through static fusion categories alone. Instead, modality contribution appears to be associated with structured interaction effects between fusion strategy, integration position, and affective pretraining. This work provides a controlled empirical bridge between fusion taxonomy and dynamic modality weighting in clinical multimodal modeling. Full article

A dual-mode electrical–optical nanocomposite hydrogel is developed by integrating carboxyl-modified upconversion nanoparticles (UCNPs-COOH) and quaternized chitosan (CQAS) into a polyacrylamide (PAAm) covalent network. The hydrogel exhibits high optical transparency (>90% in the visible region), excellent mechanical properties (fracture strain of 1742%, tensile strength of 0.85 MPa, toughness of 6.57 MJ/m³), and robust adhesion to various substrates. The synergistic covalent–noncovalent hybrid network enables efficient energy dissipation, while CQAS-enhanced dispersion of UCNPs significantly improves upconversion luminescence intensity and stability, as evidenced by prolonged fluorescence lifetime from 0.564 ms to 0.691 ms at 539 nm. Leveraging distinct electrical and optical signal transduction pathways, the hydrogel functions as a highly sensitive resistive strain sensor with multistage gauge factors up to 13.85 and excellent cyclic stability over 1200 loading–unloading cycles at 100% strain for human motion monitoring. It also serves as a ratiometric optical pH sensor over a broad range (pH 1–13) based on phenolphthalein-sensitized upconversion luminescence, with excellent repeatability. By integrating real-time resistance responses with optical readouts within a single soft material, this work demonstrates a reliable dual-mode sensing strategy for simultaneous mechanical and chemical monitoring, holding promise for wearable electronics, smart healthcare, and environment-responsive sensing systems. Full article

Accurate absolute pressure measurement is of great importance in industrial control, environmental monitoring, and aerospace. Traditional fiber-optic Fabry–Perot (F-P) pressure sensors usually involve complex microfabrication and high-cost demodulation systems, while conventional diaphragm capsule sensors are limited in sensitivity and resolution. This work presents a low-cost, high-resolution fiber-optic F-P absolute pressure sensor. The sensor uses a vacuum capsule as one reflective surface and a partially reflective fiber collimator as the other, forming a low-finesse F-P interferometer. The cavity length is linearly modulated by the elastic deformation of the capsule under pressure, and high-precision demodulation is realized using frequency modulated continuous wave (FMCW) interferometry instead of conventional spectral methods. Static experiments from 10 to 110 kPa show that the sensor exhibits a high sensitivity of 15,105 nm/kPa and a resolution of 3.3 Pa. Furthermore, the sensor operates normally within the range of −20 °C to 70 °C, exhibiting a pressure–temperature cross-sensitivity of 0.081 kPa/°C and a cavity length drift of 496 nm/h. With the advantages of high performance, simple structure, low cost, and good scalability by selecting different capsules, the proposed sensor has promising potential for practical applications in pressure measurement fields. Full article

Arsenic (As) is a ubiquitous and highly toxic metalloid with well-established carcinogenicity. Its accumulation and secondary release from lake sediments pose potential risks to lake ecosystem integrity and human health. Meanwhile, the ongoing intensification of lake eutrophication at the global scale has altered the sources, composition, and environmental behavior of internally derived dissolved organic matter (DOM). These changes have profoundly influenced As mobilization and transformation at the sediment-water interface (SWI). To advance understanding of the regulatory roles and underlying mechanisms of algal dissolved organic matter (ADOM) and submerged macrophyte dissolved organic matter (SMDOM) in As biogeochemical cycling under lake ecosystem regime shifts, extensive findings from the international literature were synthesized. The characteristic properties and environmental behaviors of ADOM and SMDOM were systematically compared, and their distinct regulatory pathways in lacustrine systems were further summarized. Results indicate that ADOM is typically characterized by low molecular weight, weak aromaticity, and high bioavailability. It can enhance As dissolution and mobilization from sediments through direct complexation, competition for adsorption sites, and stimulation of microbial metabolism and Fe(III) reduction. In contrast, SMDOM exhibits higher molecular weight, greater aromaticity, and a higher degree of humification. It tends to form stable complexes with mineral phases. Under the influence of radial oxygen loss (ROL) from submerged macrophyte roots during the growth phase, its capacity to promote mineral reduction is relatively limited. This process favors stable As retention in sediments. The regulatory effects of ADOM and SMDOM on As behavior are strongly modulated by environmental factors such as pH, redox potential (Eh), temperature, and light conditions, as well as by microbial communities. ADOM is more sensitive to reducing environments and photochemical processes. SMDOM, in contrast, exerts more persistent control under oxidizing conditions and at mineral-water interfaces. In addition, ADOM more readily drives microbial community shifts toward assemblages with enhanced capacities for Fe(III) reduction and As reduction or methylation. SMDOM is less likely to trigger strongly reducing processes. Based on these mechanisms, the outbreak and decay phases in algal-dominated lakes often correspond to critical periods of enhanced As mobilization and elevated ecological risk. In submerged macrophyte-dominated lakes, the decay phase may represent an important window for sedimentary As release. Finally, a conceptual framework describing the differential regulation of As biogeochemical cycling by ADOM and SMDOM is proposed. This framework provides a theoretical basis for As risk identification, the determination of critical risk periods, and the development of management strategies across lakes with different trophic states. Full article

Objective: This prospective study investigated the use of H-scan ultrasound (US) imaging as a novel component of a multiparametric radiomic analysis framework for characterizing human breast cancer response to neoadjuvant chemotherapy (NAC) before and early after treatment initiation. Methods: Thirty breast cancer patients scheduled for NAC were scanned using a clinical US system (Logiq E9, GE HealthCare) equipped with a 9L-D linear array transducer. Radiofrequency (RF) data was obtained at baseline (pre-NAC) and after 10% and 30% of the complete dose of chemotherapy. The RF data was analyzed by a bank of 256 frequency-shifted bandpass filters to form H-scan US frequency images. Grayscale texture features were extracted from both B-scan and H-scan US images. In addition, US attenuation coefficient and speckle statistics based on the Nakagami and Burr distributions were estimated from the RF data. Data classification of tumor and peri-tumoral regions was performed using a novel three-dimensional (3D) score map based on support vector machine (SVM) modeling. Unlike conventional classifiers that report only a single prediction score, a 3D score map provides a visual representation of the classifier decision space, enabling interpretation of class separation and treatment-induced shifts in multiparametric US measurements. Results: The dataset was split into 10 disjoint partitions (90% training, 10% testing) to compute area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy measures. Actual patient response to NAC was assessed at surgery and categorized as either pathologic complete response (pCR) or non-pCR. Multiparametric US and data classification results at pre-NAC found AUC values of 0.78 after using only tumor information (p < 0.01), which increased to 0.81 with inclusion of peri-tumoral information (p < 0.01). Significant differences in multiparametric US measures from both cancer response types was found after integration of patient data collected at 10% completion of the NAC regimen (i.e., first NAC cycle), yielding an improved AUC of 0.86 (p < 0.001). Conclusions: Multiparametric US imaging with radiomic features from both the tumor and peri-tumoral regions is a promising noninvasive approach for monitoring early breast cancer response to NAC. Full article

Hypoxia activates hypoxia-inducible factors (HIFs), which regulate genes involved in erythropoiesis, angiogenesis, and metabolism. HIF stability is controlled by oxygen-dependent HIF prolyl 4-hydroxylases (HIF-P4Hs). Pharmacological HIF-P4H inhibitors are approved for the treatment of anaemia in chronic kidney disease (CKD). Beyond erythropoiesis, these drugs have been linked to improved lipid profiles in CKD, and preclinical studies suggest benefits for glucose tolerance and cardiovascular protection. However, cardiometabolic effects of HIF-P4H inhibitors have not been systematically examined in healthy or non-anaemic individuals. This investigator-initiated, double-blind, placebo-controlled, randomised crossover trial evaluates the systemic effects of roxadustat, an orally administered pan-HIF-P4H inhibitor. The study consists of two 10-day study arms separated by a minimum 4-week washout. Participants receive 70 mg of roxadustat or a placebo thrice a week. The primary hypothesis is that roxadustat lowers plasma total cholesterol. Secondary outcomes include changes in LDL cholesterol, triglycerides, insulin sensitivity, glucose tolerance, body composition, 24 h blood pressure, exercise capacity, autonomic cardiovascular regulation, and skeletal muscle microcirculation. Healthy volunteers (n = 24) aged 18–40 years will be enrolled. This study will provide insights into the potential of HIF-P4H inhibitors for obesity, dyslipidaemia, insulin resistance, and hypertension, and may inform future therapeutic strategies for metabolic syndrome, type 2 diabetes, and cardiovascular disease. Full article