Search Results (7,618)

Anthracnose is one of the major diseases affecting Dalbergia odorifera T. Chen. However, the soil microbial mechanisms underlying D. odorifera responses to anthracnose remain largely unexplored. This study investigated three planting systems: a Dalbergia odorifera monoculture (J); a mixed plantation of D. odorifera and Pterocarpus macrocarpus (JD); and a composite mixed plantation of D. odorifera, P. macrocarpus, and Clinacanthus nutans (JDY). Using amplicon sequencing technology for soil microbial analysis and combining soil physical and chemical properties with disease severity, we comprehensively analyzed changes in soil microbial community structure and function across different planting modes. The results showed that the diverse mixed mode (JD, JDY) significantly improved soil physicochemical properties and promoted soil nutrient cycling. Redundancy analysis (RDA) indicated that soil organic matter (SOM) and disease severity, quantified by the area under the disease progress curve (AUDPC), were the primary environmental drivers of microbial community variation. Genera positively correlated with SOM and negatively correlated with AUDPC were significantly enriched in JDY and JD, whereas genera showing opposite relationships were predominantly enriched in J. Functional predictions revealed enhanced nutrient-cycling capacities in JD and JDY, with JDY uniquely harboring functional groups such as Arbuscular Mycorrhizal, Epiphyte, and Lichenized taxa. In contrast, microbial functions in the J plantation were mainly limited to environmental amelioration. Co-occurrence network analysis further showed that as planting patterns shifted from J to JDY, microbial communities evolved from competition-dominated networks to cooperative defensive networks, integrating efficient decomposition with strong pathogen suppression potential. The study demonstrates that complex mixed planting systems regulate soil properties, enhance the enrichment of key functional microbial taxa, reshape community structure and function, and ultimately enable ecological control of anthracnose disease. This study provides new perspectives and theoretical foundations for ecological disease management in plantations of rare tree species and for microbiome-based ecological immunization strategies. Full article

Background: Drug-induced cardiotoxicity poses a major challenge in drug development and clinical safety. Although machine learning (ML) methods have shown potential in predicting cardiotoxic risks, prior research has largely focused on specific mechanisms such as human Ether-à-go-go-Related Gene (hERG) inhibition. This scoping review systematically examined studies applying ML models to predict a broad range of drug-induced cardiotoxicity outcomes. Methods: A systematic search of PubMed, EMBASE, SCOPUS, and Web of Science identified studies developing ML models for cardiotoxicity prediction. Extracted data included sources, feature types, algorithms, and performance metrics, categorized by evaluation method (training, testing, cross-validation, or external validation). Results: Twenty-five studies met inclusion criteria, addressing outcomes such as arrhythmia, cardiac failure, heart block, hypertension, and myocardial infarction. Structured resources such as SIDER (Side Effect Resource) were the most common data sources, with features including molecular descriptors, fingerprints, and occasionally, target-based or transcriptomic data. Support vector machines (SVM) and random forest (RF) were the most common algorithms, showing robust predictive performance, with externally validated area under the receiver operating characteristic curve (AUC-ROC) values above 0.70 and accuracy exceeding 0.75 in several studies. Despite variability and limited external validation, ML approaches demonstrate substantial promise for predicting diverse cardiotoxic outcomes. Conclusions: This review underscores the importance of integrating heterogeneous data and rigorous validation for improving cardiotoxicity prediction. Full article

Vascular Smooth Muscle Cells (VSMC) dysfunction is a major contributor to Type 1 diabetes (T1D)-associated vascular complications. Ca²⁺ is a key messenger responsible for maintaining VSMC tone and function, and alterations in its cytosolic levels are central to diabetes-related vasculopathy. Heat Shock Protein 70 (HSP70), a multifaceted chaperone present intracellularly (iHSP70), regulates vascular reactivity by supporting Ca²⁺ handling, and extracellularly (eHSP70) activates immune signaling. Disruption of eHSP70/iHSP70 balance has been implicated in T1D-associated VSMC dysfunction. Curcumin, a phytochemical found in turmeric, is an emerging therapeutic adjuvant for treating a wide range of pathologies, including diabetes. However, whether curcumin modulates Ca²⁺ dynamics and HSP70 expression, thereby improving VSMC function, in diabetic aorta remains unclear. To investigate this, Streptozotocin-induced diabetic rats (i.p. 65 mg/kg) were treated with curcumin (300 mg/kg) for 28 days. Vascular function was evaluated using wire myography to assess changes in biphasic contraction curve and Ca²⁺ dynamics, while HSP70 was quantified using Western blotting and ELISA. Structural alterations were analyzed by assessing collagen and elastin using Picrosirius staining and fluorescence microscopy. Chronic curcumin treatment improved vascular function by normalizing Ca²⁺ mishandling, restoring the eHSP70/iHSP70 ratio, reducing hypercontractility, and mitigating arterial structural alterations. These findings indicate that curcumin could potentially ameliorate diabetes-related VSMC dysfunction by restoring Ca²⁺ homeostasis and modulating HSP70. Full article

This study presents a comprehensive atomistic investigation of the structural, mechanical, and thermal properties of Pd₆₀Ir_nRh_19−n trimetallic nanoclusters adopting a truncated octahedral geometry. The compositional evolution of chemical ordering, local pressure distributions, and melting behavior was systematically analyzed using Gupta potential-based basin-hopping global optimization. The accuracy of the Gupta potential predictions was further validated for all configurations using density functional theory (DFT) calculations. The surface layer consisted solely of Pd atoms and was held constant throughout the study. Meanwhile, Ir and Rh atoms were distributed within the 19-atom core region, allowing a detailed evaluation of how variations in core composition affect the energetic and thermal stability of the clusters. The Pd₆₀Ir₆Rh₁₃ configuration exhibits the minimum value of mixing energy, corresponding to the most symmetric and energetically stable atomic arrangement. Local pressure analyses showed that Ir incorporation enhances internal compressive stress and induces tensile relaxation on the Pd surface, achieving an optimal strain balance at n = 6. Melting analyses based on caloric curves and Lindemann indices revealed a non-monotonic dependence of melting temperature on Ir content, with Ir-rich clusters displaying the highest thermal resistance and Rh-rich systems showing reduced stability. These findings clarify how Ir/Rh distribution governs the energetic, mechanical, and thermal response of Pd–Ir–Rh nanoalloys, offering a coherent atomistic framework for understanding their composition-dependent stability. Full article

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

The Bactrocera minax (Enderlein) (Diptera: Tephritidae) and Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae) are the only members of the subgenus of the Tetradacus of Bactrocera. They share nearly identical morphological characteristics and occupy highly overlapping ecological niches, specifically harming citrus crops and causing substantial damage to citrus production in China. To determine the suitable habitat of the two pests and how the citrus coverage affects this distribution. This study employed the Maximum Entropy model (MaxEnt) to predict the potential geographic distributions (PGDs) of B. minax and B. tsuneonis under current and future climate scenarios, using species occurrence data and key environmental variables. The result indicate that the MaxEnt model performed well, with an area under the curve value (AUC) of 0.969. The citrus distribution index, precipitation of driest month (BIO 14), min temperature of coldest month (BIO 6), and elevation were identified as the primary environmental factors affecting their PGDs. The PGDs for these pests are mainly concentrated in southern China, where citrus is extensively cultivated. Guizhou and Hunan identified as the most significant high-suitability habitat. The projected distribution of B. minax and B. tsuneonis show minimal changes under the future climate conditions estimated by the MaxENT model. However, under global warming scenarios, their PGDs are projected to gradually shrink, although eastern Sichuan remains at high risk of invasion by B. tsuneonis. Prevention, quarantine, and control measures for B. tsuneonis require continued attention. The findings of this study offer a more robust theoretical basis for the targeted monitoring and control of B. minax and B. tsuneonis in China. Full article

Chrysanthemum, a traditional medicinal and edible plant, possesses diverse health-promoting properties attributed to its rich profile of bioactive compounds. However, the intrinsic quality, influenced by the composition of fundamental components like starch and lignin, varies significantly across different cultivars and origins. This study establishes a comprehensive phytochemical profile of 12 representative Chinese chrysanthemum cultivars by systematically quantifying their starch and lignin contents. Furthermore, it develops and validates a novel, low-cost rapid detection method for starch utilizing smartphone-based colorimetry. The starch content, determined by a colorimetric anthrone-sulfuric acid assay, ranged from 2.68 to 18.69 g/100 g, while the lignin content, measured via the acetyl bromide digestion followed by UV spectrophotometry at 280 nm, varied from 4.21 to 13.63 g/100 g, revealing substantial inter-cultivar differences. For starch analysis, a low-cost, immediate, general-purpose, and high-throughput (LIGHt) smartphone-based colorimetry was implemented. Standard curves constructed from both absorbance and the LIGHt assay demonstrated excellent linearity (R² > 0.99). The method’s performance was evaluated under different lighting conditions and across various smartphone models. The UV spectrophotometry condenses lignin quantification to a single 30-min digestion–reading cycle, bypassing the two-day Klason protocol and increases efficiency greatly. The work successfully provides a foundational component analysis and validates a portable, high-throughput framework for on-site quality control of plant-based products, demonstrating the strong potential of smartphone-based colorimetry for rapid starch detection and a complementary laboratory-scale lignin assay. Full article

The coffee leaf miner (Leucoptera coffeella) is a major pest of coffee crops and can cause significant economic losses. Early monitoring is essential to support decision-making for its control. This study aimed to evaluate the potential of fuzzy logic for detecting leaf miner infestation using a 2.5-year historical series of Sentinel-2A satellite images processed on the Google Earth Engine platform. Field monitoring of coffee leaf miner infestation was carried out at the EPAMIG Experimental Field, located in São Sebastião do Paraíso, Minas Gerais, Brazil. The period evaluated was from September 2022 to April 2025. Vegetation indices were calculated using the Google Earth Engine platform, and a database was built with eight indices (NDVI, EVI, GNDVI, SR, IPVI, NDMI, MCARI, and CLMI) along with coffee leaf miner infestation data. Principal Component Analysis (PCA) was applied to reduce data dimensionality and identify the most relevant indices for distinguishing infested from healthy plants, explaining 90.9% of the total variance in the first two components (PC1 and PC2). The indices CLMI, IPVI, GNDVI, and MCARI showed the greatest contribution to class separation. A fuzzy inference model was implemented based on the mean index values and validated through performance metrics. The results indicated an overall accuracy of 79.1%, a sensitivity (recall) of 86.6%, a specificity of 66.6%, an F1-score of 0.838, a Kappa coefficient of 0.545, and an area under the curve (AUC) of 0.766. These findings confirm the potential of integrating orbital spectral data via Google Earth Engine with fuzzy logic analysis as an efficient tool, contributing to the adoption of more sustainable monitoring practices in coffee farming. The fuzzy logic system received as input the spectral values derived from Sentinel-2A imagery, specifically the indices identified as most relevant by the PCA (CLMI, IPVI, GNDVI, and MCARI). These indices were computed and integrated into the inference model through processing routines developed in the Google Earth Engine platform, enabling a direct connection between satellite-derived spectral patterns and the detection of coffee leaf miner infestation. Full article

The oral cavity acts as the anatomical gateway to the respiratory tract, sharing both microbiological and pathophysiological links with the lower airways. Although radiotherapy is a cornerstone treatment for lung cancer, reliable oral microbiome biomarkers for predicting patient outcomes remain lacking. We analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls across discovery and two validation cohorts via 16S rRNA sequencing. Healthy controls exhibited a significantly higher abundance of Streptococcus compared to patients (p = 0.049, p < 0.001, p < 0.001, respectively). The structure of the microbial community exhibited substantial dynamic changes during treatment. Responders showed enrichment of Rothia aeria (p = 0.027) and Prevotella salivae (p = 0.043), associated with prolonged overall survival (OS) and progression-free survival (PFS), whereas non-responders exhibited elevated Porphyromonas endodontalis (p = 0.037) correlating with shorter OS and PFS. According to Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2) analysis, Akkermansia and Alistipes were nearly absent in non-responders, while Desulfovibrio and Moraxella were virtually absent in responders. A diagnostic model based on Streptococcus achieved area under the curve (AUC) values of 0.85 (95% CI: 0.78–0.91) and 0.99 (95% CI: 0.98–1) in the validation cohorts, and a response prediction model incorporating Prevotella salivae and Neisseria oralis yielded an AUC of 0.74 (95% CI: 0.58–0.90). Furthermore, in small cell lung cancer, microbiota richness and diversity were inversely correlated with Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.008, p < 0.001, respectively) and pro-gastrin-releasing peptide (ProGRP) levels (p = 0.065, p = 0.084, respectively). These results demonstrate that lung cancer-associated oral microbiota signatures dynamically reflect therapeutic response and survival outcomes, supporting their potential role as non-invasive biomarkers for diagnosis and prognosis. Full article

Background: To meet the urgent need for novel antibacterial agents that are active also against worrying superbugs, natural pentacyclic triterpenoids, including totally inactive betulin (BET) and betulinic acid (BA), as well as ursolic acid (UA), active on Gram-positive bacteria, have been chemically modified, achieving compounds 1–7. Methods: Triterpenoid derivatives 1–7 and all synthetic intermediates were characterized by chemometric-assisted FTIR and NMR spectroscopy, as well as by other analytical techniques, which confirmed their structure and high purity. Minimum inhibitory concentration values (MICs) of 1–7, BET, BA and UA were determined by the broth dilution method, using a selection of Gram-positive and Gram-negative clinically isolated superbugs. Results: Performed experiments evidenced that compounds 4–7 had potent antibacterial effects against Gram-positive methicillin-resistant Staphylococcus aureus and S. epidermidis (MRSA and MRSE), as well as against vancomycin-resistant Enterococcus faecalis and E. faecium (VRE). The antibacterial effects of 4–7 were due to the insertion of a triphenyl phosphonium (TPP) group and were higher than those reported so far for other BET, BA and UA derivatives, especially considering the complex pattern of resistance of the isolates used here and their clinical source. Conclusions: For the first time, by inserting TPP, a real activity (MICs 2–16 µg/mL) was conferred to inactive BET and BA (MICs > 1024 and 256 µg/mL). Moreover, the antibacterial effects of UA were improved 16- and 32-fold against MRSE and MRSA (MICs = 2 vs. 32 and 64 μg/mL). Future Perspectives: Based on these very promising microbiologic results, new experiments are currently underway with the best-performing compounds 5 and 7 (MICs = 2 μg/mL) on an enlarged number of Gram-positive isolates, to confirm their MICs. Moreover, investigations about their possible antibiofilm activity, time-killing curves and cytotoxicity on eukaryotic cells will be carried out to define their pharmacological behavior and clinical potential. Full article

Background: Decompressive craniectomy (DC) is a life-saving procedure for severe traumatic brain injury (TBI); however, its long-term outcomes remain controversial. Cranioplasty traditionally performed to restore cranial integrity, has been increasingly recognized for its potential role in neurological recovery. This study aimed to investigate the impact of cranioplasty timing and status on long-term mortality and functional outcomes after DC for severe TBI. Methods: We retrospectively reviewed 151 patients who underwent DC between 2014 and 2018. Patients were categorized into three groups according to cranioplasty timing: early (<3 months), late (≥3 months), and no cranioplasty. Clinical and radiologic data, including the Rotterdam CT scores, were analyzed. The primary endpoints were 5-year mortality and 12-month functional outcome assessed by the Glasgow Outcome Scale (GOS). Univariate and multivariate logistic regression analyses identified independent predictors and receiver operating characteristic (ROC) curves with are under the curve (AUC) values evaluated model performance. Results: Of 151 eligible patients (mean age = 53.9 ± 17.4 years; 82.1% male), overall 5-year mortality was 76.8% (116/151). Mortality differed substantially by cranioplasty group: 64.5% in early cranioplasty, 70.8% in late cranioplasty, and 82.3% in patients who did not undergo cranioplasty. Unfavorable 12-month functional outcomes occurred in 45.2%, 79.2%, and 91.7% of these groups, respectively. In multivariate analysis, no cranioplasty independently predicted both higher 5-year mortality (OR = 2.78, 95% CI = 1.06–7.25, p = 0.038) and unfavorable functional outcome (OR = 3.09, 95% CI = 1.18–8.09, p = 0.022). Older age was also associated with increased mortality (p = 0.019). ROC analysis showed moderate discriminative performance for 5-year mortality (AUC = 0.71) and good discrimination for unfavorable functional outcome (AUC = 0.80). Conclusions: Absence of cranioplasty was associated with higher long-term mortality and poorer functional recovery following DC for severe TBI. Early cranioplasty may enhance cerebral restoration and rehabilitation potential, improving both survival and neurological outcomes. Full article

Objectives: This study utilized artificial intelligence (AI)–based radiomics analysis of computed tomography (CT) images using a modified U–Net for lung nodule segmentation and convolutional neural network based on VGG–16 to predict lymphovascular invasion (LVI) in stage 0–I lung adenocarcinoma. Additionally, the study investigated whether combining radiomics data with serum microRNA (miR)–30d level as a potential biomarker could enhance predictive performance. Methods: A total of 1265 patients who underwent complete resection between 2008 and 2018 were included. AI–based CT analysis was performed, and logistic regression was applied to predict LVI using 35 imaging features. A risk score (RS) generated from 840 patients in the derivation cohort was used to identify a high–risk group, with validation performed using 425 patients. Additionally, 47 cases with extracellular vesicle (EV)–derived miR–30d level data were analyzed to evaluate the value of the integrated approach. Results: Among all the patients, 467 patients (36.9%) were LVI–positive, and LVI was independently associated with poorer overall survival. The receiver operating characteristic curve for LVI based on the RS yielded an area under the curve of 0.899. For LVI prediction, the sensitivity, specificity, and accuracy were 84.8%, 83.7%, and 83.9%, respectively, in the derivation group, and 82.3%, 79.4%, and 80.5%, respectively, in the validation group. The integrated approach with miR–30d enhanced the predictability of LVI, achieving a sensitivity of 93.3%, specificity of 70.5%, and accuracy of 85.1%. Conclusions: AI–based radiomics demonstrated high effectiveness for predicting LVI, with RSs showing broad clinical applications. The addition of EV–derived miR–30d modestly improved predictability. Full article

With global climate warming, reports of glacier lake outburst floods (GLOFs) have become increasingly frequent, highlighting the crucial need for robust GLOF sensitivity assessment methods for disaster prevention and mitigation. A reliable GLOF susceptibility assessment method was developed and applied in the Palong Zangbu River Basin in the Nagqu region of the Tibetan Plateau, integrating Digital Elevation Models (DEMs), glacier data, remote sensing imagery, and field survey data. The assessment evaluated the potential hazard levels of glacier lakes. Between 2000 and 2023, both the number and area of glacier lakes in the basin showed an increasing trend. Specifically, the number of glacier lakes larger than 0.08 km² increased by 32, with an area expansion of 14.17 km², corresponding to a growth rate of 43.95%. Based on the GLOF susceptibility assessment, 15 glacier lakes were identified as potentially hazardous in the study area, with the robustness of the method validated through ROC curve analysis. Therefore, it is recommended to regularly apply this method for GLOF susceptibility assessments in the Palong Zangbu River Basin, updating monitoring data and remote sensing imagery. This research provides valuable insights for GLOF susceptibility assessments in the High Mountain Asia (HMA) region. Full article

Background: Oral squamous cell carcinoma (OSCC) is a highly aggressive neoplasm characterized by grim survival outcomes, despite significant therapeutic advances. Mortality rates (up to 70%) have remained unaltered for decades, predominantly due to profound diagnostic delays. These derive from the asymptomatic nature of the early stages of oral carcinogenesis and the emergence of dysplastic areas in previously benign lesions, acting as the bridge to malignant transformation. Hence, the establishment of reliable salivary biomarkers is crucial for non-invasive OSCC detection, even from the premalignant stage of dysplasia. Based on our previous bioinformatic research identifying stage-specific miRNAs throughout OSCC progression, which yielded miR-34a-5p as the most significant, we aimed to experimentally investigate its role in oral oncogenesis and explore its stage-reflecting biomarker potential for liquid biopsy. Methods: The expression of miR-34a was evaluated using quantitative real-time PCR in saliva samples from 9 patients with oral premalignant dysplastic lesions, 10 patients with OSCC and 10 healthy controls. The diagnostic accuracy of miR-34a expression profiles was assessed using ROC-curve analyses. Results: The expression of salivary miR-34a differed significantly across the studied groups, demonstrating a steep decrease in the presence of epithelial premalignant dysplasia, significant upregulation in OSCC and intermediate levels in normal oral mucosa (p < 0.001). The ROC results indicate strong diagnostic performance for the detection of oral dysplasia (AUC = 0.93; p < 0.001), OSCC (AUC = 0.77; p = 0.01) and excellent accuracy for the discrimination between premalignant and OSCC lesions (AUC = 0.98; p < 0.001). Conclusions: Our findings reveal a state-dependent dysregulation of miR-34a in oral carcinogenesis, suggesting its complex role as a pathogenetic agent that allows for malignant transformation through its diminished expression, and as a secondary reactive mechanism attempting to suppress tumor development. Salivary miR-34a holds great, stage-specific diagnostic potential, thereby reflecting the health state of oral mucosa in real time. Full article

Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes, remains insufficiently characterized. Methods: We conducted a pilot evaluation of a previously developed DL model using 135 chest radiographs from Kangwon National University Hospital. Expert physicians established the reference standard. Model performance was assessed and receiver operating characteristic (ROC) curve and precision recall curve (PRC) analyses were performed. Differences between correctly classified and misclassified cases were examined using Wilcoxon rank-sum and Fisher’s exact tests to explore potential clinical or radiographic contributors to model failure. Results: The model correctly classified 129 of 135 cases. The sensitivity was 96.1% (95% confidence interval (CI): 92.2–98.9%), specificity was 85.7% (95% CI: 42.2–97.7%), positive predictive value (PPV) was 99.2% (95% CI: 96.1–99.9%), negative predictive value (NPV) was 54.5% (95% CI: 25.4–80.8%), balanced accuracy was 90.8%, and F1-score was 0.976. The area under the ROC curve was 0.970 (95% CI: 0.929–1.000) and that under the PRC was 0.727 (95% CI: 0.289–1.000), reflecting substantial uncertainty related to the very small number of incomplete cases (n = 6). No statistically significant differences in clinical or radiographic characteristics were observed between correctly classified and misclassified cases. Conclusions: The DL model performed well in identifying correctly positioned NG tubes but demonstrated limited and unstable performance for detecting incomplete placements. Given the safety implications of misclassification, the model should be used only as an assistive tool with mandatory physician oversight. Larger, multi-center studies with greater representation of incomplete cases are required to obtain more reliable estimates and support safe clinical implementation. Full article