Search Results (905)

Several natural products have been shown to display antiviral activity against the hepatitis B virus (HBV), among a number of other viruses. In a previous study, the hydro-alcoholic extracts (n = 66) of 31 species from the Venezuelan Amazonian rain forest were tested on the hepatoma cell line HepG2.2.15, which constitutively produces HBV. One of the species that exerted inhibitory activity on HBV replication was Senna silvestris. The aim of this study was the bioassay-guided purification of the ethanol fraction of leaves of S. silvestris, which displayed the most significant inhibitory activity against HBV. After solvent extraction and two rounds of reverse-phase HPLC purification, NMR analysis identified salsolinol as the compound that may exert the desired antiviral activity. The purified compound exerted inhibition of both HBV DNA and core HBV DNA. Pure salsolinol obtained from a commercial source also displayed anti-HBV DNA inhibition, with an approximate MIC value of 12 µM. Although salsolinol is widely used in Chinese traditional medicine to treat congestive heart failure, it has also been associated with Parkinson’s disease. More studies are warranted to analyze the effect of changes in its chemical conformation, searching for potent antiviral, perhaps dual agents against HBV and HIV, with reduced toxicity. Full article

Simian foamy virus (SFV) is a retrovirus that infects primates. However, epidemiological studies of SFV are often limited to captive populations. The southeastern Brazilian Atlantic Forest is home to both an endemic, endangered species, Leontopithecus rosalia, and an introduced species, Leontopithecus chrysomelas, to which no data on SFV exist. In this study, we assessed the molecular prevalence of SFV, their viral load, and their phylogenetic relationship in these two species of primates. Genomic DNA was extracted from 48 oral swab samples of L. chrysomelas and 102 of L. rosalia. Quantitative PCR (qPCR) was performed to diagnose SFV infection and quantify viral load. SFV prevalence was found to be 23% in L. chrysomelas and 33% in L. rosalia. No age-related differences in prevalence were observed; however, L. rosalia showed a higher mean viral load (3.27 log10/10⁶ cells) compared to L. chrysomelas (3.03 log10/10⁶ cells). The polymerase gene sequence (213 pb) of L. rosalia (SFVlro) was clustered within a distinct SFV lineage found in L. chrysomelas. The estimated origin of SFVlro dated back approximately 0.0836 million years ago. Our study provides the first molecular prevalence data for SFV in free-living Leontopithecus populations while offering insights into the complex evolutionary history of SFV in American primates. Full article

The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow cytometry, which is expensive and time-consuming, particularly for DSCC analysis. Mid-infrared spectroscopy (MIR) enables qualitative and quantitative analysis of milk constituents with great advantages, being cheap, non-destructive, fast, and high-throughput. The objective of this study is to develop a dairy cattle udder health status diagnostic model of MIR. Data on milk composition, SCC, DSCC, and MIR from 2288 milk samples collected in dairy farms were analyzed using the CombiFoss 7 DC instrument (FOSS, Hilleroed, Denmark). Three MIR spectral preprocessing methods, six modeling algorithms, and three different sets of MIR spectral data were employed in various combinations to develop several diagnostic models for mastitis of dairy cattle. The MIR diagnostic model of effectively identifying the healthy and mastitis cattle was developed using a spectral preprocessing method of difference (DIFF), a modeling algorithm of Random Forest (RF), and 1060 wavenumbers, abbreviated as “DIFF-RF-1060 wavenumbers”, and the AUC reached 1.00 in the training set and 0.80 in the test set. The other MIR diagnostic model of effectively distinguishing mastitis and chronic/persistent mastitis cows was “DIFF-SVM-274 wavenumbers”, with an AUC of 0.87 in the training set and 0.85 in the test set. For more effective use of the model on dairy farms, it is necessary and worthwhile to gather more representative and diverse samples to improve the diagnostic precision and versatility of these models. Full article

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

Background: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC. Methods: This review was conducted under PRISMA 2020 guidelines and included studies from January 2020 to December 2024 that utilized AI in TSCC imaging. A total of 13 studies were included, employing AI models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF). Imaging modalities analyzed included MRI, CT, PET, ultrasound, histopathological whole-slide images (WSI), and endoscopic photographs. Results: Diagnostic performance was generally high, with area under the curve (AUC) values ranging from 0.717 to 0.991, sensitivity from 63.3% to 100%, and specificity from 70.0% to 96.7%. Several models demonstrated superior performance compared to expert clinicians, particularly in delineating tumor margins and estimating the depth of invasion (DOI). However, only one study conducted external validation, and most exhibited moderate risk of bias in patient selection or index test interpretation. Conclusions: AI-based diagnostic tools hold strong potential for enhancing TSCC detection, but future research must address external validation, standardization, and clinical integration to ensure their reliable and widespread adoption. Full article

Sequential drought and heat stress pose a growing threat to forest ecosystems in the context of climate change, yet systematic evaluation methods for woody plants remain limited. This study aimed to develop a comprehensive screening platform for identifying woody plant species tolerant to sequential drought and heat stress among 27 native species growing in Korea. A sequential stress protocol was applied: drought stress for 2 weeks, followed by high-temperature exposure at 45 °C. Physiological indicators, including relative water content (RWC) and electrolyte leakage index (ELI), were used for preliminary screening, supported by phenotypic observations, Evans blue staining for cell death, and DAB staining to assess oxidative stress and recovery ability. The results revealed clear differences among species. Chamaecyparis obtusa, Quercus glauca, and Q. myrsinaefolia exhibited strong tolerance, maintaining high RWC and low ELI values, while Albizia julibrissin was highly susceptible, showing severe membrane damage and low survival. DAB staining successfully distinguished tolerance levels based on oxidative recovery. Additional species such as Camellia sinensis, Q. acuta, Q. phillyraeoides, Q. salicina, and Ternstroemia japonica showed varied responses: Q. phillyraeoides demonstrated high tolerance, T. japonica showed moderate tolerance, and Q. salicina was relatively sensitive. The integrated screening system effectively differentiated tolerant species through multiscale analysis—physiological, cellular, and morphological—demonstrating its robustness and applicability. This study provides a practical and reproducible framework for evaluating sequential drought and heat stress in trees and offers valuable resources for urban forestry, reforestation, and climate-resilient species selection. Full article

Background: Glioblastoma (GBM) is one of the most challenging malignancies in all of neoplasms. These malignancies are associated with unfavorable clinical outcomes and significantly compromised patient wellbeing. The immunological landscape within the tumor microenvironment (TME) plays a critical role in determining GBM prognosis. By mining data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and correlating them with immune responses in the TME, genes associated with the immune microenvironment with potential prognostic value were obtained. Method: We selected GSE16011 as the training set. Gene expression profiles were substrates scored by both ESTIMATE and xCell, and immune cell subpopulations in GBM were analyzed by CIBERSORT. Gene expression profiles associated with low immune scores were performed by lasso regression, Cox analysis and random forest (RF) to identify a prognostic model for the multiple genes associated with immune infiltration in GBM. Then we constructed a nomogram to optimize the prognostic model using GSE7696 and TCGA-GBM as validation sets and evaluated these data for gene mutation and gene enrichment analysis. Result: The prognostic correlation between the six genes (MEOX2, PHYHIP, RBBP8, ST18, TCF12, and THRB) and GBM was finally found by lasso regression, Cox regression, and RF, and the online database obtained that all six genes were differentially expressed in GBM. Therefore, a prognostic correlation model was constructed based on the six genes. Kaplan–Meier (KM) survival analysis showed that this prognostic model had excellent prognostic ability. Conclusions: Prognostic models based on tumor microenvironment and immune score stratification and the construction of related genes have potential applications for prognostic analysis of GBM patients. Full article

Background: This study aimed to develop a nomogram based on the most relevant clinical, CT, and radiomic features comprising 11 key signatures (2 clinical, 2 CT-based, and 7 radiomic) for the non-invasive prediction of the EGFR mutation status and to support the timely initiation of tyrosine kinase inhibitor (TKI) therapy in patients with non-small cell lung cancer (NSCLC) adenocarcinoma. Methods: Retrospective real-world data were collected from 521 patients with histologically confirmed NSCLC adenocarcinoma who underwent CT imaging and either surgical resection or pathological biopsy for EGFR mutation testing. Five Random Forest classification models were developed and trained on various datasets constructed by combining clinical, CT, and radiomic features extracted from CT image regions of interest (ROIs), with and without feature preselection. Results: The model trained exclusively on the most relevant clinical, CT, and radiomic features demonstrated superior predictive performance compared to the other models, with strong discrimination between EGFR-mutant and wild-type cases (AUC = 0.88; macro-average = 0.90; micro-average = 0.89; precision = 0.90; recall = 0.94; F1-score = 0.91; and accuracy = 0.87). Conclusions: A nomogram constructed using a Random Forest model trained solely on the most informative clinical, CT, and radiomic features outperformed alternative approaches in the non-invasive prediction of the EGFR mutation status, offering a promising decision-support tool for precision treatment planning in NSCLC. Full article

Background: Accurate diagnosis of acute appendicitis in children remains challenging due to variable presentations and limitations of existing clinical scoring systems. While machine learning (ML) offers a promising approach to enhance diagnostic precision, most prior studies have been limited by small sample sizes, single-center data, or a lack of external validation. Methods: This prospective, multicenter study included 8586 pediatric patients to develop a machine learning-based diagnostic model using routinely available clinical and hematological parameters. A separate, prospectively collected external validation cohort of 3000 patients was used to assess model performance. The Random Forest algorithm was selected based on its superior performance during model comparison. Diagnostic accuracy, sensitivity, specificity, Area Under Curve (AUC), and calibration metrics were evaluated and compared with traditional scoring systems such as Pediatric Appendicitis Score (PAS), Alvarado, and Appendicitis Inflammatory Response Score (AIRS). Results: The ML model outperformed traditional clinical scores in both development and validation cohorts. In the external validation set, the Random Forest model achieved an AUC of 0.996, accuracy of 0.992, sensitivity of 0.998, and specificity of 0.993. Feature-importance analysis identified white blood cell count, red blood cell count, and mean platelet volume as key predictors. Conclusions: This large, prospectively validated study demonstrates that a machine learning-based scoring system using commonly accessible data can significantly improve the diagnosis of pediatric appendicitis. The model offers high accuracy and clinical interpretability and has the potential to reduce diagnostic delays and unnecessary imaging. Full article

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide. Even though the screening programs have decreased the incidence rates, the prognosis for CRC varies depending on the stage at diagnosis. Thus, early diagnosis is still a big challenge due to screening methods, and subsequent diagnosis is not very sensitive. Methods: In this work, LC-MS-based metabolomics, a powerful and sensitive tool to study complex dynamic changes, was used to analyze 211 human fecal samples from control individuals (CTRL), adenoma (AA), and CRC patients. Results: Multivariate and univariate statistical analysis highlighted cholesteryl esters (CEs) and fecal haemoglobin, quantified by fecal immunochemical test (FIT), as relevant biomarkers that clearly differentiate CRC from AA and CTRL. Predictive models based on random forest and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) demonstrate that CEs, together with FIT measurement, improved the CRC and CTRL classification, but not AA. This study revealed that the AA group is a transitional stage with high heterogeneity. The increased tendency observed in CEs from CTRL to CRC might be related to the imbalance of cholesterol homeostasis due to cancer cells requiring a high cholesterol level for cell development and proliferation. The free cholesterol is probably obtained from CEs, as it is the most cost/effective way to obtain the needed cholesterol. Conclusions: The accumulation of CEs is produced by two possible approaches: (1) dysfunction of cholesterol absorption in the small intestine and/or (2) transported inside exosomes from cell to cell to promote proliferation. Full article

Bursera bipinnata (DC.) Engl. (B. bipinnata), commonly known as “copal chino,” is a widely distributed Mexican tree found in transitional zones between pine-oak and deciduous forests. It is valued for its high-quality copal resin, traditionally used in ceremonies and offerings. Additionally, B. bipinnata is recognized for its significant value in traditional medicine, particularly in treating ailments associated with inflammation. In this work, the inhibition of nitric oxide (NO) production of the volatile fraction and resin of B. bipinnata in LPS-stimulated RAW 264.7 macrophage cells were demonstrated. In contrast, the volatile fraction exhibited 37.43 ± 7.13% inhibition at a concentration of 40 µg/mL. Chromatographic analyses of the total resin enabled the chemical characterization of eleven pentacyclic triterpenes belonging to the ursane, oleanane, and lupane series, as well as eight monoterpenes. Notably, the structures of compounds 15, 17, and 29–35 are reported for the first time from the resin of Bursera bipinnata. The anti-inflammatory activity observed for B. bipinnata resin in this study may be attributed to its high content of the triterpenes α-amyrin (15, 29.7%) and 3-epilupeol (17, 38.1%), both known for their anti-inflammatory properties. These findings support the traditional use of this copal resin. Full article

Background: Diabetic foot ulcers (DFUs) are a severe complication of diabetes and are characterized by impaired wound healing and a high amputation risk. Exosomes—which are nanovesicles carrying proteins, RNAs, and lipids—mediate intercellular communication in wound microenvironments, yet their biomarker potential in DFUs remains underexplored. Methods: We analyzed transcriptomic data from GSE134431 (13 DFU vs. 8 controls) as a training set and validated findings in GSE80178 (6 DFU vs. 3 controls). A sum of 7901 differentially expressed genes (DEGs) of DFUs were detected and intersected with 125 literature-curated exosome-related genes (ERGs) to yield 51 candidates. This was followed by GO/KEGG analyses and a PPI network construction. Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). Expression trends were confirmed in both datasets. Analyses included nomogram construction, functional and correlation analyses, immune infiltration, GSEA, gene co-expression and regulatory network construction, drug prediction, molecular docking, and RT-qPCR validation in clinical samples. Results: A nomogram combining these markers achieved an acceptable calibration (Hosmer–Lemeshow p = 0.0718, MAE = 0.044). Immune cell infiltration (CIBERSORT) revealed associations between biomarker levels and NK cell and neutrophil subsets. Gene set enrichment analysis (GSEA) implicated IL-17 signaling, proteasome function, and microbial infection pathways. A GeneMANIA network highlighted RNA processing and vesicle trafficking. Transcription factor and miRNA predictions uncovered regulatory circuits, and DGIdb-driven drug repurposing followed by molecular docking identified Indatuximab ravtansine and heparin as high-affinity SDC1 binders. Finally, RT-qPCR validation in clinical DFU tissues (n = 5) recapitulated the bioinformatic expression patterns. Conclusions: We present five exosome-associated genes as novel DFU biomarkers with diagnostic potential and mechanistic links to immune modulation and vesicular transport. These findings lay the groundwork for exosome-based diagnostics and therapeutic targeting in DFU management. Full article

Background/Objectives: One of the plants found in Indonesian forests that has potential as an herbal medicine is andaliman (Zanthoxylum acanthopodium DC.). The fruit of Z. acanthopodium contains phenolic compounds that are known to modulate the immune response. The purpose of this study is to determine the extract profile and immunomodulatory activity of Z. acanthopodium fruit and to develop a soft capsule formulation of the extract in the form of emulsion, which stabilizes and acts as an immunomodulatory candidate. Methods: Extract profiling was conducted by liquid chromatography UHPLC–HRMS, and the predicted molecular structure was then used to search for the name of the compound using the mzcloud database. Immunomodulatory activity of the extract and its emulsion was assessed using a lymphocyte viability assay. The extract emulsion to be encapsulated as a soft capsule was developed by employing different types of oil and solubilizer in the oil phase, and a water phase containing the extract and two types of emulsifiers. Results: The chemical composition of andaliman extract was analyzed, including total phenolic content (4%), total flavonoid content (0.35%), and quercetin content (0.13%). Based on LC-HRMS analysis, eleven compounds derived from the ethanolic extract of andaliman were identified as potential immunomodulatory agents. The F3.3F formulation, which contains 30% MCT oil phase with solubilizer lauroyl-PEG-32 glycerides and a water phase with 35% Polysorbat (Tween) 80 emulsifier, provided the most stability. This stability is attributed to the presence of the Tween 80 emulsifier, which has superior wetting and washing functions, strong detergency, and good emulsifying properties compared to the PEG emulsifier used in formulation F3.3E. The survival rates in the lymphocyte cell viability test results indicate that treatment with andaliman extract (173.697% at 15.625 ppm; 174.923% at 31.25 ppm; 168.457% at 62.5 ppm) was better than treatment with kojic acid (144.375% at 15.625 ppm; 137.891% at 31.25 ppm; 146.345% at 62.5 ppm), used as the immunomodulatory agent standard. Conclusions: This study highlights the potential of andaliman extract as an immunomodulatory agent to be developed as an emulsion in a soft capsule. Full article

Wood is an important raw material for industrial applications. Its fiber-specific genetic modification provides an effective strategy to alter wood characteristics in tree breeding. Here, we performed a cross-analysis of previously reported single-cell RNA sequencing and the AspWood database during wood formation to identify potential xylem fiber-dominant expressing genes in poplar. As a result, 32 candidate genes were obtained, and subsequently, we further examined the expression of these genes in fibers and/or vessels of stem secondary xylem using the laser capture microdissection technique and RT-qPCR. Analysis identified nine candidate genes, including PtrFLA12-2, PtrIRX12, PtrFLA12-6, PtrMYB52, PtrMYB103, PtrMAP70, PtrLRR-1, PtrKIFC2-3, and PtrNAC12. Next, we cloned the promoter regions of the nine candidate genes and created promoter::GUS transgenic poplars. Histochemical GUS staining was used to investigate the tissue expression activities of these gene promoters in transgenic poplars. In one month, transgenic plantlets grown in medium showed intensive GUS staining signals that were visible in the leaves and apical buds, suggesting substantial expression activities of these gene promoters in plantlets predominantly undergoing primary growth. In contrast, for three-month-old transgenic poplars in the greenhouse with predominantly developed secondary stem tissues, the promoters of seven of nine candidate genes, including PtrMYB103, PtrIRX12, and PtrMAP70, showed secondary xylem fiber-dominant GUS signals with considerable spatial specificity. Overall, this study presents xylem fiber-dominant promoters that are well-suited for specifically expressing genes of interest in wood fibers for forest tree breeding. Full article