Search Results (1,563)

In post-mortem genetic identification, teeth represent one of the most reliable sources of nuclear DNA when other biological samples are unavailable, owing to their ability to protect genetic material from autolysis, microbial degradation, and environmental insults. The present study describes a forensic veterinary case report involving a Staffordshire Bull Terrier litter in which the appearance of blue-coated offspring from a phenotypically black sire and a blue dam prompted parentage verification. The sire died before formal ante-mortem sampling could be carried out. In accordance with Regulation (EC) No. 1069/2009, Article 15, the carcass was interred in the owner’s garden. Before burial, two teeth were collected as the sole biological material available for identification by the official veterinarian, ensuring an unambiguous chain of custody from death certification to laboratory analysis. DNA extraction was performed using two independent protocols: Method A, a silica-membrane-based purification (QIAamp DNA Kit, Qiagen), applied to dental pulp, and Method B, a decalcification-based workflow (T-Bone Ex Kit) followed by DNA extraction using EZ2 Connect Instruments (Qiagen). Quantification of the DNA samples obtained were done by fluorometry. Short Tandem Repeat (STR) genotyping was performed using the Canine Genotypes™ Panel 1.1 by Applied Biosystems (Thermo Fisher Scientific, Waltham, MA, USA) targeting the 18 autosomal ISAG-recommended microsatellite loci. Parentage assignments were carried out against reference STR profiles obtained from peripheral blood of the dam and offspring. The two methods, considering a different biological matrix and different operating conditions, allowed us to obtain the STR profile, but Method B showed quality electropherograms, improving the confidence and interpretation of all the markers analyzed. The differences observed between the two approaches may reflect a combination of factors, including the different dental matrices analysed, the quantity and quality of the starting material, tooth identity and tissue preservation, and differences inherent to the extraction procedures themselves; therefore, in this case, the more complete and clearer STR profile obtained with Method B should be considered a case-specific observation rather than evidence of a direct effect of the extraction method. Overall, regardless of the DNA extraction method used, the results did not exclude the biological paternity of the blue-mantled offspring. Furthermore, this case report provides practical methodological guidance for DNA recovery from canine teeth and supports their use as a reliable source of post-mortem DNA for STR-based parentage testing. Full article

Background: Acute myeloid leukemia (AML) is biologically heterogeneous and associated with poor outcomes. Although oxidative stress contributes to AML pathogenesis, the prognostic value of related biomarkers remains uncertain. Objective: This study aimed to evaluate associations between oxidative stress-related biomarkers and prognosis in adults with AML, focusing on overall survival (OS), complete remission (CR), relapse, relapse-free survival (RFS), event-free survival (EFS), and early mortality. Methods: This PRISMA-compliant systematic review was registered in PROSPERO (CRD420261364956). MEDLINE/PubMed, Scopus, Cochrane Library, Science Citation Index, ClinicalTrials.gov, and WHO ICTRP were searched from January 2015 through 31 July 2026. Eligible studies included adults with AML, oxidative stress-related biomarkers measured in biological samples, and extractable prognostic data. Risk of bias was assessed using QUIPS and certainty of evidence using GRADE. Results: Thirteen of 537 records met the inclusion criteria; 203 supplementary reports yielded no additional studies. Investigated biomarkers included ROS-related phenotypes, antioxidant and redox-regulatory genes, glutathione metabolism, iron/inflammation-related markers, and oxidative DNA damage indicators. Adverse outcomes were associated with ROS-related phenotypes, combined ROS/aldehyde dehydrogenase activity, GPX3, GSTP1, ferritin, gamma-glutamyl transpeptidase-to-albumin ratio, SOD1, and glutathione-related metabolic profiles. Conclusions: These biomarkers may have prognostic value, particularly for OS, but heterogeneity limits clinical application. Standardized validation and integration into applicable risk models are required. Full article

Background and Objectives: Antimicrobial resistance represents an important patient-safety concern in surgical departments, where empirical therapy, invasive procedures, intensive-care exposure, and healthcare-associated infections may contribute to adverse outcomes. This study aimed to describe temporal changes in institutional surveillance indicators related to microbiological testing coverage, selected antimicrobial-resistance phenotypes among clinical isolates, selected-antibiotic use profiles, and infection-related patient-safety outcomes in surgical departments and the intensive care unit of a Romanian tertiary clinical emergency hospital. Materials and Methods: A retrospective, longitudinal, single-center observational study was conducted from January 2023 to December 2025. Data were extracted from four institutional sources: microbiology laboratory records, pharmacy antimicrobial-use data, the infection prevention and control registry, and the clinical-administrative hospital information system. Microbiological testing coverage, the selected resistance phenotypes and healthcare-associated infection indicators were analyzed at semester level, with selected-antibiotic DDD profiles being analyzed at department level. Statistical analyses included descriptive statistics, Cochran–Armitage trend testing, Fisher’s exact test, and exploratory Spearman correlation. Results: The hospital-wide microbiological testing intensity index based on 1892 antibiogram events among 8141 antibiotic-treated patient records increased significantly from 18.05% (Semester I 2023) to 28.20% (Semester II 2025). When expressed as proportions of clinical isolates, E. coli MDR decreased from 10/136 isolates (7.4%) in Semester II 2023 to 0/122 isolates (0.0%) in Semester II 2025, and S. aureus MRSA+MLSB decreased from 11/36 isolates (30.6%) to 0/22 isolates (0.0%) over the same interval. Undetected several semesters, P. aeruginosa XDR/PDR accounted for 4/25 clinical isolates (16.0%) in the final semester. Selected-antibiotic DDD profiles differed across departments, with General Surgery and ICU accounting for the largest selected-antibiotic totals. Preoperative bacteriological screening increased from 48 tests in Semester I 2024 to 329 tests in Semester II 2025, MRSA-positive screening results decreasing from 10/48 (20.8%) to 2/329 (0.6%) over this interval, this finding being interpreted descriptively because the screened population and screening protocol could not be fully standardized retrospectively. Among 46 validated cases of healthcare or Clostridioides difficile infections, the overall mean length of stay was 15.35 days, peaking at 20.80 days in surgical departments. Conclusions: Routinely collected hospital data can generate useful institutional surveillance signals related to antimicrobial resistance, antimicrobial use, and infection-related patient safety. Increased microbiological testing coverage and lower proportions of selected MDR/MRSA phenotypes were observed during the study period, while late detection of XDR/PDR P. aeruginosa highlighted the need for sustained microbiological surveillance and department-specific stewardship review. Full article

Background/Objectives: Cytochrome P450(CYP)2C19 activity varies widely among individuals. As genetic factors, CYP2C19*2 and CYP2C19*3 alleles reduce CYP2C19 activity, while the CYP2C19*17 allele increases CYP2C19 activity. However, environmental and physiological factors can also influence individual CYP2C19 activity. In this study, we searched for novel endogenous biomarkers for CYP2C19 activity using CYP2C19 gene polymorphism data combined with results of untargeted metabolomic analysis. Methods: 431 general adults analyzed in the Kyoto J-MICC Study and 255 patients who visited Oita University Hospital were studied. Plasma samples were pretreated by solid-phase and liquid-liquid extraction and subjected to untargeted metabolomic analysis using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Based on CYP2C19 gene polymorphism data, participants were classified into extensive metabolizers (EM), intermediate metabolizers (IM), and poor metabolizers (PM). Compounds showing significant differences in abundance among the three groups were considered candidate compounds for predicting CYP2C19 activity. The predictive performance of candidate compounds for CYP2C19 PM status was evaluated using covariate-adjusted receiver operating characteristic (ROC) analysis. Results: The normalized abundance of compounds with m/z 160.1342, 303.2319 (a fatty acyl or prenol lipid), 314.2309, 449.3238, 653.3021, 792.5744, and 811.5988 (a glycerophospholipid or sphingolipid), and 902.5404 (a fatty acyl) differed significantly among CYP2C19 EM, IM, and PM groups (p < 0.05), and these eight compounds were considered candidate compounds. Covariate-adjusted ROC analysis showed that none of the candidate compounds significantly improved the discrimination of CYP2C19 PM status. Conclusions: Untargeted metabolomics combined with CYP2C19 gene polymorphism data yielded eight compounds associated with CYP2C19 phenotype. Further studies are needed to evaluate the usefulness of these compounds as biomarkers of CYP2C19 activity. Full article

A water-soluble, low-molecular-weight polysaccharide fraction, designated OJP-2, was extracted from the roots of Ophiopogon japonicus; its structural characteristics and immunomodulatory activity in macrophages were subsequently investigated. OJP-2 exhibited a narrow apparent molecular weight distribution with an average molecular weight (Mw) of 3568 Da and was primarily composed of fructose (0.784) and glucose (0.208). Structural analysis—integrating UV spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and 1D/2D nuclear magnetic resonance (NMR) spectroscopy—revealed that OJP-2 is an inulin-type fructan. Its structure is characterized predominantly by β-(2→1)-linked Fruf chains and terminal α-D-Glcp residues, with potential signals indicating C-6-substituted Fruf units. In vitro immunological studies demonstrated that OJP-2 promoted nitric oxide (NO) production and enhanced the secretion of IL-6, IL-1β, and TNF-α in RAW264.7 macrophages. Under LPS/IFN-γ stimulation, OJP-2 induced non-monotonic changes in the CD86/CD206 macrophage phenotype, with the most pronounced effects observed at a concentration of 50 μg/mL. Furthermore, Western blot analysis showed that, compared to the Model group, treatment with OJP-2 resulted in a downward trend in the relative levels of p-p65/p65 and p-IκBα/IκBα across the tested concentration range. Collectively, these findings indicate that OJP-2 modulates macrophage activation phenotypes and influences NF-κB-related signaling pathways. Thus, OJP-2 is a candidate fructan for further investigation of immunomodulatory activity. Full article

Soybean pod-related traits are important agronomic characteristics associated with seed development, domestication, cultivar identification, and breeding improvement. However, conventional phenotyping methods mainly rely on manual measurements, which are time-consuming and labor-intensive and capture only limited dimensions of pod variation, while the genetic basis of skeleton- and curvature-based pod descriptors remains insufficiently characterized in biparental populations. In this study, eight quantitative traits representing pod size, shape, and color components were extracted from an existing mature pod image dataset of an interspecific soybean recombinant inbred line (RIL) population using the established deep learning-based image phenotyping framework. These traits exhibited substantial phenotypic variation, with across-year entry-mean broad-sense heritability (H²) estimates ranging from 0.30 to 0.80. Composite interval mapping (CIM) based on a high-density genetic linkage map identified 54 quantitative trait loci (QTLs), which were integrated into 39 non-redundant loci, including six cross-year stable QTLs and three QTLs supported by best linear unbiased prediction (BLUP) analysis. Candidate genes within selected focal QTL regions were prioritized through functional annotation and pod and seed developmental expression analyses. Among them, Glyma.17G109100 (GmSW17) was prioritized as a positional candidate gene for pod size-related variation, whereas Glyma.19G120400 (L1), a previously validated causal gene for pod color, was located within qV19. These findings demonstrate the effectiveness of combining deep learning-based phenotyping with genetic analysis for dissecting the genetic architecture of complex soybean pod-related traits and provide valuable stable QTLs and candidate genes for future functional studies and soybean molecular breeding. Full article

Background: Brain tumours impose complex, spatially heterogeneous disturbances on neural circuits that extend far beyond the immediate lesion site. While gross anatomical displacement of the cortico-spinal tract (CST) has been extensively studied, the topological reorganization of the functionally informed structural connectome—and its dependence on tumour molecular phenotype—remains incompletely understood. Objectives: This study aimed to characterize upper-limb functionally informed network topology in brain tumour patients, identify histological and molecular patterns of structural reorganization at the cortical and subcortical level, and determine the impact on neurophysiological parameters. Methods: Forty-eight patients with supratentorial motor-eloquent tumours (MET’s) underwent diffusion-weighted imaging (DWI) as part of their preoperative motor mapping. Connectivity matrices based on streamline passing counts were extracted from 426 nodes of the HCPex atlas using DSI Studio^® upon seeding the structural connectome in the motor hotspot (best motor response) for the functional area of the upper limb identified using preoperative navigated transcranial magnetic stimulation (nTMS). Paired-sample t-tests compared tumour versus healthy hemispheres across network topology metrics. The impact of nTMS-derived excitability metrics—interhemispheric resting motor threshold ratio (iRMTr) and cortical silent period (CSP)—and tumour histological and molecular characteristics on the connectome was assessed. Results: The global network topology of the tumour hemisphere was preserved when compared to the healthy baseline hemisphere across all tumour types (p > 0.05). Subcortical analysis revealed significant hyper-connectivity in the tumour hemisphere, with elevated degree, strength, clustering coefficient, local efficiency, and eigenvector centrality (p < 0.05). Basal ganglia motor loop degree was increased in the tumour hemisphere (mean 7.37 versus 5.19; p = 0.0009). IDH-mutant tumours generated significantly more topologically organized compensatory networks than IDH-wildtype tumours (Clustering Coefficient 0.345 versus 0.273; p = 0.018). Of the cortical nodes on the side of the tumour, significant hyper-connectivity was seen in the supplementary motor area (p = 0.0011), premotor cortex (Area 6), with increased connection seen in 6 mp (medial premotor at p =0.0033) and 6 d (dorsal premotor at p = 0.025) and primary somatosensory cortex (p = 0.027). The presence of the tumour induced significant changes across all three domains: loss of CST volume (p < 0.001), prolongation of the cortical silent period indicative of intracortical inhibition (p < 0.0001), with significant prolongation in glioblastoma versus an oligodendroglioma. Conclusions: Brain tumours significantly impact the upper limb-centred structural connectome. While global network topology is preserved, tumours induce substantial subcortical and basal ganglia network reorganization by inducing compensatory hyper-connectivity. These findings suggest that structural connectomics offers a novel framework for non-invasive tumour characterization and surgical planning. Full article

The root phenotype of cotton seedlings serves as a criterion for selecting high-vigour varieties, yet systematic evaluations of Upland Cotton germplasm resources remain limited. We measured nine root- and shoot-related traits in 204 Gossypium hirsutum accessions from diverse ecological regions. Correlation analysis revealed that root surface area, volume, and fresh weight were closely interrelated indicators. Principal component analysis extracted two biologically meaningful factors—‘absorptive capacity’ and ‘morphological strategy’—with a cumulative contribution of 55.72%. Cluster analysis classified the accessions into three functional groups: high-yield potential (n = 15), balanced adaptation (n = 135), and fine-root dominance (n = 54). These clusters corresponded with ecological origins and breeding periods, reflecting local adaptation. We propose root surface area, volume, and fresh weight as cost-effective screening indicators and identified 15 elite accessions as potential parents for root-centred breeding. These findings are particularly relevant for the arid production environments of Northwestern China. Full article

Background/Objectives: Lymph-node metastasis remains central to staging, prognosis, surveillance, and treatment planning in malignant melanoma. At present, most statistical models assessing nodal metastatic risk in malignant melanoma consider pathological descriptors, inflammatory markers, metabolic alterations, clinical data, and related variables independently of each other. Therefore, we developed a transparent artificial intelligence (AI)-based approach to assess whether the propensity for nodal metastasis is determined by a single layer of local histopathological aggressiveness or by the integration of different biological levels, including local histopathological aggressiveness, systemic inflammatory–metabolic dysregulation, biological heterogeneity, or a clinicobiological pattern. Methods: In this retrospective study, we assessed 73 adult patients undergoing surgical removal of malignant melanoma. The primary endpoint was histopathologically confirmed lymph-node metastasis. Routinely collected patient-related data, including clinical, anatomical, operative, histopathological, nodal, comorbidity, biological, clinical course, and available staging data, were structured into interpretable constructs. These included the Histopathologic Aggressiveness Index (HAI), the Inflammatory–Metabolic Dysregulation Index (IMDI), the Biological–Histological Discordance Score (BHDS), model-estimated nodal metastatic probability, integrated clinicobiological risk, and explanation stability. The AI-based framework was evaluated by applying bias-reduced and penalized logistic regression, machine learning benchmarking, leave-one-out cross-validation, bootstrap estimation, permutation testing, decision curve analysis, rule extraction, feature stability evaluation, network analysis, similarity-based retrieval, conformal uncertainty estimation, and unsupervised phenomapping. Results: For 72 out of 73 patients, nodal histopathology results were available. Among these patients, 22 had positive nodal status. Positive nodal status was associated with a higher Breslow thickness, an increased mitotic rate, ulceration, lymphovascular invasion, a nodular subtype, and palpable adenopathy. The HAI demonstrated the strongest discriminative signal between node-positive and node-negative patients (median values of 67.8 vs. 45.3; p < 0.001) and retained an independent association with nodal metastasis within the bias-reduced logistic model (odds ratio [OR] per 10-point increase: 2.74; 95% confidence interval [CI]: 1.58–4.75; p < 0.001). The IMDI showed a weak exploratory relationship and did not retain an independent association after adjustment. Similarly, the BHDS did not show significant differences in separating the two endpoint groups. The penalized logistic model including only the HAI showed good performance under leave-one-out cross-validation, with ROC AUC = 0.889, PR-AUC = 0.706, and Brier score = 0.137. Through rule extraction, we found a cohort-specific HAI threshold value > 58.6, above which all node-positive cases were located. With respect to explainability, feature stability, network analysis, similarity retrieval, conformal prediction, and phenomapping, there was convergence toward a dominant high-risk phenotype defined primarily by histopathological criteria. Conclusions: Routine melanoma registries may be transformed into internally evaluated melanoma intelligence frameworks. Histopathologically confirmed lymph-node metastasis among patients with malignant melanoma was organized primarily along an axis of local histopathological aggressiveness, while systemic inflammatory–metabolic dysregulation provided subordinate contextual biological information. Full article

Background: Progesterone signaling has been implicated in diffuse glioma biology, but the factors contributing to variability in progesterone receptor (PGR) activity remain incompletely understood. We investigated whether PROGINS-related PGR variants detected in tumor-derived DNA were associated with PGR expression and selected biological features of diffuse gliomas. Methods: This retrospective translational study included 66 patients with histopathologically confirmed diffuse gliomas. Three PROGINS-associated PGR variants—the Alu insertion, V660L (rs1042838), and H770H (rs1042839)—were analyzed using DNA extracted from fresh-frozen tumor tissue. Relative PGR gene expression, tissue PGR protein concentrations, Ki-67 labeling index, and p53 immunoreactivity were evaluated together with IDH status, ATRX expression, and 1p/19q co-deletion. Separate multivariable regression models assessed variant-specific associations and prespecified variant × sex and variant × tumor grade interactions. Multiple testing within the reported multivariable models was addressed using the Benjamini–Hochberg false discovery rate procedure. Results: Relative PGR gene expression was lower in high-grade than in low-grade gliomas (p = 0.039), whereas tissue PGR protein concentrations did not differ significantly according to grade. In multivariable analyses, V660L L-allele carriage was associated with higher PGR expression and lower Ki-67 labeling indices, H770H G-allele carriage was associated with lower PGR expression and higher p53 immunoreactivity, and Alu insertion carrier status was associated with lower PGR expression. Significant variant × sex interactions were identified for several outcomes, and an H770H × tumor grade interaction was observed for PGR expression. All nominally significant associations reported in the multivariable models remained significant after false discovery rate correction. Conclusions: PROGINS-related PGR variants detected in tumor-derived DNA are associated with selected biological features of diffuse gliomas, with several associations differing according to biological sex and, for selected outcomes, tumor grade. These findings support a context-dependent relationship between PGR variant status and glioma phenotype but do not establish functional causality or clinical biomarker utility. Independent functional and longitudinal studies are required to determine the biological and potential clinical relevance of these associations. Full article

Accurate monitoring of photosynthetic phenotypes is fundamental for breeding high photosynthetic-efficiency wheat cultivars, and UAV remote sensing provides an effective approach for their large-scale identification. However, single photosynthetic parameters are limited in comprehensively evaluating crop photosynthetic efficiency. This study integrated multiple photosynthetic phenotypic parameters using principal component analysis (PCA) and the CRITIC objective weighting method (PCA-CRITIC) to construct a Comprehensive High Photosynthetic-Efficiency Phenotypic Index (CHPPI) for winter wheat. Concurrently, multispectral vegetation indices (MSVI), RGB vegetation indices (RGBVI), texture features (TF), and their combination, Comprehensive Multispectral-Visible-Texture Features (CMVTF), were extracted from UAV multimodal remote sensing data. Based on these features, four feature selection methods, namely PCC, VIP, SPA, and UVE, were evaluated in combination with five machine learning algorithms, including KNN, DT, SVR, RF, and XGBoost, to precisely estimate the CHPPI across key growth stages. The results demonstrated that the CHPPI exhibited significant cultivar variations across growth stages. Cultivars SH06144 and Liangxing19 consistently maintained high photosynthetic efficiency levels. The fused feature set CMVTF derived from multimodal remote sensing data outperformed individual feature categories, and the feature subset selected by the UVE method most significantly enhanced model accuracy. Regarding algorithms, the ensemble-based XGBoost and RF models markedly surpassed traditional machine learning algorithms. Specifically, the CMVTF-UVE-XGBoost model achieved the optimal comprehensive performance, yielding an R² of 0.862, an RMSE of 0.032, and an MAE of 0.027 on the testing set, with its spatial distribution estimations highly consistent with measured values. This study provides robust technical support for the rapid and large-scale screening of high photosynthetic-efficiency wheat breeding materials. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disease associated with insulin resistance and hepatic lipid accumulation. Polyphenols have attracted considerable attention for their hepatoprotective and lipid-lowering activities. Our previous studies characterized a bound polyphenol extracted from the inner shell of foxtail millet (BPIS) and identified its major active components. In the present study, we investigated the molecular mechanisms underlying its biological activity using HepG2 cells and Caenorhabditis elegans. BPIS activated AMPK signaling, normalized intracellular glutathione (GSH) levels, and upregulated SLC7A11 and GPX4, suggesting modulation of ferroptosis-related pathways and improved cellular redox homeostasis. BPIS also alleviated endoplasmic reticulum stress by increasing GRP78 expression, inhibiting DRAK2, and regulating the ERK pathway, thereby improving the regulation of key lipid-metabolism-related signaling pathways, including SREBP1c, SCD1, CD36, FASN, and CPT1A. Consistent with these findings, BPIS reduced glucose- and free fatty acid-induced lipid accumulation and improved lipid-related phenotypes in C. elegans. Overall, BPIS attenuated hepatic steatosis through modulation of ferroptosis-related markers, endoplasmic reticulum stress, and lipid metabolism. These findings provide new mechanistic insights into the biological activities of BPIS and support its potential application as a nutraceutical ingredient for the prevention and management of MASLD. Full article

Rice false smut is a major panicle disease that affects rice yield and grain quality and is an important target for resistance evaluation in breeding programs. Accurate field phenotyping is important for disease assessment and resistance screening, yet current assessment relies heavily on manual visual scoring and smut ball counting, which are laborious and subject to evaluator variation. In close-range single-panicle images, false smut balls are often small, dense, occluded, adhered, making automatic detection and severity grading difficult. To address these challenges, we developed a YOLOv12-RSLW detector by integrating RepGhost, SimAM, LSCD, and WIoU into YOLOv12. Detection boxes were then used to guide the Segment Anything Model for panicle and lesion mask extraction, allowing calculation of the lesion-to-panicle area ratio as a supplementary indicator for count-based severity grading. A total of 1911 original field images were collected. After augmentation, the dataset contained 5663 images, including 4531 training images, 566 validation images, and 566 test images. Detection performance was evaluated on the test set, while SAM segmentation was assessed using 80 manually annotated original images. YOLOv12-RSLW achieved 92.06% mAP@0.5, 91.46% precision, and 87.01% recall, with 3.45 M parameters and 6.0 GFLOPs. Compared with the baseline YOLOv12, mAP@0.5 and recall increased by 3.60 and 4.37 percentage points, respectively. Within the augmented dataset, 41.2% of samples initially assigned to Grade 1 and 28.1% of those assigned to Grade 2 met the area-ratio criteria for potential reassignment to higher grades. The framework provides a quantitative approach to rice false smut severity phenotyping and may support future resistance breeding after further validation. Full article

Cauliflower emergence rate and seedling growth are key indicators of field conditions and varietal potential. Traditional manual surveys are unsuitable for continuous monitoring across multiple varieties. This study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth. Built on YOLOv11, the model incorporates a lightweight feature extraction structure and an optimized detection-scale configuration. It reduces computational complexity while maintaining detection accuracy, thereby improving the efficiency of cauliflower seedling detection. DualSlim-YOLO achieved P, R, F1-score, mAP@0.5, and mAP@0.5:0.95 of 95.35%, 96.75%, 96.05%, 98.55%, and 86.65%, respectively. The number of parameters was reduced by 38.61%, while the inference speed increased by 22.16%, demonstrating good lightweight performance. Based on this model, UAV images of 171 cauliflower varieties acquired at 7, 21, and 28 d after transplanting were used for seedling detection and emergence rate estimation. In addition, 18 time-series seedling phenotypic traits were extracted, enabling a comprehensive quantitative evaluation of emergence dynamics and early-growth performance across multiple cauliflower varieties. This method effectively screens cauliflower varieties for high emergence rates, rapid emergence, and excellent seedling growth performance. It provides technical support for high-throughput, nondestructive seedling phenotyping and early germplasm screening under field conditions. Full article

Background: Immunometabolic reprogramming is increasingly recognized as a fundamental driver of macrophage inflammatory activation. Although pomegranate polyphenols have been widely investigated for their anti-inflammatory activity, the underlying metabolic mechanisms remain poorly understood. Here, we investigated whether a pomegranate waste extract (PWE), obtained through a sustainable dimethyl carbonate (DMC)-based extraction process, modulates macrophage activation by targeting immunometabolic pathways. Methods: Human PBMC-derived macrophages stimulated with LPS and IFN-γ were treated with PWE. Inflammatory mediators, NF-κB transcription factor, histone H3 acetylation, and markers of inflammatory resolution were evaluated. Furthermore, the enzymatic activity of ATP citrate lyase (ACLY) and malic enzyme 1 (ME1) was determined. Rescue experiments with acetate, malate, and NADPH were performed to investigate the functional contribution of the ACLY–ME1 metabolic axis. Results: PWE significantly reduced NF-κB activation and the production of IL-1β, IL-6, TNF-α, ROS, NO•, and PGE₂ without affecting cell viability. Mechanistically, PWE functionally suppressed ACLY and ME1, two central enzymes linking citrate metabolism to cytosolic acetyl-CoA and NADPH generation. Indeed, acetate supplementation restored PGE₂ production and inflammatory cytokine secretion, whereas malate and NADPH rescued oxidative mediator production. PWE also lowered histone H3 acetylation, indicating that metabolic remodeling affected epigenetic regulation of inflammatory gene expression. Finally, PWE increased the expression of CPT1A, SLC25A20, Annexin A1, and FPR2, while enhancing IL-10 and 15-HETE secretion, consistent with activation of pro-resolving macrophage programs. Conclusions: These findings demonstrate that DMC-extracted PWE suppresses inflammatory macrophage activation primarily through immunometabolic modulation. By targeting the ACLY–ME1 metabolic axis, PWE limits acetyl-CoA- and NADPH-dependent inflammatory processes and fosters a shift toward a pro-resolving macrophage phenotype. Our work identifies this sustainable pomegranate waste extract as a promising modulator of macrophage immunometabolism. Full article