J Immunoassay Immunochem. 2025 Feb 11:1-6. doi: 10.1080/15321819.2025.2462807. Online ahead of print.

ABSTRACT

Bacterial colonization of the cystic fibrosis (CF) airways is polymicrobial and several emerging microorganisms with a potential pathogenic role may be present. We report a case of three siblings with CF colonized by Pandoraea over a period of 10 years. Isolates identified as various non-fermentative Gram-negative bacilli from sputum cultures were successfully re-identified as Pandoraea vervacti (P. vervacti) by a combination of matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) and 16S rRNA and gyrB sequencing. Furthermore, the transcript expression of type I and type III interferon (IFN) genes was examined in the cells of respiratory samples from these patients and compared with a Pandoraea-negative group of CF individuals. Increased respiratory levels of IFNβ, IFNε and IL28R1 mRNA were found in the three siblings. Our results demonstrate that P. vervacti can chronically colonize CF patients and alter IFN response, likely contributing to immunopathogenesis and disease progression.

PMID:39935049 | DOI:10.1080/15321819.2025.2462807

Hum Brain Mapp. 2025 Feb 15;46(3):e70141. doi: 10.1002/hbm.70141.

ABSTRACT

Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of pathologies and T1-weighted image acquisition parameters, (2) conduct a systematic technical and clinical validation, (3) improve segmentation quality in the presence of brainstem lesions, and (4) make an optimized brainstem segmentation tool available for public use. An intentionally heterogeneous ground truth dataset (n = 257) was employed in the training of deep learning models based on multi-dimensional gated recurrent units (MD-GRU) or the nnU-Net method. Segmentation performance was evaluated against ground truth labels. FreeSurfer was used for benchmarking in subsequent validation. Technical validation, including scan-rescan repeatability (n = 46) and inter-scanner reproducibility (n = 20, 3 different scanners) in unseen data, was conducted in patients with cerebral small vessel disease. Clinical validation in unseen data was performed in 1-year follow-up data of 16 patients with multiple system atrophy, evaluating the annual percentage volume change. Two lesion filling algorithms were investigated to improve segmentation performance in 23 patients with multiple sclerosis. The MD-GRU and nnU-Net models demonstrated very good segmentation performance (median Dice coefficients ≥ 0.95 each) and outperformed a previously published model trained on a narrower dataset. Scan-rescan repeatability and inter-scanner reproducibility yielded similar Bland-Altman derived limits of agreement for longitudinal FreeSurfer (total brainstem volume repeatability/reproducibility 0.68/1.85), MD-GRU (0.72/1.46), and nnU-Net (0.48/1.52). All methods showed comparable performance in the detection of atrophy in the total brainstem (atrophy detected in 100% of patients) and its substructures. In patients with multiple sclerosis, lesion filling further improved the accuracy of brainstem segmentation. We enhanced and systematically validated two fully automated deep learning brainstem segmentation methods and released them publicly. This enables a broader evaluation of brainstem volume as a candidate biomarker for neurodegeneration.

PMID:39936343 | DOI:10.1002/hbm.70141

J Sci Food Agric. 2025 Feb 12. doi: 10.1002/jsfa.14177. Online ahead of print.

ABSTRACT

BACKGROUND: Silkworm pupae (SP), the pupal stage of an edible insect, have strong potential in the food, medicine, and cosmetic industries. Sex sorting is essential to enhance nutritional content and genetic traits in SP crossbreeding but it remains labor intensive and time consuming. An intelligent method is needed urgently to improve efficiency and productivity.

RESULTS: To address the problem, an automatic SP sex-separation system was developed based on computer vision and deep learning. Specifically, based on gonad features, a novel real-time SP sex identification model with cascaded spatial channel attention (CSCA) and G-GhostNet (GPU-Ghost Network) was developed, which can capture regions of interest and achieve feature diversity efficiently. A new loss function was proposed to reduce model complexity and avoid overfitting in the training. In comparison with benchmark methods on the test set, the new model achieved superior performance with an accuracy of 96.48%. The experimental sorting accuracy for SP reached 95.59%, validating the effectiveness of the novel gender-separation strategy.

CONCLUSION: This research presents a practical method for online SP gender separation, potentially aiding the production of high-quality SP. © 2025 Society of Chemical Industry.

PMID:39936219 | DOI:10.1002/jsfa.14177

Front Plant Sci. 2025 Jan 28;16:1523552. doi: 10.3389/fpls.2025.1523552. eCollection 2025.

ABSTRACT

With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network's ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model's detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets. Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%.

PMID:39935949 | PMC:PMC11810954 | DOI:10.3389/fpls.2025.1523552

Front Oncol. 2025 Jan 28;15:1474590. doi: 10.3389/fonc.2025.1474590. eCollection 2025.

ABSTRACT

PURPOSE: This work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification.

METHODS: A total of 76 supratentorial meningioma patients treated with radiotherapy were studied. Gross tumor volumes (GTVs) were contoured by a single experienced radiation oncologist on high-resolution contrast-enhanced T1 MRI scans (T1ce), and both T1 and T1ce images were utilized for segmentation. SPU-Net, an adaptation of U-Net incorporating spherical image projection to map 2D images onto a spherical surface, was proposed. As an equivalence of a nonlinear image transform, projections enhance locoregional details while maintaining the global field of view. By employing multiple projection centers, SPU-Net generates various GTV segmentation predictions, the variance indicating the model's uncertainty. This uncertainty is quantified on a pixel-wise basis using entropy calculations and aggregated through Otsu's method for a final segmentation.

RESULTS/CONCLUSION: The SPU-Net model poses an advantage over traditional U-Net models by providing a quantitative method of displaying segmentation uncertainty. Regarding segmentation performance, SPU-Net demonstrated comparable results to a traditional U-Net in sensitivity (0.758 vs. 0.746), Dice similarity coefficient (0.760 vs. 0.742), reduced mean Hausdorff distance (mHD) (0.612 cm vs 0.744 cm), and reduced 95% Hausdorff distance (HD95) (2.682 cm vs 2.912 cm). SPU-Net not only is comparable to U-Net in segmentation performance but also offers a significant advantage by providing uncertainty quantification. The added SPU-Net uncertainty mapping revealed low uncertainty in accurate segments (e.g., within GTV or healthy tissue) and higher uncertainty in problematic areas (e.g., GTV boundaries, dural tail), providing valuable insights for potential manual corrections. This advancement is particularly valuable given the complex extra-axial nature of meningiomas and involvement with dural tissue. The capability to quantify uncertainty makes SPU-Net a more advanced and informative tool for segmentation, without sacrificing performance.

PMID:39935829 | PMC:PMC11810883 | DOI:10.3389/fonc.2025.1474590

Front Oncol. 2025 Jan 28;15:1528654. doi: 10.3389/fonc.2025.1528654. eCollection 2025.

ABSTRACT

OBJECTIVES: To develop a novel automatic delineation model, the Multi-Scale Channel Attention U-Net (MCAU-Net) model, for gallbladder segmentation on CT images of patients with liver cancer.

METHODS: We retrospectively collected the CT images from 120 patients with liver cancer, based on which ground truth was manually delineated by physicians. The images and ground truth constitute a dataset, which was proportionally divided into a training set (54%), a validation set (6%), and a test set (40%). Data augmentation was performed on the training set. Our proposed MCAU-Net model was employed for gallbladder segmentation and its performance was evaluated using Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), Positive Predictive Value (PPV), Sensitivity (SE), Hausdorff Distance (HD), Relative Volume Difference (RVD), and Volumetric Overlap Error (VOE) metrics.

RESULTS: On the test set, MCAU-Net achieved DSC, JSC, PPV, SE, HD, RVD, and VOE values of 0.85 ± 0.22, 0.79 ± 0.23, 0.92 ± 0.14, 0.84 ± 0.23, 2.75 ± 0.98, 0.18 ± 0.48, and 0.22 ± 0.42, respectively. Compared to the control models, U-Net, SEU-Net and TransUNet, the MCAU-Net improved DSC 0.06, 0.04 and 0.06, JSC by 0.09, 0.06 and 0.09, PPV by 0.08, 0.08 and 0.05, SE by 0.05,0.05 and 0.07, and reduced HD by 0.45, 0.28 and 0.41, RVD by 0.07, 0.03 and 0.07, VOE by 0.04, 0.02 and 0.08 respectively. Qualitative results revealed that MCAU-Net produced smoother and more accurate boundaries, closer to the expert delineation, with less over-segmentation and under-segmentation and improved robustness.

CONCLUSIONS: The MCAU-Net model significantly improves gallbladder segmentation on CT images. It satisfies clinical requirements and enhances the efficiency of physicians, particularly in segmenting complex anatomical structures.

PMID:39935828 | PMC:PMC11810919 | DOI:10.3389/fonc.2025.1528654

Front Med (Lausanne). 2025 Jan 28;12:1492709. doi: 10.3389/fmed.2025.1492709. eCollection 2025.

ABSTRACT

OBJECTIVE: To systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.

METHODS: A comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.

RESULTS: A collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold's protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.

CONCLUSION: This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.

PMID:39935800 | PMC:PMC11810743 | DOI:10.3389/fmed.2025.1492709

Front Cell Dev Biol. 2025 Jan 28;12:1517240. doi: 10.3389/fcell.2024.1517240. eCollection 2024.

ABSTRACT

PURPOSE: Using deep learning model to observe the blinking characteristics and evaluate the changes and their correlation with tear film characteristics in children with long-term use of orthokeratology (ortho-K).

METHODS: 31 children (58 eyes) who had used ortho-K for more than 1 year and 31 age and gender-matched controls were selected for follow-up in our ophthalmology clinic from 2021/09 to 2023/10 in this retrospective case-control study. Both groups underwent comprehensive ophthalmological examinations, including Ocular Surface Disease Index (OSDI) scoring, Keratograph 5M, and LipiView. A deep learning system based on U-Net and Swim-Transformer was proposed for the observation of blinking characteristics. The frequency of incomplete blinks (IB), complete blinks (CB) and incomplete blinking rate (IBR) within 20 s, as well as the duration of the closing, closed, and opening phases in the blink wave were calculated by our deep learning system. Relative IPH% was proposed and defined as the ratio of the mean of IPH% within 20 s to the maximum value of IPH% to indicate the extent of incomplete blinking. Furthermore, the accuracy, precision, sensitivity, specificity, F1 score of the overall U-Net-Swin-Transformer model, and its consistency with built-in algorithm were evaluated as well. Independent t-test and Mann-Whitney test was used to analyze the blinking patterns and tear film characteristics between the long-term ortho-K wearer group and the control group. Spearman's rank correlation was used to analyze the relationship between blinking patterns and tear film stability.

RESULTS: Our deep learning system demonstrated high performance (accuracy = 98.13%, precision = 96.46%, sensitivity = 98.10%, specificity = 98.10%, F1 score = 0.9727) in the observation of blinking patterns. The OSDI scores, conjunctival redness, lipid layer thickness (LLT), and tear meniscus height did not change significantly between two groups. Notably, the ortho-K group exhibited shorter first (11.75 ± 7.42 s vs. 14.87 ± 7.93 s, p = 0.030) and average non-invasive tear break-up times (NIBUT) (13.67 ± 7.0 s vs. 16.60 ± 7.24 s, p = 0.029) compared to the control group. They demonstrated a higher IB (4.26 ± 2.98 vs. 2.36 ± 2.55, p < 0.001), IBR (0.81 ± 0.28 vs. 0.46 ± 0.39, p < 0.001), relative IPH% (0.3229 ± 0.1539 vs. 0.2233 ± 0.1960, p = 0.004) and prolonged eye-closing phase (0.18 ± 0.08 s vs. 0.15 ± 0.07 s, p = 0.032) and opening phase (0.35 ± 0.12 s vs. 0.28 ± 0.14 s, p = 0.015) compared to controls. In addition, Spearman's correlation analysis revealed a negative correlation between incomplete blinks and NIBUT (for first-NIBUT, r = -0.292, p = 0.004; for avg-NIBUT, r = -0.3512, p < 0.001) in children with long-term use of ortho-K.

CONCLUSION: The deep learning system based on U-net and Swim-Transformer achieved optimal performance in the observation of blinking characteristics. Children with long-term use of ortho-K presented an increase in the frequency and rate of incomplete blinks and prolonged eye closing phase and opening phase. The increased frequency of incomplete blinks was associated with decreased tear film stability, indicating the importance of monitoring children's blinking patterns as well as tear film status in clinical follow-up.

PMID:39935789 | PMC:PMC11811098 | DOI:10.3389/fcell.2024.1517240

J R Stat Soc Series B Stat Methodol. 2024 Aug 5;87(1):98-131. doi: 10.1093/jrsssb/qkae082. eCollection 2025 Feb.

ABSTRACT

While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set-'inferring the uncertainty of model parameters on the basis of observations'-has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. Extended Fiducial inference involves jointly imputing random errors realized in observations using stochastic gradient Markov chain Monte Carlo and estimating the inverse function using a sparse deep neural network (DNN). The consistency of the sparse DNN estimator ensures that the uncertainty embedded in observations is properly propagated to model parameters through the estimated inverse function, thereby validating downstream statistical inference. Compared to frequentist and Bayesian methods, EFI offers significant advantages in parameter estimation and hypothesis testing. Specifically, EFI provides higher fidelity in parameter estimation, especially when outliers are present in the observations; and eliminates the need for theoretical reference distributions in hypothesis testing, thereby automating the statistical inference process. Extended Fiducial inference also provides an innovative framework for semisupervised learning.

PMID:39935678 | PMC:PMC11809222 | DOI:10.1093/jrsssb/qkae082

Front Psychiatry. 2025 Jan 28;16:1508772. doi: 10.3389/fpsyt.2025.1508772. eCollection 2025.

ABSTRACT

INTRODUCTION: Depression is a serious mental health disease. Traditional scale-based depression diagnosis methods often have problems of strong subjectivity and high misdiagnosis rate, so it is particularly important to develop automatic diagnostic tools based on objective indicators.

METHODS: This study proposes a deep learning method that fuses multimodal data to automatically diagnose depression using facial video and audio data. We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM) to analyze the audio features. Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.

RESULTS: We conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). The experimental results show that we achieve robust accuracy on the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.51 in estimating PHQ-8 scores from recorded interviews.

DISCUSSION: Compared with existing methods, our model shows excellent performance in multi-modal information fusion, which is suitable for early evaluation of depression.

PMID:39935533 | PMC:PMC11811426 | DOI:10.3389/fpsyt.2025.1508772

J Multidiscip Healthc. 2025 Feb 7;18:675-695. doi: 10.2147/JMDH.S493873. eCollection 2025.

ABSTRACT

PURPOSE: Breast cancer is the most common major public health problems of women in the world. Until now, analyzing mammogram images is still the main method used by doctors to diagnose and detect breast cancers. However, this process usually depends on the experience of radiologists and is always very time consuming.

PATIENTS AND METHODS: We propose to introduce deep learning technology into the process for the facilitation of computer-aided diagnosis (CAD), and address the challenges of class imbalance, enhance the detection of small masses and multiple targets, and reduce false positives and negatives in mammogram analysis. Therefore, we adopted and enhanced RetinaNet to detect masses in mammogram images. Specifically, we introduced a novel modification to the network structure, where the feature map M5 is processed by the ReLU function prior to the original convolution kernel. This strategic adjustment was designed to prevent the loss of resolution for small mass features. Additionally, we introduced transfer learning techniques into training process through leveraging pre-trained weights from other RetinaNet applications, and fine-tuned our improved model using the INbreast dataset.

RESULTS: The aforementioned innovations facilitate superior performance of the enhanced RetiaNet model on the public dataset INbreast, as evidenced by a mAP (mean average precision) of 1.0000 and TPR (true positive rate) of 1.00 at 0.00 FPPI (false positive per image) on the INbreast dataset.

CONCLUSION: The experimental results demonstrate that our enhanced RetinaNet model defeats the existing models by having more generalization performance than other published studies, and it can also be applied to other types of patients to assist doctors in making a proper diagnosis.

PMID:39935433 | PMC:PMC11812562 | DOI:10.2147/JMDH.S493873

Front Genet. 2025 Jan 22;15:1464471. doi: 10.3389/fgene.2024.1464471. eCollection 2024.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.

METHODS: In this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.

RESULTS: These findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.

DISCUSSION: This study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies.

PMID:39935693 | PMC:PMC11811625 | DOI:10.3389/fgene.2024.1464471

J Inflamm Res. 2025 Feb 7;18:1899-1910. doi: 10.2147/JIR.S499029. eCollection 2025.

ABSTRACT

While TRIM proteins are extensively studied in the context of lung tumors, their roles in non-tumor chronic lung diseases remain underexplored. This review delves into the emerging significance of TRIM family proteins in the pathogenesis of idiopathic pulmonary fibrosis (IPF), asthma, chronic obstructive pulmonary disease (COPD), and pulmonary hypertension (PH). TRIM proteins modulate key pathological processes, including inflammation, fibrosis, and cellular remodeling, contributing to disease progression. We highlight their potential as biomarkers and therapeutic targets, offering promising avenues for drug development in these debilitating respiratory disorders. However, the translation of these findings into clinical applications faces significant challenges. These include the dual functional nature of TRIM proteins, their context-dependent roles, the complexity of their downstream signaling networks, and the limitations of current therapeutic strategies in achieving tissue-specific targeting with minimal off-target effects. Addressing these challenges will require innovative approaches and interdisciplinary efforts to unlock the therapeutic potential of TRIM proteins in non-tumor chronic lung diseases.

PMID:39935527 | PMC:PMC11812559 | DOI:10.2147/JIR.S499029

Front Plant Sci. 2025 Jan 28;16:1555255. doi: 10.3389/fpls.2025.1555255. eCollection 2025.

NO ABSTRACT

PMID:39935951 | PMC:PMC11810884 | DOI:10.3389/fpls.2025.1555255

Front Plant Sci. 2025 Jan 28;16:1533442. doi: 10.3389/fpls.2025.1533442. eCollection 2025.

ABSTRACT

INTRODUCTION: The genetic wealth present in pigmented rice varieties offer abundant variation in different sources of antioxidants to meet nutritional security targets among rice-consuming communities. There is limited knowledge of the dynamic changes in the lipidome of rice during germination and the corresponding genes associated with the antioxidant and anti-cancerous properties of lipophilic fractions of pigmented rice sprouts (PRS).

METHODS: In this study, we profiled the lipidome of diverse pigmented rice collections of germinated sprouts. Further, we employed Genome-wide association studies (GWAS), gene-set analysis, and targeted association analysis to identify the candidate genes linked to these lipids.

RESULTS: The genetic analyses revealed 72 candidate genes involved in the regulation of these accumulating lipids in PRS. Marker trait associations (MTA) analysis shown that the combination GGTAAC/ACAAGCTGGGCCC was associated with increased levels of unsaturated lipids and carotenoids, which likely underlie these beneficial effects. This superior MTA combination exhibited potent inhibitory activity against HCT116 and A549 cell lines, with average 1/IC50 values of 0.03 and 0.02 (mL/μg), respectively, compared to the inferior MTAs.

DISCUSSION: Collectively, our findings demonstrate that MTAs linked to selected GDSL esterase/lipase (GELP) genes, OsACP1, and lecithin-cholesterol acyltransferase significantly enhance antioxidant and anti-cancer properties, potentially through the mobilization of unsaturated lipids and carotenoids during germination. This study offers valuable insights into the health-promoting potential of germinated rice sprouts as a rich dietary source of antioxidants beneficial to human health.

PMID:39935946 | PMC:PMC11810972 | DOI:10.3389/fpls.2025.1533442

Front Genet. 2025 Jan 28;16:1547943. doi: 10.3389/fgene.2025.1547943. eCollection 2025.

NO ABSTRACT

PMID:39935834 | PMC:PMC11810898 | DOI:10.3389/fgene.2025.1547943

Front Plant Sci. 2025 Jan 28;15:1520474. doi: 10.3389/fpls.2024.1520474. eCollection 2024.

ABSTRACT

Seagrasses are a paraphyletic group of marine angiosperms and retain certain adaptations from the ancestors of all embryophytes in the transition to terrestrial environments. Among these adaptations is the production of flavonoids, versatile phenylpropanoid secondary metabolites that participate in a variety of stress responses. Certain features, such as catalytic promiscuity and metabolon interactions, allow flavonoid metabolism to expand to produce novel compounds and respond to a variety of stimuli. As marine environments expose seagrasses to a unique set of stresses, these plants display interesting flavonoid profiles, the functions of which are often not completely clear. Flavonoids will likely prove to be effective and versatile agents in combating the new host of stress conditions introduced to marine environments by anthropogenic climate change, which affects marine environments differently from terrestrial ones. These new stresses include increased sulfate levels, changes in salt concentration, changes in herbivore distributions, and ocean acidification, which all involve flavonoids as stress response mechanisms, though the role of flavonoids in combatting these climate change stresses is seldom discussed directly in the literature. Flavonoids can also be used to assess the health of seagrass meadows through an interplay between flavonoid and simple phenolic levels, which may prove to be useful in monitoring the response of seagrasses to climate change. Studies focusing on the genetics of flavonoid metabolism are limited for this group, but the large chalcone synthase gene families in some species may provide an interesting topic of research. Anthocyanins are typically studied separately from other flavonoids. The phenomenon of reddening in certain seagrass species typically focuses on the importance of anthocyanins as a UV-screening mechanism, while the role of anthocyanins in cold stress is discussed less often. Both of these stress response functions would be useful for adaptation to climate change-induced deviations in tidal patterns and emersion. However, ocean warming will likely lead to a decrease in anthocyanin content, which may impact the performance of intertidal seagrasses. This review highlights the importance of flavonoids in angiosperm stress response and adaptation, examines research on flavonoids in seagrasses, and hypothesizes on the importance of flavonoids in these organisms under climate change.

PMID:39935685 | PMC:PMC11810914 | DOI:10.3389/fpls.2024.1520474

Front Microbiol. 2025 Jan 28;16:1535114. doi: 10.3389/fmicb.2025.1535114. eCollection 2025.

ABSTRACT

Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) is a plant pathogenic bacterium that possesses complicated motility regulation pathways including a typical chemotaxis system. A significant portion of our understanding about the genes functioning in Pst DC3000 motility is based on comparison to other bacteria. This leaves uncertainty about whether gene functions are conserved, especially since specific regulatory modules can have opposite functions in sets of Pseudomonas. In this study, we used a competitive selection to enrich for mutants with altered swimming motility and used random barcode transposon-site sequencing (RB-TnSeq) to identify genes with significant roles in swimming motility. Besides many of the known or predicted chemotaxis and motility genes, our method identified PSPTO_0406 (dipA), PSPTO_1042 (chrR) and PSPTO_4229 (hypothetical protein) as novel motility regulators. PSPTO_0406 is a homolog of dipA, a known cyclic di-GMP degrading enzyme in P. aeruginosa. PSPTO_1042 is part of an extracytoplasmic sensing system that controls gene expression in response to reactive oxygen species, suggesting that PSPTO_1042 may function as part of a mechanism that enables Pst DC3000 to alter motility when encountering oxidative stressors. PSPTO_4229 encodes a protein containing an HD-related output domain (HDOD), but with no previously identified functions. We found that deletion and overexpression of PSPTO_4229 both reduce swimming motility, suggesting that its function is sensitive to expression level. We used the overexpression phenotype to screen for nonsense and missense mutants of PSPTO_4229 that no longer reduce swimming motility and found a pair of conserved arginine residues that are necessary for motility suppression. Together these results provide a global perspective on regulatory and structural genes controlling flagellar motility in Pst DC3000.

PMID:39935648 | PMC:PMC11813219 | DOI:10.3389/fmicb.2025.1535114

Front Microbiol. 2025 Jan 28;16:1534826. doi: 10.3389/fmicb.2025.1534826. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fmicb.2024.1481702.].

PMID:39935631 | PMC:PMC11813217 | DOI:10.3389/fmicb.2025.1534826

Alzheimers Dement (N Y). 2025 Feb 11;11(1):e70051. doi: 10.1002/trc2.70051. eCollection 2025 Jan-Mar.

ABSTRACT

INTRODUCTION: Brain steady-state gamma oscillations evoked using a non-invasive medical device (Spectris) have shown potential clinical benefits in patients with mild-moderate Alzheimer's disease (AD), including reduced functional and cognitive decline, reduced brain volume and myelin loss, and increased brain functional connectivity. We analyzed changes in cerebrospinal fluid (CSF) proteins after Spectris treatment in mild cognitive impairment (MCI) and their relationship to established biological pathways implicated in AD.

METHODS: Unbiased proteomic analysis of CSF samples from participants with amyloid-positive MCI (n = 10) was conducted from the FLICKER (NCT03543878) clinical trial. Participants used the Cognito Therapeutics medical device (Spectris), confirmed to evoke steady-state gamma oscillations. Participants were instructed to use the device daily for 1 hour each day during the trial. CSF was collected prior to the start of stimulation and after 4 and 8 weeks of treatment. The proteome was analyzed using tandem mass tag mass spectrometry.

RESULTS: Differential expression analysis of proteins at baseline and after 8 weeks of treatment (N = 5) revealed that 110 out of 2951 proteins met the significance threshold (analysis of variance, P < 0.05, no false discovery rate). Sixty proteins were upregulated, and 50 proteins were downregulated after treatment. Changes in protein expression were mapped to the consensus human AD protein network, representing co-expressed and functionally linked modules linked to cell type and biochemical pathways. Treatment altered CSF proteins linked to AD-related brain proteome modules, including those involved in myelination (proteolipid protein 1, ecotropic viral integration site 2A), synaptic and neuroimmune functions, and regulation of cellular lipid transportation. Biological pathway analysis revealed that most impacted pathways were associated with lipoproteins, cholesterol, phospholipids processing, and phosphatidylcholine biosynthesis.

DISCUSSION: The CSF proteomic changes observed in this study suggest pleiotropic effects on multiple pathways involved in AD, including myelination, synaptic and neuroimmune function, and lipid transport. These findings are also consistent with observations of white matter and myelin preservation after Spectris treatment of AD.

HIGHLIGHTS: We analyzed changes in cerebrospinal fluid (CSF) proteins in response to sensory-evoked gamma oscillations in individuals with mild cognitive impairment.Sensory evoked steady-state gamma oscillations were evoked by Spectris medical device.Changes in CSF proteins were observed after 8 weeks of daily 1 hour treatment.Affected proteins were related to myelination, synaptic and neuroimmune functions, and regulation of cellular lipid transportation.Proteomic changes support clinical outcomes and myelin preservation of Spectris treatment.

PMID:39935616 | PMC:PMC11812123 | DOI:10.1002/trc2.70051