Sci Transl Med. 2025 Feb 5;17(784):eadp8913. doi: 10.1126/scitranslmed.adp8913. Epub 2025 Feb 5.

ABSTRACT

Although oncogenic NRAS activates mitogen-activated protein kinase (MAPK) signaling, inhibition of the MAPK pathway is not therapeutically efficacious in NRAS-mutant (NRASMUT) tumors. Here, we report that selectively silencing the ribosomal protein S6 kinase 2 (S6K2) while preserving the activity of S6K1 perturbs lipid metabolism, enhances fatty acid unsaturation, and triggers lethal lipid peroxidation in NRASMUT melanoma cells that are resistant to MAPK inhibition. S6K2 depletion induces endoplasmic reticulum stress and peroxisome proliferator-activated receptor α (PPARα) activation, triggering cell death selectively in MAPK inhibitor-resistant melanoma. We found that combining PPARα agonists and polyunsaturated fatty acids phenocopied the effects of S6K2 abrogation, blocking tumor growth in both patient-derived xenografts and immunocompetent murine melanoma models. Collectively, our study establishes S6K2 and its effector subnetwork as promising targets for NRASMUT melanomas that are resistant to global MAPK pathway inhibitors.

PMID:39908352 | DOI:10.1126/scitranslmed.adp8913

Cell Rep. 2025 Feb 4;44(2):115274. doi: 10.1016/j.celrep.2025.115274. Online ahead of print.

ABSTRACT

The mechanism by which Wnt signaling, an essential pathway controlling development and disease, stabilizes β-catenin has been a subject of debate over the last four decades. Casein kinase 1α (CK1α) functions as a pivotal negative regulator of this signaling pathway, initiating the events that destabilize β-catenin. However, whether and how CK1α activity is regulated in Wnt-off and Wnt-on states remains poorly understood. We now show that CK1α activity requires its association with the α catalytic subunit of protein phosphatase 2A (PPP2CA) on AXIN, the scaffold protein of the β-catenin destruction complex. Wnt stimulation induces the dissociation of PPP2CA from CK1α, resulting in CK1α autophosphorylation and its consequent inactivation. Moreover, autophosphorylated CK1α is enriched in a subset of colorectal cancers (CRCs) harboring constitutive Wnt activation. Our findings identify a mechanism by which Wnt stimulation inactivates CK1α, filling a critical gap in our understanding of Wnt signaling, with relevance for CRC.

PMID:39908140 | DOI:10.1016/j.celrep.2025.115274

Braz J Med Biol Res. 2025 Jan 31;58:e14060. doi: 10.1590/1414-431X2024e14060. eCollection 2025.

ABSTRACT

Cancer is the second leading cause of death worldwide. Cancer cachexia is a multifactorial catabolic syndrome responsible for almost one third of cancer-related deaths. Drug repurposing has been used in oncological research and drugs like clenbuterol and metformin seem to be reasonable candidates in the context of cancer cachexia, because the former is a β2-agonist that stimulates muscle gain and the latter has anti-inflammatory properties. The aim of this study was to assess the effects of a short-term treatment with metformin and clenbuterol, isolated or combined, on tumor growth and cancer cachexia parameters in Walker 256 tumor-bearing rats, a model of cancer cachexia. To this end, Wistar rats were separated into 8 groups and 4 of them were injected with Walker 256 tumor cells (W groups). Control (C) and W groups received the following treatments: metformin (M), clenbuterol (Cb), or metformin combined with clenbuterol (MCb). Body and tumor weight, metabolic parameters, and oxidative damage in the tumor were assessed. Compared to the C group, the W group showed body weight loss, hypoglycemia, hyperlactatemia, and hypertriacylglycerolemia. None of the treatments could reverse body weight loss, although they reversed the alterations of the assessed plasma metabolic parameters. Surprisingly, only clenbuterol alone reduced tumor weight. Hydrogen peroxide production and lipid peroxidation in tumor tissue was increased in this group. In conclusion, metformin and clenbuterol ameliorated metabolic cachexia parameters in Walker tumor-bearing rats, but only clenbuterol reduced the tumor weight, probably, through a lipid peroxidation-dependent cell death.

PMID:39907424 | DOI:10.1590/1414-431X2024e14060

Microbiol Spectr. 2025 Feb 5:e0301024. doi: 10.1128/spectrum.03010-24. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa is a bacterial pathogen that is a major cause of lung infections in cystic fibrosis (CF) and other patients. Isolates of P. aeruginosa from CF patients commonly carry filamentous phages (Pf phages), which constitute a family of temperate phages known to be related to biofilm production and antibiotic sequestration. In this study, we identified 12 new Pf phage genomes in a collection of clinical isolates of P. aeruginosa from CF patients. Study of the anti-phage defense systems in the bacterial isolates revealed the presence of 89 such systems, of which eight were encoded in the Pf phage genomes. Finally, although a weak relation between resistance to phage infection and the number of anti-phage defense systems was detected, it was observed that the phage resistance was related to the presence of Pf phages and the anti-phage defense systems encoded in these phages.IMPORTANCEBacteria harbor a wide range of defense mechanisms to avoid phage infections that hamper the application of phage therapy because they can lead to the rapid acquisition of phage resistance. In this study, eight anti-phage defense systems were found in the genome of 12 Pf phages that were presents in 56% of the CF isolates of P. aeruginosa. The high prevalence of these phages underlines the importance of our findings about newly discovered filamentous phages and the role of these phages in resistance to phage infections. Thus, the knowledge of the anti-defense system in the Pf phage genomes could be useful in assessing the possible application of phage therapy to treat an infectious disease.

PMID:39907445 | DOI:10.1128/spectrum.03010-24

Med Biol Eng Comput. 2025 Feb 5. doi: 10.1007/s11517-025-03291-4. Online ahead of print.

ABSTRACT

Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical for improving cure rates and prognosis. Deep learning-based breast cancer classification algorithms have substantially alleviated the burden on medical personnel. However, existing breast cancer diagnosis models face notable limitations which are challenging to obtain in clinical settings, such as reliance on a large volume of labeled samples, an inability to comprehensively extract features from breast cancer images, and susceptibility to overfitting on account of imbalanced class distribution. Therefore, we propose the class-aware multi-level attention learning model focused on semi-supervised breast cancer diagnosis to effectively reduce the dependency on extensive data annotation. Additionally, we develop the multi-level fusion attention learning module, which integrates multiple mutual attention components across different layers, allowing the model to precisely identify critical regions for lesion categorization. Finally, we design the class-aware adaptive pseudo-labeling module which adaptively predicts category distribution in unlabeled data, and directs the model to focus on underrepresented categories, ensuring a balanced learning process. Experimental results on the BACH dataset demonstrate that our proposed model achieves an accuracy of 86.7% with only 40% labeled microscopic data, showcasing its outstanding contribution to semi-supervised breast cancer diagnosis.

PMID:39907850 | DOI:10.1007/s11517-025-03291-4

Eur J Nucl Med Mol Imaging. 2025 Feb 5. doi: 10.1007/s00259-025-07117-1. Online ahead of print.

ABSTRACT

INTRODUCTION: Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using deep learning on multi-tracer, multi-scanner, and multi-center datasets.

METHODS: In the current study, we employed six datasets (from 12 cameras) for various tasks and purposes. Dataset #1 (93 patients, 99mTc-MDP) was used to develop the 2D-planar segmentation and localization models. Dataset #2 (216 patients, 99mTc-DPD) was used for the detection (grade 0 vs. grades 1, 2, and 3) and scoring (0 and 1 vs. grades 2 and 3) of ATTR-CM. Datasets #3 (41 patients, 99mTc-HDP), #4 (53 patients, 99mTc-PYP), and #5 (129 patients, 99mTc-DPD) were used as external centers. ATTR-CM detection and scouring were performed by two physicians in each center. Moreover, Dataset #6 consisting of 3215 patients without labels, was employed for retrospective model performance evaluation. Different regions of interest were cropped and fed into the classification model for the detection and scoring of ATTR-CM. Ensembling was performed on the outputs of different models to improve their performance. Model performance was measured by classification accuracy, sensitivity, specificity, and AUC. Grad-CAM and saliency maps were generated to explain the models' decision-making process.

RESULTS: In the internal test set, all models for detection and scoring achieved an AUC of more than 0.95 and an F1 score of more than 0.90. For detection in the external dataset, AUCs of 0.93, 0.95, and 1 were achieved for datasets 3, 4, and 5, respectively. For the scoring task, AUCs of 0.95, 0.83, and 0.96 were achieved for these datasets, respectively. In dataset #6, we found ten cases flagged as ATTR-CM by the network. Out of these, four cases were confirmed by a nuclear medicine specialist as possibly having ATTR-CM. GradCam and saliency maps showed that the deep-learning models focused on clinically relevant cardiac areas.

CONCLUSION: In the current study, we developed and evaluated a fully automated pipeline to detect and score ATTR-CM using large multi-tracer, multi-scanner, and multi-center datasets, achieving high performance on total body images. This fully automated pipeline could lead to more timely and accurate diagnoses, ultimately improving patient outcomes.

PMID:39907796 | DOI:10.1007/s00259-025-07117-1

Eur Radiol. 2025 Feb 5. doi: 10.1007/s00330-025-11406-6. Online ahead of print.

ABSTRACT

OBJECTIVES: The aim of this work is to evaluate the performance of deep learning (DL) models for breast cancer diagnosis with MRI.

MATERIALS AND METHODS: A literature search was conducted on Web of Science, PubMed, and IEEE Xplore for relevant studies published from January 2015 to February 2024. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42024485371). The quality assessment of diagnostic accuracy studies-2 (QUADAS2) tool and the Must AI Criteria-10 (MAIC-10) checklist were used to assess quality and risk of bias. The meta-analysis included studies reporting DL for breast cancer diagnosis and their performance, from which pooled summary estimates for the area under the curve (AUC), sensitivity, and specificity were calculated.

RESULTS: A total of 40 studies were included, of which only 21 were eligible for quantitative analysis. Convolutional neural networks (CNNs) were used in 62.5% (25/40) of the implemented models, with the remaining 37.5% (15/40) hybrid composite models (HCMs). The pooled estimates of AUC, sensitivity, and specificity were 0.90 (95% CI: 0.87, 0.93), 88% (95% CI: 86, 91%), and 90% (95% CI: 87, 93%), respectively.

CONCLUSIONS: DL models used for breast cancer diagnosis on MRI achieve high performance. However, there is considerable inherent variability in this analysis. Therefore, continuous evaluation and refinement of DL models is essential to ensure their practicality in the clinical setting.

KEY POINTS: Question Can DL models improve diagnostic accuracy in breast MRI, addressing challenges like overfitting and heterogeneity in study designs and imaging sequences? Findings DL achieved high diagnostic accuracy (AUC 0.90, sensitivity 88%, specificity 90%) in breast MRI, with training size significantly impacting performance metrics (p < 0.001). Clinical relevance DL models demonstrate high accuracy in breast cancer diagnosis using MRI, showing the potential to enhance diagnostic confidence and reduce radiologist workload, especially with larger datasets minimizing overfitting and improving clinical reliability.

PMID:39907762 | DOI:10.1007/s00330-025-11406-6

Radiol Artif Intell. 2025 Feb 5:e240039. doi: 10.1148/ryai.240039. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate cancer detection and marker placement accuracy of two artificial intelligence (AI) models developed for interpretation of screening mammograms. Materials and Methods This retrospective study included data from 129 434 screening examinations (all female, mean age 59.2, SD = 5.8) performed between January 2008 and December 2018 in BreastScreen Norway. Model A was commercially available and B was an in-house model. Area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CIs) were calculated. The study defined 3.2% and 11.1% of the examinations with the highest AI scores as positive, threshold 1 and 2, respectively. A radiologic review assessed location of AI markings and classified interval cancers as true or false negative. Results The AUC was 0.93 (95% CI: 0.92-0.94) for model A and B when including screen-detected and interval cancers. Model A identified 82.5% (611/741) of the screen-detected cancers at threshold 1 and 92.4% (685/741) at threshold 2. For model B, the numbers were 81.8% (606/741) and 93.7% (694/741), respectively. The AI markings were correctly localized for all screen-detected cancers identified by both models and 82% (56/68) of the interval cancers for model A and 79% (54/68) for B. At the review, 21.6% (45/208) of the interval cancers were identified at the preceding screening by either or both models, correctly localized and classified as false negative (n = 17) or with minimal signs of malignancy (n = 28). Conclusion Both AI models showed promising performance for cancer detection on screening mammograms. The AI markings corresponded well to the true cancer locations. ©RSNA, 2025.

PMID:39907587 | DOI:10.1148/ryai.240039

Radiol Artif Intell. 2025 Feb 5:e240167. doi: 10.1148/ryai.240167. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospective study introduces PIA, a novel self-supervised deep learning model that integrates a three-compartment diffusion-relaxation model with hybrid multidimensional MRI. PIA was trained to encode the biophysical model into a deep neural network to predict measurements of tissue-specific biomarkers for PCa without extensive training data requirements. Comprehensive in-silico and in-vivo experiments, using histopathology measurements as the reference standard, were conducted to validate the model's efficacy in comparison to the traditional Non-Linear Least Squares (NLLS) algorithm. PIA's robustness to noise was tested in in-silico experiments with varying signal-to-noise ratio (SNR) conditions, and in-vivo performance for estimating volume fractions was evaluated in 21 patients (mean age 60 (SD:6.6) years; all male) with PCa (n = 71 regions of interest). Evaluation metrics included the intraclass correlation coefficient (ICC) and Pearson correlation coefficient. Results PIA predicted the reference standard tissue parameters with high accuracy, outperforming conventional NLLS methods, especially under noisy conditions (rs = 0.80 versus 0.65, P < .001 for epithelium volume at SNR = 20:1). In in-vivo validation, PIA's noninvasive volume fraction estimates matched quantitative histology (ICC = 0.94, 0.85 and 0.92 for epithelium, stroma, and lumen compartments, respectively, P < .001 for all). PIA's measurements strongly correlated with PCa aggressiveness (r = 0.75, P < .001). Furthermore, PIA ran 10,000 faster than NLLS (0.18 seconds versus 40 minutes per image). Conclusion PIA provided accurate prostate tissue biomarker measurements from MRI data with better robustness to noise and computational efficiency compared with the NLLS algorithm. The results demonstrate the potential of PIA as an accurate, noninvasive, and explainable AI method for PCa detection. ©RSNA, 2025.

PMID:39907585 | DOI:10.1148/ryai.240167

Int J Neural Syst. 2025 Feb 4:2550014. doi: 10.1142/S0129065725500145. Online ahead of print.

ABSTRACT

Cross-user variability is a well-known challenge that leads to severe performance degradation and impacts the robustness of practical myoelectric control systems. To address this issue, a novel method for myoelectric recognition of finger movement patterns is proposed by incorporating a neural decoding approach with unsupervised domain adaption (UDA) learning. In our method, the neural decoding approach is implemented by extracting microscopic features characterizing individual motor unit (MU) activities obtained from a two-stage online surface electromyogram (SEMG) decomposition. A specific deep learning model is designed and initially trained using labeled data from a set of existing users. The model can update adaptively when recognizing the movement patterns of a new user. The final movement pattern was determined by a fuzzy weighted decision strategy. SEMG signals were collected from the finger extensor muscles of 15 subjects to detect seven dexterous finger-movement patterns. The proposed method achieved a movement pattern recognition accuracy of ([Formula: see text])% over seven movements under cross-user testing scenarios, much higher than that of the conventional methods using global SEMG features. Our study presents a novel robust myoelectric pattern recognition approach at a fine-grained MU level, with wide applications in neural interface and prosthesis control.

PMID:39907499 | DOI:10.1142/S0129065725500145

Laryngoscope. 2025 Feb 5. doi: 10.1002/lary.32036. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop and validate a deep-learning classifier trained on voice data extracted from videolaryngostroboscopy recordings, differentiating between three different vocal fold (VF) states: healthy (HVF), unilateral paralysis (UVFP), and VF lesions, including benign and malignant pathologies.

METHODS: Patients with UVFP (n = 105), VF lesions (n = 63), and HVF (n = 41) were retrospectively identified. Voice samples were extracted from stroboscopic videos (Pentax Laryngeal Strobe Model 9400), including sustained /i/ phonation, pitch glide, and /i/ sniff task. Extracted audio files were converted into Mel-spectrograms. Voice samples were independently divided into training (80%), validation (10%), and test (10%) by patient. Pretrained ResNet18 models were trained to classify (1) HVF and pathological VF (lesions and UVFP), and (2) HVF, UVFP, and VF lesions. Both classifiers were further validated on an external dataset consisting of 12 UVFP, 13 VF lesions, and 15 HVF patients. Model performances were evaluated by accuracy and F1-score.

RESULTS: When evaluated on a hold-out test set, the binary classifier demonstrated stronger performance compared to the multi-class classifier (accuracy 83% vs. 40%; F1-score 0.90 vs. 0.36). When evaluated on an external dataset, the binary classifier achieved an accuracy of 63% and F1-score of 0.48, compared to 35% and 0.25 for the multi-class classifier.

CONCLUSIONS: Deep-learning classifiers differentiating HVF, UVFP, and VF lesions were developed using voice data from stroboscopic videos. Although healthy and pathological voice were differentiated with moderate accuracy, multi-class classification lowered model performance. The model performed poorly on an external dataset. Voice captured in stroboscopic videos may have limited diagnostic value, though further studies are needed.

LEVEL OF EVIDENCE: 4 Laryngoscope, 2025.

PMID:39907244 | DOI:10.1002/lary.32036

NMR Biomed. 2025 Mar;38(3):e70002. doi: 10.1002/nbm.70002.

ABSTRACT

The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field strengths of 3 and 0.55 T. We trained DL reconstructions using two different training strategies, supervised (SV) and self-supervised (SSV), with retrospectively sixfold undersampled data obtained at 3 T. We then evaluated the DL reconstructions against standard techniques, parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. We also tested DL reconstructions with prospectively accelerated acquisitions and evaluated their robustness when changing fields strengths from 3 to 0.55 T. DL reconstructions demonstrated a reduction in average acquisition time from 599/542 to 255/180 s for MRCP at 3 T/0.55 T. In both retrospective and prospective undersampling, PSNR and SSIM of DL reconstructions were higher than those of PI and CS. At the same time, DL reconstructions preserved the image quality of undersampled data, including sharpness and the visibility of hepatobiliary ducts. In addition, both DL approaches produced high-quality reconstructions at 0.55 T. In summary, DL reconstructions trained for highly accelerated MRCP enabled a reduction in acquisition time by a factor of 2.4/3.0 at 3 T/0.55 T while maintaining the image quality of conventional acquisitions.

PMID:39907193 | DOI:10.1002/nbm.70002

J Proteome Res. 2025 Feb 5. doi: 10.1021/acs.jproteome.4c00907. Online ahead of print.

ABSTRACT

Protein sulfation can be crucial in regulating protein-protein interactions but remains largely underexplored. Sulfation is nearly isobaric to phosphorylation, making it particularly challenging to investigate using mass spectrometry. The degree to which tyrosine sulfation (sY) is misidentified as phosphorylation (pY) is, thus, an unresolved concern. This study explores the extent of sY misidentification within the human phosphoproteome by distinguishing between sulfation and phosphorylation based on their mass difference. Using Gaussian mixture models (GMMs), we screened ∼45 M peptide-spectrum matches (PSMs) from the PeptideAtlas human phosphoproteome build for peptidoforms with mass error shifts indicative of sulfation. This analysis pinpointed 104 candidate sulfated peptidoforms, backed up by Gene Ontology (GO) terms and custom terms linked to sulfation. False positive filtering by manual annotation resulted in 31 convincing peptidoforms spanning 7 known and 7 novel sY sites. Y47 in calumenin was particularly intriguing since mass error shifts, acidic motif conservation, and MS2 neutral loss patterns characteristic of sulfation provided strong evidence that this site is sulfated rather than phosphorylated. Overall, although misidentification of sulfation in phosphoproteomics data sets derived from cell and tissue intracellular extracts can occur, it appears relatively rare and should not be considered a substantive confounding factor in high-quality phosphoproteomics data sets.

PMID:39907647 | DOI:10.1021/acs.jproteome.4c00907

Elife. 2025 Feb 5;13:RP97424. doi: 10.7554/eLife.97424.

ABSTRACT

Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfactXMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfactXMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfactXMBD showed advantages in major quantity-control metrics, such as branch division accuracy and trajectory topology, outperforming most established methods. In real datasets, MGPfactXMBD recovered the critical pathways and cell types in microglia development with experimentally valid regulons and markers. Furthermore, MGPfactXMBD discovered evolutionary trajectories of tumor-associated CD8+ T cells and yielded new subtypes of CD8+ T cells with gene expression signatures significantly predictive of the responses to immune checkpoint inhibitor in independent cohorts. In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.

PMID:39907554 | DOI:10.7554/eLife.97424

Plant Physiol. 2025 Feb 5:kiaf050. doi: 10.1093/plphys/kiaf050. Online ahead of print.

NO ABSTRACT

PMID:39907471 | DOI:10.1093/plphys/kiaf050

Appl Environ Microbiol. 2025 Feb 5:e0213424. doi: 10.1128/aem.02134-24. Online ahead of print.

ABSTRACT

Beauveria bassiana (Bb) is one of the most widely used biocontrol agents, and its products constitute more than one-third of the global market share of fungal insecticides. Solid-state fermentation (SSF) is widely used in the production of Beauveria bassiana (Bb) because of its economic practicality and high production efficiency. However, the heat generated during fermentation can sharply reduce both the yield and quality of Bb, and current industrial methods to mitigate high temperatures during fermentation are inadequate, leading to increased production costs. Thus, exploring the underlying mechanism of how heat is produced by Bb is crucial for improving the SSF procedure and yield. This study employed multiomics data analysis of Bb during SSF to explore the relationships between fungal fermentation and environmental factors. We found that the heat production period for SSF was 12 hours to 48 hours post-inoculation. To further explore the underlying mechanism during this heating period, we identified 454 temperature-correlated metabolites (TCMs) and 1,994 temperature-correlated genes (TCGs). Annotations of the above TCMs and TCGs revealed significant enrichment in the arginine biosynthesis pathway; specifically, the expression level of glutamine synthetase, a TCG, decreased with fermentation time, whereas the expression levels of the TCGs L-arginine and L-glutamine increased with fermentation time, and glutamine synthetase and L-glutamine in the arginine biosynthesis pathway cycle produced the end product L-arginine. Furthermore, when the substrates of the SSF were treated with exogenous arginine, the temperature peak of the SSF significantly decreased with increasing concentration of exogenously added arginine.IMPORTANCEA large amount of experimental evidence from the field has shown that Bb is an irreplaceable mature product that protects the health of our agriculture and ecosystem. In addition to high efficiency and host extensiveness, low cost is a critical merit that makes Bb products frequently used in the field. However, the growing cost of power and labor in the Bb industry, especially the SSF procedure, has significantly increased the price of its products, thus restricting the use of Bb in the field. This study not only fills the theoretical knowledge gaps concerning the molecular basis of the interrelationship between Bb and the fermentation environment during SSF but also provides an economical and applicable strategy (the addition of arginine to the fermentation media) to further lower the cost and increase the yield of Bb during SSF at the industrial level.

PMID:39907454 | DOI:10.1128/aem.02134-24

Bioinformatics. 2025 Feb 5:btaf048. doi: 10.1093/bioinformatics/btaf048. Online ahead of print.

ABSTRACT

SUMMARY: We present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration.

AVAILABILITY AND IMPLEMENTATION: NetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons, and deposited in Zenodo at https://doi.org/10.5281/zenodo.14719118 .

SUPPLEMENTARY DATA: Contribution guidelines, additional figures, and descriptions for data, knowledge, methods, evaluation strategies and their implementation are available in the Supplementary Data and in the NetworkCommons documentation at https://networkcommons.readthedocs.io/.

PMID:39907203 | DOI:10.1093/bioinformatics/btaf048

Heliyon. 2025 Jan 16;11(2):e42052. doi: 10.1016/j.heliyon.2025.e42052. eCollection 2025 Jan 30.

ABSTRACT

Glioblastoma (GBM) is one of the most aggressive cancers due to its high mortality rate in spite of intensive treatment. It may be happened because of drug resistance against their typical receptors, since these receptor genes are often mutated by environmental stress. So identifying mutated oncodriver genes which could be used as potential drug target is essential in order to develop effective new therapeutic drugs as well as better prognosis for GBM patients. In this study, we analyzed whole exome sequencing (WES) profiles of NCBI database on GBM and matched-normal (control) samples originated from astrocyte like neural stem cells (NSC) of subventricular zone (SVZ) to explore GBM-causing mutated oncodriver genes, since SVZ is considered as the origin of GBM development. We detected 16 mutated oncodriver genes. Then, filtering by differential co-expression analysis based on independent RNA-Seq profiles of CGGA database revealed 10 genes as dysregulated oncodriver genes. Following that, 3 significantly overexpressed oncodriver genes (MTCH2, VWF, and WDR89) were identified as potential drug targets. Then molecular mechanisms of GBM development were investigated by these three overexpressed driver genes through gene ontology (GO), KEGG-pathways, Gene regulatory network (GRN) and mutation analysis. Finally, overexpressed oncodriver genes guided top-ranked six drug agents (Irinotecan, Imatinib, etoposide, pazopanib, trametinib and cabozanitinib) were recommended against GBM through molecular docking study. Most of our findings received support by the literature review also. Therefore, the findings of this study might carry potential values to the wet-lab researchers for further investigation in terms of diagnosis and therapies of GBM.

PMID:39906820 | PMC:PMC11791140 | DOI:10.1016/j.heliyon.2025.e42052

Parasit Vectors. 2025 Feb 4;18(1):40. doi: 10.1186/s13071-024-06648-0.

ABSTRACT

BACKGROUND: Schistosomiasis, which is caused by the parasite Schistosoma mansoni as well as other species of the trematode genus Schistosoma, leads to chronic inflammation and finally to liver fibrosis. If untreated, the disease can cause life-threatening complications. The current treatment of schistosomiasis relies on a single drug, praziquantel (PZQ). However, there is increasing concern about emerging resistance to PZQ due to its frequent use.

METHODS: To identify potential alternative drugs for repurposing, the Open Global Health Library (OGHL) was screened in vitro, using two different screening workflows at two institutions, against adult S. mansoni couples and newly transformed schistosomula. This was followed by confirmation of the effects of the lead structures against adult worms.

RESULTS: In vitro screening at one of the institutions identified two fast-acting substances affecting worm physiology (OGHL00022, OGHL00121). The effects of the two lead structures were investigated in more detail by confocal laser scanning microscopy and 5-ethynyl 2´-deoxyuridine (EdU) assays to assess morphological effects and stem cell effects. Both substances showed negative effects on stem cell proliferation in S. mansoni but no further morphological changes. The EC50values of both compounds were determined, with values for compound OGHL00022 of 5.955 µM for pairing stability, 10.88 µM for attachment, and 18.77 µM for motility, while the values for compound OGHL00121 were 7.088 µM for pairing stability, 8.065 µM for attachment, and 6.297 µM for motility 24 h after treatment. Furthermore, S. mansoni couples were treated in vitro with these two lead structures simultaneously to check for additive effects, which were found with respect to reduced motility. The second in vitro screening, primarily against newly transformed schistosomula and secondarily against adult worms, identified four lead structures in total (OGHL00006, OGHL00022, OGHL00169, OGHL00217). In addition, one of the tested analogues of the hits OGHL00006, OGHL00169, and OGHL00217 showed effects on both stages.

CONCLUSIONS: In two independent in vitro screening approaches against two stages of S. mansoni one common interesting structure with rapid effects was identified, OGHL00022, which provides opportunities for further development.

PMID:39905554 | DOI:10.1186/s13071-024-06648-0

Virol J. 2025 Feb 4;22(1):26. doi: 10.1186/s12985-025-02622-z.

ABSTRACT

Mosquito-borne viruses (MBVs) are a major global health threat, causing significant morbidity and mortality. MBVs belong to several distinct viral families, each with unique characteristics. The primary families include Flaviviridae (e.g., Dengue, Zika, West Nile, Yellow Fever, Japanese Encephalitis), transmitted predominantly by Aedes and Culex mosquitoes; Togaviridae, which consists of the genus Alphavirus (e.g., Chikungunya, Eastern and Western Equine Encephalitis viruses), also transmitted by Aedes and Culex; Bunyaviridae (recently reorganized), containing viruses like Rift Valley Fever and Oropouche virus, transmitted by mosquitoes and sometimes sandflies; and Reoviridae, which includes the genus Orbivirus (e.g., West Nile and Bluetongue viruses), primarily affecting animals and transmitted by mosquitoes and sandflies. Despite extensive research, effective antiviral treatments for MBVs remain scarce, and current therapies mainly provide symptomatic relief and supportive care. This review examines the viral components and cellular and immune factors involved in the life cycle of MBVs. It also highlights recent advances in antiviral strategies targeting host factors such as lipid metabolism, ion channels, and proteasomes, as well as viral targets like NS2B-NS3 proteases and nonstructural proteins. Additionally, it explores immunomodulatory therapies to enhance antiviral responses and emphasizes the potential of drug repurposing, bioinformatics, artificial intelligence, and deep learning in identifying novel antiviral candidates. Continued research is crucial in mitigating MBVs' impact and preventing future outbreaks.

PMID:39905499 | DOI:10.1186/s12985-025-02622-z