Front Neurorobot. 2025 Jul 28;19:1604453. doi: 10.3389/fnbot.2025.1604453. eCollection 2025.

ABSTRACT

Myoelectric control systems translate electromyographic signals (EMG) from muscles into movement intentions, allowing control over various interfaces, such as prosthetics, wearable devices, and robotics. However, a major challenge lies in enhancing the system's ability to generalize, personalize, and adapt to the high variability of EMG signals. Artificial intelligence, particularly neural networks, has shown promising decoding performance when applied to large datasets. However, highly parameterized deep neural networks usually require extensive user-specific data with ground truth labels to learn individual unique EMG patterns. However, the characteristics of the EMG signal can change significantly over time, even for the same user, leading to performance degradation during extended use. In this work, we propose an innovative three-stage neural network training scheme designed to progressively develop an adaptive workflow, improving and maintaining the network performance on 28 subjects over 2 days. Experiments demonstrate the importance and necessity of each stage in the proposed framework.

PMID:40791943 | PMC:PMC12336220 | DOI:10.3389/fnbot.2025.1604453

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10981108. Epub 2025 May 12.

ABSTRACT

With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in complex segmentation tasks. The rise of Vision Transformer (ViT) has effectively compensated for this deficiency of CNNs and promoted the application of ViT-based U-networks in medical image segmentation. However, the high computational demands of ViT make it unsuitable for many medical devices and mobile platforms with limited resources, restricting its deployment on resource-constrained and edge devices. To address this, we propose EViT-UNet, an efficient ViT-based segmentation network that reduces computational complexity while maintaining accuracy, making it ideal for resource-constrained medical devices. EViT-UNet is built on a U-shaped architecture, comprising an encoder, decoder, bottleneck layer, and skip connections, combining convolutional operations with self-attention mechanisms to optimize efficiency. Experimental results demonstrate that EViT-UNet achieves high accuracy in medical image segmentation while significantly reducing computational complexity. The code is available at https://github.com/Retinal-Research/EVIT-UNET.

PMID:40791942 | PMC:PMC12337706 | DOI:10.1109/isbi60581.2025.10981108

Front Neurol. 2025 Jul 28;16:1615523. doi: 10.3389/fneur.2025.1615523. eCollection 2025.

ABSTRACT

Brain diseases pose a significant global health challenge due to their complexity and the limitations of traditional medical strategies. Recent advancements in artificial intelligence (AI), especially deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), offer powerful new tools for analysis. These neural networks are effective at extracting complex patterns from high-dimensional data. By integrating diverse data sources-such as neuroimaging, multi-omics, and clinical information-multimodal AI provides the comprehensive view needed to understand intricate disease mechanisms. This review outlines how these technologies enhance precision drug development and enable closed-loop treatment systems for brain disorders. Key applications include improving diagnostic accuracy, identifying novel biomarkers, accelerating drug discovery through target identification and virtual screening, and predicting patient-specific treatment responses. These AI-driven methods have the potential to shift medicine from a one-size-fits-all model to a personalized approach, with diagnostics and therapies tailored to individual profiles. However, realizing this potential requires addressing significant challenges related to data access, model interpretability, clinical validation, and practical integration.

PMID:40791911 | PMC:PMC12336123 | DOI:10.3389/fneur.2025.1615523

medRxiv [Preprint]. 2025 Jul 14:2025.07.11.25331386. doi: 10.1101/2025.07.11.25331386.

ABSTRACT

BACKGROUND: Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care right heart catheterization (RHC). Seismocardiography (SCG), a non-invasive technique which records subtle chest wall vibrations generated by cardiac mechanical activity, may offer a promising alternative for CO determination.

OBJECTIVES: To develop and evaluate a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC.

METHODS: We trained a deep convolutional neural network for CO estimation using an open-access dataset comprising 73 heart failure patients with simultaneous RHC, SCG, and ECG recordings. Model performance was evaluated using a rotating leave-pair-out cross-validation strategy.

RESULTS: When estimating CO, the deep learning model achieved a mean bias of -0.35 L/min with limits of agreement (LoA) from -2.21 to 1.51 L/min. When predicting cardiac index in patients with a reference index < 2.2 L/min/m 2 , the model yielded a mean bias of 0.07 L/min/m 2 with LoA from -0.35 to 0.48 L/min/m 2 .

CONCLUSIONS: This study demonstrates the feasibility of using deep learning in combination with wearable SCG sensors to non-invasively estimate CO. Model performance was particularly strong in low-output states. These findings highlight the potential of SCG-based monitoring to augment clinical decision-making in settings where invasive measurements are impractical or unavailable. Prospective multicenter validation is needed to confirm generalizability and assess clinical impact.

SOURCES OF SUPPORT: This work was supported by NIH grants T32 HL129964 (N.J.K.), K08 ES037420 (N.J.K.), R01 HL124021 (S.Y.C.), R01 HL122596 (S.Y.C.); R01 HL151228 (S.Y.C.); the McKamish Family Foundation, the Hemophilia Center of Western Pennsylvania, and the Institute for Transfusion Medicine (N.J.K.), United Therapeutics Jenesis Innovative Research Award (N.J.K.), and the Pulmonary Hypertension Association (N.J.K.).

DISCLOSURES: S.Y.C. has served as a consultant for Merck, Janssen, and United Therapeutics; S.Y.C. is a director, officer, and shareholder in Synhale Therapeutics and Amlysion Therapeutics; S.Y.C. and N.J.K. hold research grants from United Therapeutics; S.Y.C. holds research grants from Bayer and the WoodNext Foundation. S.Y.C. has filed patent applications regarding the targeting of metabolism in pulmonary hypertension. Other authors: none.

TWITTER SUMMARY: We developed a deep learning model to non-invasively estimate cardiac output from wearable seismocardiogram (SCG) signals in patients undergoing right heart catheterization. This is the largest study to date using SCG for noninvasive cardiac output monitoring. #HeartFailure, #WearableTech, #AIInCardiology.

PMID:40791697 | PMC:PMC12338910 | DOI:10.1101/2025.07.11.25331386

medRxiv [Preprint]. 2025 Jul 17:2025.07.16.25331662. doi: 10.1101/2025.07.16.25331662.

ABSTRACT

Biomedical data are inherently multimodal, capturing complementary aspects of a patient condition. Deep learning (DL) algorithms that integrate multiple biomedical modalities can significantly improve clinical decisionmaking, especially in domains where collecting data is not simple and data are highly heterogeneous. However, developing effective and reliable multimodal DL methods remains challenging, requiring large training datasets with paired samples from modalities of interest. An increasing number of de-identifed biomedical datasets are publicly accessible, though they still tend to be unimodal. For example, several publicly available skin lesion datasets aid automated dermatology clinical decision-making. Still, they lack annotated reports paired with the images, thereby limiting the advance and use of multimodal DL algorithms. This work presents a strategy exploiting real and synthesized data in a multimodal architecture that encodes finegrained text representations within image embeddings to create a robust representation of skin lesion data. Large language models (LLMs) are used to synthesize textual descriptions from image metadata that are subsequently paired with the original skin lesion images and used for model development. The architecture is evaluated on the classification of skin lesion images, considering nine internal and external data sources. The proposed multimodal representation outperforms the unimodal one on the classification of skin lesion images, achieving superior performance in every tested dataset.

PMID:40791679 | PMC:PMC12338939 | DOI:10.1101/2025.07.16.25331662

Data Brief. 2025 Jul 20;62:111912. doi: 10.1016/j.dib.2025.111912. eCollection 2025 Oct.

ABSTRACT

Street vending is a prominent component of the informal economy, yet its prevalence remains poorly quantified due to the limitations of traditional survey methods, which are costly, invasive, and labor-intensive. To enable scalable, image-based assessments of this activity, we present the StreetVendor-SLI dataset, specifically designed for detecting vendors in urban environments. The dataset comprises 2794 high-resolution images (2416×1359 px), obtained from video footage recorded with a user grade camera mounted on a motorcycle. The original dataset contains 1397 images, with an average size of 5 MB per image, resulting in a total dataset size of 4.63 GB. Privacy compliance with GDPR guidelines was achieved by anonymizing pedestrian faces and vehicle license plates using an open-source YOLO object detection pipeline. Every image is annotated utilizing the YOLO format, with vendors enclosed in bounding boxes and classified into three categories: fixed-stall vendor (1774 labels), semi-fixed vendor (459 labels), and itinerant vendor (124 labels). To address class imbalance and enhance model generalization, data augmentation techniques-including geometric transformations (rotation, flipping, scaling, shearing) and spectral adjustments (brightness, contrast, hue)-were applied. The Steet-level Imagery dataset thus provides an openly available option for the detection of street vendors, offering a valuable resource for researchers studying informal economic activities and urban policies.

PMID:40791665 | PMC:PMC12337018 | DOI:10.1016/j.dib.2025.111912

Data Brief. 2025 Jul 21;62:111908. doi: 10.1016/j.dib.2025.111908. eCollection 2025 Oct.

ABSTRACT

The mango image dataset presented in this article contains clear and detailed images of the fifteen most common and popular mango (Mangifera indica) varieties in Bangladesh: Amrapali, Ashshina Classic, Ashshina Zhinuk, Banana Mango, Bari-4, Bari-11, Fazli Classic, Fazli Shurmai, Gourmoti, Harivanga, Himsagor, Katimon, Langra, Rupali, and Shada. The mango specimens were sourced from various fruit markets across six districts of Bangladesh, namely Rajshahi, Chapai Nawabganj, Satkhira, Panchagarh, Rangpur, and Dhaka, which are famous for popular mango cultivation and availability to ensure a wide geographic representation. To maintain the quality and uniformity of images across the dataset, the images were captured using a high-definition smartphone camera under a standardized and controlled environment. Overall, the full dataset contains a total of 28,515 images, where 5703 images are original (raw) and 5703 images are processed with a blend of both real and virtual backgrounds. The processed images were further augmented resulting in a total of 17,109 augmented images. This is done to enhance their utility for training machine learning and deep learning models, particularly for performing computer vision tasks such as object detection, classification, and segmentation. This augmentation includes transformations such as flipping, rotation, shearing, blurring, variation of brightness and exposure, and introduction of noise to simulate diverse real-world scenarios and improve model robustness. This dataset holds strong reuse potential across computer vision, agriculture, food processing, and biodiversity research. It supports automated mango variety identification, sorting, grading, and quality assessment in precision agriculture. It can also aid in breeding climate-resilient, high-yield mango varieties, enhancing food security and sustainable farming. Additionally, it facilitates studies on phenotypic diversity, genetic correlations, and regional trait comparisons. The dataset can help ensure traceability, authenticity, and quality assurance, improving supply chains and export potential. From a biodiversity standpoint, it contributes to documenting and conserving unique mango varieties.

PMID:40791664 | PMC:PMC12337025 | DOI:10.1016/j.dib.2025.111908

bioRxiv [Preprint]. 2025 Jul 14:2025.07.09.663937. doi: 10.1101/2025.07.09.663937.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a devastating chronic lung disorder with limited treatment options. Macropinocytosis is one of the key cellular processes involved in nutrient consumption from the extracellular environment under stress conditions. Here, we studied the role of macropinocytosis in lung fibroblast activation and experimental pulmonary fibrosis. We found that macropinocytosis is increased in human lung fibroblasts (HLFs) derived from IPF patients. The inhibition of macropinocytosis with 5-(n-ethyl-n-isopropyl)-amiloride (EIPA) significantly inhibited profibrotic responses in IPF-derived and TGF-β1-stimulated HLFs. EIPA exerted antifibrotic effects by regulating amino acid (AA) uptake, mammalian target of rapamycin complex 1 (mTORC1) activation and mesenchyme homeobox1 (MEOX1) expression in activated HLFs. Both genetic and pharmacological inhibition of macropinocytosis significantly ameliorated pulmonary fibrosis in bleomycin (Bleo)-injured mice. Using IPF-derived precision cut lung slices (PCLS), we observed robust repression of profibrotic gene expression programs in EIPA-treated PCLS across different fibroblast subpopulations. Finally, we found that imipramine (Imi), a tricyclic antidepressant approved by the Food and Drug Administration (FDA), effectively inhibited macropinocytosis and ameliorated profibrotic responses in lung fibroblasts, Bleo-injured mice and IPF-derived PCLS. Taken together, our results suggest macropinocytosis inhibition as a potential therapeutic strategy to treat pulmonary fibrosis.

PMID:40791420 | PMC:PMC12338576 | DOI:10.1101/2025.07.09.663937

bioRxiv [Preprint]. 2025 Jul 18:2025.07.16.665213. doi: 10.1101/2025.07.16.665213.

ABSTRACT

YAP/TAZ signaling is required for initiation of lung alveolar repair, yet previous studies in idiopathic pulmonary fibrosis (IPF) predicted increased YAP/TAZ signaling in alveolar epithelial cells (AECs). We investigated whether persistent YAP/TAZ AEC signaling contributes to failed epithelial repair and persistent fibrotic remodeling. In IPF lungs, we identified increased YAP + /TAZ + AECs and increased expression of YAP/TAZ transcriptional targets compared to donor control lungs. In human lung organoids, pharmacological YAP/TAZ activation resulted in phenotype shifts of AECs into aberrant transitional states. In mice with Yap/Taz activation (YT active ) resulting from deletion of Hippo-kinases Stk3/4 in alveolar-type 2 (AT2) cells, resulted in persistent fibrotic remodeling at 28- and 56-days post-bleomycin injury. Gene promoter activity associated with transitional cell markers ( Krt19, Hopx, and Runx2 ) was increased in YT active AT2 cells. Immunofluorescent staining showed a loss of AT2 associated Cebpa and increased Krt19 in YT active lineage traced AT2 cells 28 days post-injury. Inhibition of Yap/Taz using Verteporfin resulted in improved lung repair in YT active mouse lungs, including increased Cebpa and decreased Krt19 + transitional cells. These findings demonstrate sustained Yap/Taz activation drives abnormal alveolar repair and persistent fibrotic remodeling. Blocking aberrant persistent Yap/Taz activity promotes adaptive repair and has potential as a therapeutic strategy for PF.

PMID:40791334 | PMC:PMC12338630 | DOI:10.1101/2025.07.16.665213

Front Microbiol. 2025 Jul 28;16:1618327. doi: 10.3389/fmicb.2025.1618327. eCollection 2025.

ABSTRACT

Tomato spotted wilt orthotospovirus (TSWV) is a major plant pathogen causing significant economic losses in tomato production worldwide. Understanding its genetic diversity and evolutionary mechanisms is crucial for effective disease management. This study analyzed TSWV isolates from symptomatic tomato plants collected across Croatia, Montenegro and Slovenia between 2020 and 2024. High-throughput sequencing (HTS) was employed to obtain whole-genome sequences, followed by phylogenetic analyses to assess genetic variability and relationships among isolates from these three countries and other isolates of worldwide geographic origin. Phylogenetic analyses placed all studied isolates within the L1-M3-S3 genotype, commonly associated with solanaceous crops in Europe. While Croatian and Slovenian isolates exhibited high genetic similarity, Montenegrin isolates clustered in a distinct subgroup, showing closer relationships to Asian and Mediterranean accessions. Despite the severe disease symptoms observed, no substitutions in the NSm protein associated with resistance-breaking (RB) phenotypes were detected. These findings suggest that additional virome components, environmental factors or so far unknown mechanism(s) may contribute to infection and disease severity in tomato and strongly support the need of continuous surveillance of TSWV genetic diversity in order to inform breeding programs and develop sustainable management strategies to mitigate future outbreaks.

PMID:40792267 | PMC:PMC12336143 | DOI:10.3389/fmicb.2025.1618327

iScience. 2025 Jul 16;28(8):113121. doi: 10.1016/j.isci.2025.113121. eCollection 2025 Aug 15.

ABSTRACT

IRE1α and its downstream XBP1 signal is the most conserved unfolded protein response pathway that cells utilize to combat endoplasmic reticulum stress, also known to be utilized by tumor cells to adapt to harsh environment, leading to tumor progression. Several inhibitors against IRE1α have been developed, some of which show promising effect in clinical trial for cancer therapy, but none of them have been used in practice. Considering that hyper-activation of IRE1α induces cell death, we hypothesize that activation of IRE1α could be an alternative way for tumor suppression. Here, we identified divalent manganese ion as a potent activator to IRE1α, which interacts with the cytosolic part of IRE1α directly, augmenting the downstream pro-apoptotic pathway but not the pro-survival outcome. Mn2+ limits tumor growth in xenograft model in an IRE1α-dependent way. Our finding suggests pharmacological activation of IRE1α as an underestimated but promising way in cancer therapy.

PMID:40792035 | PMC:PMC12337650 | DOI:10.1016/j.isci.2025.113121

iScience. 2025 Jul 15;28(8):113112. doi: 10.1016/j.isci.2025.113112. eCollection 2025 Aug 15.

ABSTRACT

Prolactin is the main hormonal driver of mammalian lactation. To sustain milk production, basal prolactin levels must remain elevated compared to nonpregnant states. However, prolactin (23 kDa) is short-lived in circulation due to rapid renal excretion. Here, we design and test the galactopoietic effects of an engineered long-lasting prolactin in mice. The engineered variant, prolactin-extra long-acting (Prolactin-XL), is comprised of endogenously active human prolactin fused to an engineered human immunoglobulin G1 (IgG1) Fc domain. Prolactin-XL has a serum half-life of 70.9 h in mice, 2,625-fold longer than endogenously active human prolactin alone (70.9 h vs. 0.02 h). Prolactin-XL is engineered to be more susceptible to gastrointestinal proteases to reduce its uptake by nursing neonates. We demonstrate that Prolactin-XL increases lactation and restores growth of pups fed by dams with pharmacologically ablated lactation. We propose that Prolactin-XL is a potential tool for the study and pharmacologic stimulation of galactopoiesis.

PMID:40792031 | PMC:PMC12337787 | DOI:10.1016/j.isci.2025.113112

iScience. 2025 Jul 21;28(8):113165. doi: 10.1016/j.isci.2025.113165. eCollection 2025 Aug 15.

ABSTRACT

Colorectal cancer (CRC) is a prevalent digestive system malignancy accompanied by peritoneal metastasis occurring in 7% of cases. Methyltransferase-like 3 (METTL3) promoted the progression of CRC whereas its function in peritoneal metastasis was incompletely understood. Here, we found that METTL3 was upregulated in peritoneal metastasis tissues of CRC patients compared with CRC tissues. By sequencing the mRNA of above tissues, we discovered that METTL3-mediated N6-methyladenosine (m6A) modification regulated the downstream target neurexin-3 (NRXN3). NRXN3 promoted CRC peritoneal metastasis in vivo. Mechanically, we further verified the specific methylation sites of NRXN3 mRNA modified by METTL3. Functional cellular assays demonstrated METTL3-mediated upstream regulation of NRXN3. We also demonstrated that YTHDC1 is necessary for the METTL3-mediated stabilization of NRXN3 mRNA. Taken together, this study established a METTL3-YTHDC1-NRXN3 m6A modification-dependent regulation system in CRC peritoneal metastasis, suggesting potential candidates for peritoneal metastasis treatment.

PMID:40792021 | PMC:PMC12337687 | DOI:10.1016/j.isci.2025.113165

Biol Rev Camb Philos Soc. 2025 Aug 11. doi: 10.1111/brv.70064. Online ahead of print.

ABSTRACT

Theory predicts that facultatively asexual animals, which can leverage the advantages of both sexual and asexual reproduction, should outcompete obligately sexual and obligately asexual animals. Yet, paradoxically, obligate sexual reproduction predominates in many animal lineages, while the most flexible form of facultative asexuality (i.e. facultative parthenogenesis) appears to be rare. Recent theoretical work suggests that sexual conflict could help to resolve this paradox. Males that coercively fertilise females' eggs may, in the process, prevent alleles for parthenogenesis from spreading by limiting opportunities for asexual reproduction. Coercive males may also inhibit asexual reproduction by making resistance to sex disproportionately costly for females. In this review, we outline evidence of interactions with males that could impose costs on parthenogenetic females or hinder their ability to reproduce parthenogenetically in diverse animal taxa. The evidence suggests that such interactions between the sexes have the potential to mediate sexual conflict over mating and reproductive mode, both within facultative species and between closely related sexual and asexual taxa. However, the relative costs of sex and parthenogenesis are clearly context dependent, and much remains unknown. The most direct evidence for male inhibition of parthenogenesis comes from stick insects, but several other systems offer promising avenues for further investigation. Further research on the costs of mating and resistance in such systems could shed light on the reasons for the puzzling rarity of facultative parthenogenesis in nature.

PMID:40790891 | DOI:10.1111/brv.70064

Plant Genome. 2025 Sep;18(3):e70089. doi: 10.1002/tpg2.70089.

ABSTRACT

Stem rot, caused by Sclerotium rolfsii Sacc., is a devastating soil-borne disease causing up to 80% yield losses in groundnut globally. To dissect the genetic basis of resistance, we evaluated a diverse minicore germplasm panel over 3 years in stem rot sick-field conditions. Multi-locus genome-wide association study with the 58K single nucleotide polymorphisms (SNPs) Axiom_Arachis array genotyping identified 13 significant genomic regions associated with resistance across eight chromosomes with logarithm of the odds (LOD) scores ranging from 4.5 to 12.4 and R2 values between 6.9% and 58%. Within these regions, 145 candidate genes were implicated, including wall-associated receptor kinases, lucine-rich repeat and NB-ARC domain proteins, and peroxidase superfamily proteins. These genes orchestrate resistance through pathogen perception (e.g., receptor-like kinases), direct inhibition (R genes), toxin detoxification, and activation of transcription factors driving protective compound synthesis for cell recovery. If these defenses are compromised, a hypersensitive response-mediated apoptosis is triggered. Notably, resistance was exclusive to Virginia-type groundnut. The identified candidate genes showed strong correlation with RNA-seq data from stem rot-infected plants, reinforcing their role in the transcriptional defense response. Three kompetitive allele-specific PCR markers, namely, SnpAH00614 (on auxin-related gene AhSR001), SnpAH00625 (on histidine triad protein gene AhSR002), and SnpAH00626 (on E3 ubiquitin ligase gene AhSR003), were validated, confirming their significant contribution to stem rot resistance. These markers may facilitate the development of stem rot-resistant varieties through direct application in breeding programs through marker-assisted selection.

PMID:40790867 | DOI:10.1002/tpg2.70089

Food Res Int. 2025 Oct;218:116910. doi: 10.1016/j.foodres.2025.116910. Epub 2025 Jun 23.

ABSTRACT

Amylase/trypsin-inhibitors (ATIs) are known allergens and triggers of non-celiac wheat sensitivity. Until now, ATIs were only quantitated in wheat species. We developed and validated a targeted stable isotope dilution analysis LC-MS/MS method to quantitate ten barley-specific ATIs, including one monomeric and one dimeric amylase-inhibitor, four chloroform/methanol-soluble types, three subtilisin/chymotrypsin-inhibitors and one amylase/subtilisin-inhibitor. After successful validation in terms of precision, recovery and limits of detection and quantitation, the method was applied to 181 barley accessions from the Global EcoSeed panel, comprising 113 two-row and 68 six-row barleys of different genetic backgrounds. The overall ATI content was 1.1-5.2 mg/g, corresponding to 0.7-3.6 % of the total protein content with no clear distinction between two-row and six-row barleys. This study is the first to provide insights on the ATI content and composition of barley, which can be used to make low-ATI foods for special dietary needs.

PMID:40790697 | DOI:10.1016/j.foodres.2025.116910

BMC Plant Biol. 2025 Aug 11;25(1):1062. doi: 10.1186/s12870-025-07102-8.

ABSTRACT

Xylella fastidiosa, a highly pathogenic, xylem-limited, gram-negative bacterial species, represents a significant threat to many plant species, including olive, almond, grapevine, and alfalfa. Through cross-species transcriptomic analysis of Olea europaea, Prunus dulcis, Vitis vinifera, and Medicago sativa, we identified a novel core resistance network consisting of 18 conserved genes against Xylella fastidiosa, alongside 1852 divergent expression patterns. These common genes may play a crucial role in orchestrating a multi-layered plant defense response, enabling (1) structural reinforcement as well as facilitating cuticular wax biosynthesis (KCS11 and KAS1); (2) stress signaling mediated by hormonal crosstalk involving jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA) mediated by the genes AOS and CYP707A4, alongside calcium signaling through ACA12 gene; (3) antimicrobial 22 compound production (β-amyrin synthase BAS, ABC transporter PDR6); and (4) resource optimization through trehalose metabolism (AT1G23870) and amino acid transport (AAP2). The protein-protein interaction networks revealed coordinated regulation of immune hubs including BAK1, WRKY33, and WRKY40, with novel connections to subtilase proteases and ubiquitin-proteasome components. This conserved molecular framework highlights evolutionary convergence in plant defenses against xylem pathogens, providing future targets for engineering resistance through cell wall modification, stress signaling potentiation, and secondary metabolite engineering.

PMID:40790398 | DOI:10.1186/s12870-025-07102-8

Nat Commun. 2025 Aug 11;16(1):7416. doi: 10.1038/s41467-025-62415-4.

ABSTRACT

Mathematical models are indispensable for studying the architecture and behavior of intracellular signaling networks. It is common to develop models using phenomenological approximations due to the difficulty of fully observing the intermediate steps in intracellular signaling pathways. Thus, multiple models can be built to represent the same pathway. This opens up challenges for model selection and decreases certainty in predictions. Here, we investigate Bayesian multimodel inference (MMI) as an approach to increase certainty in systems biology predictions, which becomes relevant when one wants to leverage a set of potentially incomplete models. Using existing models of the extracellular-regulated kinase (ERK) pathway, we show that MMI successfully combines models and yields predictors robust to model set changes and data uncertainties. We then use MMI to identify possible mechanisms of experimentally measured subcellular location-specific ERK activity. This work highlights MMI as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.

PMID:40790297 | DOI:10.1038/s41467-025-62415-4

Front Pharmacol. 2025 Jul 28;16:1620732. doi: 10.3389/fphar.2025.1620732. eCollection 2025.

ABSTRACT

BACKGROUND: Curcumin, a key bioactive component of turmeric (Curcuma longa L. [Zingiberaceae]), has gained considerable attention for its potential to mitigate drug-induced toxicity. This review synthesizes and clarifies current findings on curcumin's ability to prevent the adverse effects of various pharmaceuticals.

METHODS: A comprehensive search using multiple databases-PubMed®, Scopus®, ScienceDirect®, and Web of Science®-was conducted for articles published up to October 2023. The current review is limited to randomized controlled trials, observational studies, and animal studies investigating the protective role of curcumin against drug-induced toxicity. The data extraction process included a variety of study characteristics, types of drugs used, curcumin dosing regimens, and reported outcomes associated with drug-induced toxicity.

RESULTS: A total of twenty-five studies were reviewed for this analysis. Curcumin may help reduce the side effects of certain medications, including sertraline, diclofenac, paclitaxel, irinotecan, and methotrexate.

DISCUSSION: Research also indicates that curcumin possesses antioxidant properties, reduces inflammation, and aids sperm production. Most importantly, sperm motility, density, and morphology significantly improved in curcumin-treated groups compared to the control groups undergoing toxic pharmaceutical treatment. The dosage of curcumin used in these studies ranges from 50 to 200 mg/kg body weight.

CONCLUSION: The available evidence suggests that curcumin may serve as a protective agent for male reproductive health against drug-induced damage, based on its diverse effects in mitigating oxidative stress and inflammation, which provide potential use in preserving reproductive health in males during pharmacological interventions. However, standardization of methodologies, along with more clinical evidence, is highly required before the practical application of findings related to treatment benefits can be made. Subsequent studies should focus on optimizing the use of this compound in combination with other pharmacological agents to enhance the protective effects of curcumin on male reproductive health.

PMID:40792206 | PMC:PMC12336216 | DOI:10.3389/fphar.2025.1620732

BMC Cancer. 2025 Aug 11;25(1):1303. doi: 10.1186/s12885-025-14733-5.

ABSTRACT

AIMS: The objective of this study was to analyze immune-related adverse events (irAEs) in a real-world data sample and examine the differences in incidence between affected organ systems, irAE severity, therapeutic agent, and gender.

METHODS: We retrospectively analyzed all consecutive patients treated with anti-cytotoxic T-lymphocyte associated protein 4 (CTLA-4) antibodies, anti-programmed death 1 (PD-1) inhibitors, and programmed death-ligand 1 (PD-L1) inhibitors between January 2020 and May 2023 in a tertiary referral center in Switzerland. IrAEs documented in the electronic health records (EHR) were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) and analyzed descriptively.

RESULTS: Among the 500 patients, 196 (39.2%) were female. Treatments included pembrolizumab (51.2%), atezolizumab (20.2%), nivolumab (14.4%), durvalumab (6.4%), ipilimumab in combination with nivolumab (4.8%), cemiplimab (1.4%), avelumab (1.2%), and ipilimumab (0.4%). N = 216 (43.2%) patients had ≥ 1 irAEs (females: 47.4%; males: 40.5%). Severe (≥ grade 3) irAEs were reported in 13.6% of patients. The following irAE incidences were found: dermatological (15.2%), gastrointestinal (13.0%), endocrine (10.8%), musculoskeletal (4.8%), pulmonary (3.8%), systemic (3.6%), neurological (2.6%), cardiac (1.4%), renal (1.4%), hematological (0.6%), and ocular (0.2%).

CONCLUSION: Nearly half of the patients experienced ≥ 1 irAEs, of which one-third severe. Females experienced more irAEs than males, above all due to a higher incidence of grade 1 irAEs. Only about half of the irAEs were reported as coded diagnosis. Further prospective studies on irAEs are warranted using structured documentation.

PMID:40790180 | DOI:10.1186/s12885-025-14733-5