bioRxiv [Preprint]. 2025 Feb 27:2025.02.21.639501. doi: 10.1101/2025.02.21.639501.

ABSTRACT

Matrix metalloproteinases (MMPs) are a family of proteases that drive degradation of extracellular matrix (ECM) across many tissues. MMP activity is antagonized by tissue inhibitors of metalloproteinases (TIMPs), resulting in a complex multivariate system with many MMP isoforms and TIMP isoforms interacting across a network of biochemical reactions - each with their own distinct kinetic rates. This system complexity makes it very difficult to identify which specific molecules are most responsible for driving ECM turnover in vivo and therefore the most promising therapeutic targets. To help elucidate the specific roles of various MMP and TIMP isoforms, we present a computational systems biology model of collagen turnover capturing all possible interactions between type I collagen, four different MMP isoforms (MMP-1, -2, -8, and -9), and three different TIMP isoforms (TIMP-1, -2, and -4). We used dye-quenched fluorescent collagen to monitor the degradation of collagen in the presence of various MMP+TIMP cocktails, and we then used these experimental data to fit hypothetical reaction system topologies in order to investigate their respective accuracies. We determined kinetic rate constants for this system and used post-myocardial infarct time courses of collagen, MMP, and TIMP levels to perform a parameter sensitivity analysis across the model reaction rates and predict which molecules and interactions are the important regulators of ECM in the infarcted heart. Notably, the model suggested that MMP degradation and inactivation terms were more important for driving collagen levels than TIMP interaction terms. In sum, this work highlights the need for systems-level analyses to distinguish the roles of various biomolecules operating with a complex system, prioritizes therapeutic targets for post-infarct cardiac remodeling, and presents a computational framework that can be applied to many other collagen-rich tissues.

PMID:40060515 | PMC:PMC11888197 | DOI:10.1101/2025.02.21.639501

3 Biotech. 2025 Apr;15(4):76. doi: 10.1007/s13205-025-04248-y. Epub 2025 Mar 6.

ABSTRACT

Heavy metal toxicity hinders plant growth and development by inducing oxidative stress, decreasing biomass, impairing photosynthesis, and potentially leading to plant death. The inherent defense mechanisms employed by plants, including metal sequestration into vacuoles, phytochelation, cell wall metal adsorption and an enhanced antioxidant system can be improved via various approaches to mitigate heavy metal toxicity. This review primarily outlines plants direct and indirect responses to HM stress and the tolerance mechanisms by which plants combat the toxic effects of metals and metalloids to understand the effective management of HMs and metalloids in the soil system. Furthermore, this review highlights measures to mitigate metal and metalloid toxicity and improve metal tolerance through various physio-biochemical, biological, and molecular approaches. This review also provides a comprehensive account of all the mitigative approaches by comparing physio-biochemical, biological and molecular approaches. Finally, we compared all the mitigative approaches used in monocotyledonous and dicotyledonous to increase their metal tolerance. Although many studies have compared monocot and dicot plants based on metal toxicity and tolerance effects, comparisons of these mitigative approaches have not been explored.

PMID:40060292 | PMC:PMC11885775 | DOI:10.1007/s13205-025-04248-y

MedComm (2020). 2025 Mar 6;6(3):e70101. doi: 10.1002/mco2.70101. eCollection 2025 Mar.

ABSTRACT

Neutrophil extracellular traps (NETs) are unique fibrous structures released by neutrophils in response to various pathogens, exhibiting both anti-inflammatory and proinflammatory effects. In autoimmune conditions, NETs serve as crucial self-antigens triggering inflammatory cascades by activating the inflammasome and complement systems, disrupting self-tolerance mechanisms and accelerating autoimmune responses. Furthermore, NETs play a pivotal role in modulating immune cell activation, affecting adaptive immune responses. This review outlines the intricate relationship between NETs and various diseases, including inflammatory arthritis, systemic autoimmune diseases, Behçet's disease, systemic lupus erythematosus, autoimmune kidney diseases, autoimmune skin conditions, systemic sclerosis, systemic vasculitis, and gouty arthritis. It highlights the potential of targeting NETs as a therapeutic strategy in autoimmune diseases. By examining the dynamic balance between NET formation and clearance in autoimmune conditions, this review offers critical insights and a theoretical foundation for future research on NET-related mechanisms. Advances in systems biology, flow cytometry, and single-cell multiomics sequencing have provided valuable tools for exploring the molecular mechanisms of neutrophils and NETs. These advancements have renewed focus on the role of neutrophils and NETs in autoimmune diseases, offering promising avenues for further investigation into their clinical implications.

PMID:40060194 | PMC:PMC11885892 | DOI:10.1002/mco2.70101

aBIOTECH. 2025 Jan 23;6(1):116-132. doi: 10.1007/s42994-024-00194-0. eCollection 2025 Mar.

ABSTRACT

Plant metabolites are crucial for the growth, development, environmental adaptation, and nutritional quality of plants. Plant metabolomics, a key branch of systems biology, involves the comprehensive analysis and interpretation of the composition, variation, and functions of these metabolites. Advances in technology have transformed plant metabolomics into a sophisticated process involving sample collection, metabolite extraction, high-throughput analysis, data processing, and multidimensional statistical analysis. In today's era of big data, the field is witnessing an explosion in data acquisition, offering insight into the complexity and dynamics of plant metabolism. Moreover, multiple omics strategies can be integrated to reveal interactions and regulatory networks across different molecular levels, deepening our understanding of plant biological processes. In this review, we highlight recent advances and challenges in plant metabolomics, emphasizing the roles for this technique in improving crop varieties, enhancing nutritional value, and increasing stress resistance. We also explore the scientific foundations of plant metabolomics and its applications in medicine, and ecological conservation.

PMID:40060186 | PMC:PMC11889285 | DOI:10.1007/s42994-024-00194-0

Front Microbiol. 2025 Feb 21;15:1462941. doi: 10.3389/fmicb.2024.1462941. eCollection 2024.

ABSTRACT

Permafrost thaw increases the bioavailability of ancient organic matter, facilitating microbial metabolism of volatile organic compounds (VOCs), carbon dioxide, and methane (CH4). The formation of thermokarst (thaw) lakes in icy, organic-rich Yedoma permafrost leads to high CH4 emissions, and subsurface microbes that have the potential to be biogeochemical drivers of organic carbon turnover in these systems. However, to better characterize and quantify rates of permafrost changes, methods that further clarify the relationship between subsurface biogeochemical processes and microbial dynamics are needed. In this study, we investigated four sites (two well-drained thermokarst mounds, a drained thermokarst lake, and the terrestrial margin of a recently formed thermokarst lake) to determine whether biogenic VOCs (1) can be effectively collected during winter, and (2) whether winter sampling provides more biologically significant VOCs correlated with subsurface microbial metabolic potential. During the cold season (March 2023), we drilled boreholes at the four sites and collected cores to simultaneously characterize microbial populations and captured VOCs. VOC analysis of these sites revealed "fingerprints" that were distinct and unique to each site. Total VOCs from the boreholes included > 400 unique VOC features, including > 40 potentially biogenic VOCs related to microbial metabolism. Subsurface microbial community composition was distinct across sites; for example, methanogenic archaea were far more abundant at the thermokarst site characterized by high annual CH4 emissions. The results obtained from this method strongly suggest that ∼10% of VOCs are potentially biogenic, and that biogenic VOCs can be mapped to subsurface microbial metabolisms. By better revealing the relationship between subsurface biogeochemical processes and microbial dynamics, this work advances our ability to monitor and predict subsurface carbon turnover in Arctic soils.

PMID:40059907 | PMC:PMC11885255 | DOI:10.3389/fmicb.2024.1462941

MedComm (2020). 2025 Mar 6;6(3):e70124. doi: 10.1002/mco2.70124. eCollection 2025 Mar.

ABSTRACT

This multicenter study aimed to investigate the efficacy and safety of PD-1/PD-L1 inhibitors plus chemotherapy (ICI-Chemo group) versus chemotherapy alone (Chemo group) for patients with cancer of unknown primary (CUP) in the first-line setting. We included patients with unfavorable CUP across four medical centers in China. Between January 2014 and December 2023, 117 patients were enrolled: 46 patients in the ICI-Chemo group and 71 patients in the Chemo group. After a median follow-up of 28.1 months, the ICI-Chemo group exhibited a significant improvement over the Chemo group in median PFS (9.10 months vs. 6.37 months; hazard ratio [HR] 0.46; 95% CI: 0.30-0.71; p < 0.001) and OS (35.67 months vs. 10.2 months; HR 0.37; 95% CI: 0.22-0.64; p < 0.001). Similarly, among patients who received TP (taxane plus platinum)-based chemotherapies, OS and PFS benefits were observed in the ICI-Chemo group. The objective response rate was higher in the ICI-Chemo group than in the Chemo group (54.35% vs. 22.53%, p < 0.001). Grade 3 or higher drug-related adverse events occurred in 11 patients (23.91%) in the ICI-Chemo group and 28 patients (39.44%) in the Chemo group. Thus, PD-1/PD-L1 inhibitors plus chemotherapy could be the preferred first-line treatment for patients with unfavorable CUP, providing improved efficacy and manageable toxicity.

PMID:40060196 | PMC:PMC11885889 | DOI:10.1002/mco2.70124

Future Microbiol. 2025 Mar 9:1-13. doi: 10.1080/17460913.2025.2475639. Online ahead of print.

ABSTRACT

AIMS: To evaluate the antibacterial and antibiofilm activities of sodium ibuprofenate (NaI) and its hypertonic variant (NaIHS) against multidrug-resistant Gram-negative bacteria (MDR-GNB) and explore their potential to inhibit β-lactamase enzymes.

METHODS: Antibacterial activity was assessed using minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and time-kill assays. Antibiofilm activity was evaluated by measuring bacterial viability and biomass reduction in preformed biofilms. Scanning electron microscopy (SEM) was used to observe membrane effects. Molecular docking and molecular dynamics simulations were conducted to analyze the binding affinity of ibuprofen to the active sites of β-lactamases (CTX-M-15, KPC-2, OXA-23).

RESULTS: NaI exhibited bactericidal activity at concentrations of 25-75 mm, with Acinetobacter baumannii being the most susceptible. NaCl (≥0.5 M) enhanced bactericidal efficacy and lowered MBCs. Time-kill assays indicated rapid bacterial eradication within 2 hours, with NaIHS achieving similar results at lower concentrations. SEM confirmed membrane disruption. Both formulations reduced bacterial viability in biofilms, with NaIHS showing greater efficiency. In silico studies suggest ibuprofen may inhibit β-lactamases, with enhanced interactions in saline environments.

CONCLUSION: Sodium ibuprofenate, particularly in its hypertonic form, demonstrates strong antibacterial, antibiofilm, and potential β-lactamase inhibitory activity, making it a promising candidate for treating MDR-GNB infections.

PMID:40059403 | DOI:10.1080/17460913.2025.2475639

Bioconjug Chem. 2025 Mar 10. doi: 10.1021/acs.bioconjchem.4c00561. Online ahead of print.

ABSTRACT

Cu(II) coordination complexes are emerging as promising anticancer agents due to their ability to induce oxidative stress through reactive oxygen species (ROS) generation. In this study, we synthesized and characterized two novel Cu(II) metallopeptide systems, 1/Cu(II) and 2/Cu(II), derived from the oligocationic bipyridyl cyclopeptides 1 and 2, and designed to enhance the transport of Cu(II) into cells and increase ROS levels. Spectroscopic and electrochemical analyses confirmed the formation of stable metallopeptide species in aqueous media. Inductively coupled plasma mass spectrometry (ICP-MS) studies demonstrated that both metallopeptides significantly increase intracellular Cu(II) accumulation in NCI/ADR-RES cancer cells, highlighting their role as efficient Cu(II) transporters. Additionally, ROS generation assays revealed that 1/Cu(II) induces a substantial increase in intracellular ROS levels, supporting the hypothesis of oxidative stress-induced cytotoxicity. Cell-viability assays further confirmed that both 1/Cu(II) and 2/Cu(II) exhibit strong anticancer activity in a number of cancer cell lines, with IC50 values significantly lower than those of their free cyclopeptide counterparts or Cu(II) alone, showing an order of activity higher than that of cisplatin. Finally, molecular modeling studies provided further insights into the structural stability and coordination environment of Cu(II) within the metallopeptide complexes. These findings suggest that these Cu(II) cyclometallopeptide systems hold potential as novel metal-based therapeutic agents, leveraging Cu(II) transport and ROS increase as key strategies for cancer treatment.

PMID:40059798 | DOI:10.1021/acs.bioconjchem.4c00561

J Child Adolesc Psychopharmacol. 2025 Mar 10. doi: 10.1089/cap.2024.0130. Online ahead of print.

ABSTRACT

Introduction: Prior studies have demonstrated that, in both adults and youth, bipolar disorder (BD) is a polygenic illness. However, no studies have examined polygenic risk scores (PRSs) in relation to the longitudinal course of mood symptoms in youth with BD. Methods: This study included 246 youth of European ancestry with BD (7-20 years old at intake) from the Course and Outcome of Bipolar Youth study and Centre for Youth Bipolar Disorder. Mood symptom severity was assessed at intake and, for 168 participants, prospectively for a median of 8.7 years. PRSs for BD, schizophrenia (SCZ), major depressive disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were constructed using genome-wide summary statistics from independent adult cohorts. Results: Higher BD-PRS was significantly associated with lower most severe lifetime depression score at intake (β = -0.14, p = 0.03). Higher SCZ-PRS and MDD-PRS were associated with significantly less time spent in euthymia (SCZ-PRS: β = -0.21, p = 0.02; MDD-PRS: β = -0.22, p = 0.01) and more time with any subsyndromal mood symptoms (i.e., any mania, mixed, or depression symptoms; SCZ-PRS: β = 0.15, p = 0.04; MDD-PRS: β = 0.17, p = 0.01) during follow-up. PRSs for BD and ADHD were not significantly associated with any longitudinal mood variable. Conclusions: This exploratory analysis was the first to examine psychiatric PRSs in relation to the prospective course of mood symptoms among youth with BD. Results from the current study can serve to guide future youth BD studies with larger sample sizes on this topic.

PMID:40059772 | DOI:10.1089/cap.2024.0130

Small Methods. 2025 Mar 9:e2401860. doi: 10.1002/smtd.202401860. Online ahead of print.

ABSTRACT

Recent advances in the development of tumor-targeting nanoparticles (NPs) significantly enhance cancer therapies by effectively increasing the therapeutic window of drugs, improving the response of tumor cells, and augmenting anti-tumor immunity. However, the understanding of the tumor-targeting process remains elusive because of the complex in vivo behavior of nanoparticles. Various factors are reported to alter this process; however, systematic studies on tumor-targeting mechanisms remain limited. In this review, an overview of current strategies for improving tumor targeting and their applications in cancer chemotherapy and immunotherapy, with a focus on the advancement of the understanding of various in vivo barriers for effective tumor targeting and penetration is provided.

PMID:40059474 | DOI:10.1002/smtd.202401860

ACS Nano. 2025 Mar 10. doi: 10.1021/acsnano.5c01436. Online ahead of print.

ABSTRACT

Current high-sensitivity immunoassay protocols often involve complex signal generation designs or rely on sophisticated signal-loading and readout devices, making it challenging to strike a balance between sensitivity and ease of use. In this study, we propose a homogeneous-based intelligent analysis strategy called Mata, which uses weight analysis to quantify basic immune signals through signal subunits. We perform nanomagnetic labeling of target capture events on micrometer-scale polystyrene subunits, enabling magnetically regulated kinetic signal expression. Signal subunits are classified through the multi-level signal classifier in synergy with the developed signal weight analysis and deep learning recognition models. Subsequently, the basic immune signals are quantified to achieve ultra-high sensitivity. Mata achieves a detection of 0.61 pg/mL in 20 min for interleukin-6 detection, demonstrating sensitivity comparable to conventional digital immunoassays and over 22-fold that of chemiluminescence immunoassay and reducing detection time by more than 70%. The entire process relies on a homogeneous reaction and can be performed using standard bright-field optical imaging. This intelligent analysis strategy balances high sensitivity and convenient operation and has few hardware requirements, presenting a promising high-sensitivity analysis solution with wide accessibility.

PMID:40059671 | DOI:10.1021/acsnano.5c01436

Adv Mater. 2025 Mar 10:e2418147. doi: 10.1002/adma.202418147. Online ahead of print.

ABSTRACT

Testing diverse biomolecules and observing their dynamic interaction in complex biological systems in a label-free manner is critically important for terahertz (THz) absorption spectroscopy. However, traditionally employed micro/nanophotonic techniques suffer from a narrow operating resonance and strong absorption band interference from polar solutions preventing seriously reliable, on-demand biosensor integration. Here, a multifunctional THz plasmonic biosensing platform by leveraging multiple interfering resonances from quasi-bound states in the continuum designed to noninvasively and in situ track the temporal evolution of molecules in multiple analyte systems, is proposed. In contrast to conventional microphotonic sensors, this platform demonstrates substantially broadband performance and reduced footprints, allowing for simultaneous detection of diverse molecular vibrant at multiple spectral points through robust near-field interactions. Furthermore, this sensor enables real-time analysis of amino acid absorption as water evaporates despite its strong overlapping absorption bands in the THz range. By utilizing the real-time format of the reflectance method to acquire a comprehensive spectro-temporal data collection, this approach supports developing a deep neural network to discriminate and predict the composition and proportion of multiple mixtures, obviating the need for frequency scanning or microfluidic devices. This approach offers innovative viewpoints for exploring biological processes and provides valuable tools for biological analysis.

PMID:40059582 | DOI:10.1002/adma.202418147

Cancer Med. 2025 Mar;14(5):e70728. doi: 10.1002/cam4.70728.

ABSTRACT

INTRODUCTION: Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology.

METHODS: We utilized PubMed to search for peer-reviewed research articles published between 2016 and 2021 with the query type "("deep learning" or "machine learning" or "neural network" or "artificial intelligence") and ("neoplas$" or "cancer$" or "tumor$" or "tumour$")." We included clinical trials and original research studies and excluded reviews and meta-analyses. Oncology-related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies.

RESULTS: Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%-93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. "White" vs. "non-White" or "White" vs. "Black").

CONCLUSIONS: We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non-White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology.

PMID:40059400 | DOI:10.1002/cam4.70728

Med Biol Eng Comput. 2025 Mar 10. doi: 10.1007/s11517-025-03340-y. Online ahead of print.

ABSTRACT

Noninvasive brain-computer interfaces (BCIs) have rapidly developed over the past decade. This new technology utilizes magneto-electrical recording or hemodynamic imaging approaches to acquire neurophysiological signals noninvasively, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These noninvasive signals have different temporal resolutions ranging from milliseconds to seconds and various spatial resolutions ranging from centimeters to millimeters. Thanks to these neuroimaging technologies, various BCI modalities like steady-state visual evoked potential (SSVEP), P300, and motor imagery (MI) could be proposed to rehabilitate or assist patients' lost function of mobility or communication. This review focuses on the recent development of paradigms, methods, and applications of noninvasive BCI for motor or communication assistance and rehabilitation. The selection of papers follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), obtaining 223 research articles since 2016. We have observed that EEG-based BCI has gained more research focus due to its low cost and portability, as well as more translational studies in rehabilitation, robotic device control, etc. In the past decade, decoding approaches such as deep learning and source imaging have flourished in BCI. Still, there are many challenges to be solved to date, such as designing more convenient electrodes, improving the decoding accuracy and efficiency, designing more applicable systems for target patients, etc., before this new technology matures enough to benefit clinical users.

PMID:40059266 | DOI:10.1007/s11517-025-03340-y

Abdom Radiol (NY). 2025 Mar 10. doi: 10.1007/s00261-025-04853-8. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop an automated deep learning (DL) methodology for detecting small hepatocellular carcinoma (sHCC) in cirrhotic livers, leveraging Gd-EOB-DTPA-enhanced MRI.

METHODS: The present retrospective study included a total of 120 patients with cirrhosis, comprising 78 patients with sHCC and 42 patients with non-HCC cirrhosis, who were selected through stratified sampling. The dataset was divided into training and testing sets (8:2 ratio). The nnU-Net exhibits enhanced capabilities in segmenting small objects. The segmentation performance was assessed using the Dice coefficient. The ability to distinguish between sHCC and non-HCC lesions was evaluated through ROC curves, AUC scores and P values. The case-level detection performance for sHCC was evaluated through several metrics: accuracy, sensitivity, and specificity.

RESULTS: The AUCs for distinguishing sHCC patients from non-HCC patients at the lesion level were 0.967 and 0.864 for the training and test cohorts, respectively, both of which were statistically significant at P < 0.001. At the case level, distinguishing between patients with sHCC and patients with cirrhosis resulted in accuracies of 92.5% (95% CI, 85.1-96.9%) and 81.5% (95% CI, 61.9-93.7%), sensitivities of 95.1% (95% CI, 86.3-99.0%) and 88.2% (95% CI, 63.6-98.5%), and specificities of 87.5% (95% CI, 71.0-96.5%) and 70% (95% CI, 34.8-93.3%) for the training and test sets, respectively.

CONCLUSION: The DL methodology demonstrated its efficacy in detecting sHCC within a cohort of patients with cirrhosis.

PMID:40059243 | DOI:10.1007/s00261-025-04853-8

Sci Rep. 2025 Mar 10;15(1):8163. doi: 10.1038/s41598-025-88967-5.

ABSTRACT

Deep learning-based medical image segmentation methods are generally divided into convolutional neural networks (CNNs) and Transformer-based models. Traditional CNNs are limited by their receptive field, making it challenging to capture long-range dependencies. While Transformers excel at modeling global information, their high computational complexity restricts their practical application in clinical scenarios. To address these limitations, this study introduces VMAXL-UNet, a novel segmentation network that integrates Structured State Space Models (SSM) and lightweight LSTMs (xLSTM). The network incorporates Visual State Space (VSS) and ViL modules in the encoder to efficiently fuse local boundary details with global semantic context. The VSS module leverages SSM to capture long-range dependencies and extract critical features from distant regions. Meanwhile, the ViL module employs a gating mechanism to enhance the integration of local and global features, thereby improving segmentation accuracy and robustness. Experiments on datasets such as ISIC17, ISIC18, CVC-ClinicDB, and Kvasir demonstrate that VMAXL-UNet significantly outperforms traditional CNNs and Transformer-based models in capturing lesion boundaries and their distant correlations. These results highlight the model's superior performance and provide a promising approach for efficient segmentation in complex medical imaging scenarios.

PMID:40059111 | DOI:10.1038/s41598-025-88967-5

Lung. 2025 Mar 9;203(1):40. doi: 10.1007/s00408-025-00797-4.

ABSTRACT

PURPOSE: We used data from the IPF-PRO Registry of patients with idiopathic pulmonary fibrosis (IPF) to identify characteristics that predicted survival for a further > 5 years.

METHODS: Participants had IPF that was diagnosed or confirmed at the enrolling center in the previous 6 months. Patients were followed prospectively. A Classification And Regression Tree (CART) was used to identify predictors of survival > 5 versus ≤ 5 years following enrollment. The following variables, assessed at enrollment, were considered: age; body mass index (BMI); former smoker; current smoker; time from first imaging evidence, symptoms, or diagnosis of IPF to enrollment; forced vital capacity (FVC) % predicted; diffusing capacity of the lungs for carbon monoxide (DLco) % predicted; antifibrotic drug use; supplemental oxygen use; history of cardiac disease; pulmonary hypertension; COPD/emphysema; and rural location.

RESULTS: The analysis cohort comprised 819 patients, of whom 278 (33.9%) survived > 5 years. DLco % predicted, supplemental oxygen use and FVC % predicted were the most important variables for predicting survival > 5 versus ≤ 5 years after enrollment. The importance of these variables (scaled such that the most important had an importance of 100%) was 100%, 78.2% and 74.2%, respectively. The optimism-corrected area under the curve (AUC) of the CART was 0.72, with an accuracy of 0.72.

CONCLUSION: Among patients enrolled in the IPF-PRO Registry, a decision tree that included DLco % predicted, oxygen use and FVC % predicted facilitated the prediction of survival > 5 years. Understanding predictors of longer-term survival may facilitate conversations with patients about their prognosis and treatment.

PMID:40059108 | DOI:10.1007/s00408-025-00797-4

J Exp Bot. 2025 Mar 10:eraf100. doi: 10.1093/jxb/eraf100. Online ahead of print.

ABSTRACT

Hydrogen sulfide (H2S) is increasingly recognized as a crucial signaling molecule in plants, playing key roles in regulating physiological processes and enhancing stress tolerance. This review provides an updated summary of H2S signaling in plant stress responses, discussing its uptake from external environmental sources, its endogenous biosynthesis, and its broader functions in stress adaptation. We summarize the impact of H2S on plants under various stress conditions and review the mechanisms through which it mediates signaling functions, with a particular focus on H2S-mediated protein persulfidation. Additionally, we provide an overview of the current understanding of protein persulfidation in regulating physiological processes and stress responses in plants, offering both a general discussion of its effects under different stress conditions and specific examples to highlight its significance. Finally, we review recent proteomic studies on protein persulfidation in plants, comparing the identified persulfidated proteins across studies and highlighting shared biological processes and pathways. This review aims to consolidate the current understanding of H2S signaling and its roles mediated by protein persulfidation in plants, while also offering insights to inspire future research in this rapidly evolving field.

PMID:40059712 | DOI:10.1093/jxb/eraf100

Genetics. 2025 Mar 10:iyaf025. doi: 10.1093/genetics/iyaf025. Online ahead of print.

ABSTRACT

Tissue-regulated alternative exons are dictated by the interplay between cis-elements and trans-regulatory factors such as RNA binding proteins. Despite extensive research on splicing regulation, the full repertoire of these cis and trans features and their evolutionary dynamics across species are yet to be fully characterized. Members of the CUG-binding protein and ETR-like family (CELF) of RNA binding proteins are known to play a key role in the regulation of tissue-biased splicing patterns, and when mutated, these proteins have been implicated in a number of neurological and muscular disorders. In this study, we sought to characterize specific mechanisms that drive tissue-specific splicing in vivo of a model switch-like exon regulated by the neuronal-enriched CELF ortholog in C. elegans, UNC-75. Using sequence alignments, we identified deeply conserved intronic UNC-75 binding motifs overlapping the 5' splice site and upstream of the 3' splice site, flanking a strongly neural-repressed alternative exon in the Zonula Occludens gene zoo-1. We confirmed that loss of UNC-75 or mutations in either of these cis-elements lead to substantial de-repression of the alternative exon in neurons. Moreover, mis-expression of UNC-75 in muscle cells is sufficient to induce the neuron-like robust skipping of this alternative exon. Lastly, we demonstrate that overlapping an UNC-75 motif within a heterologous 5' splice site leads to increased skipping of the adjacent alternative exon in an unrelated splicing event. Together, we have demonstrated that a specific configuration and combination of cis elements bound by this important family of RNA binding proteins can achieve robust splicing outcomes in vivo.

PMID:40059624 | DOI:10.1093/genetics/iyaf025

Small Methods. 2025 Mar 9:e2500136. doi: 10.1002/smtd.202500136. Online ahead of print.

ABSTRACT

Mechanical strain substantially influences tissue shape and function in various contexts from embryonic development to disease progression. Disruptions in these processes can result in congenital abnormalities and short-circuit mechanotransduction pathways. Manipulating strain in live tissues is crucial for understanding its impact on cellular and subcellular activities, unraveling the interplay between mechanics and cells. Existing tools, such as optogenetic modulation of strain, are limited to small strains over limited distances and durations. Here, a high-strain stretcher system, the TissueTractor, is introduced to enable simultaneous high-resolution spatiotemporal imaging of live cells and tissues under strain applications varying from 0% to over 100%. We use the system with organotypic explants from Xenopus laevis embryos, where applied tension reveals cellular strain heterogeneity and remodeling of intracellular keratin filaments. To highlight the device's adaptability, the TissueTractor is also used to study two other mechanically sensitive cell types with distinct physiological roles: human umbilical vein endothelial cells and mouse neonatal cardiomyocytes, revealing cell morphological changes under significant strain. The results underscore the potential of the TissueTractor for investigating mechanical cues that regulate tissue dynamics and morphogenesis.

PMID:40059484 | DOI:10.1002/smtd.202500136