Respir Res. 2025 May 16;26(1):188. doi: 10.1186/s12931-025-03264-z.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a devastating age-related disease with unknown causes and limited effective treatment. Dysregulation of Alveolar Type 2 (AT2) cells facilitates the initiation of IPF. While differentiation of AT2 into AT1 is necessary for restoring alveolar epithelium. Delta-like non-canonical Notch ligand 1 (DLK1) is a paternally imprinted gene that controls stem cell differentiation. However, the role of DLK1 on AT2 during lung fibrosis remains unclear.

METHODS: Lung specimens from 11 patients with IPF or contemporaneous non-IPF controls were collected to determine DLK1 expression. The murine model of bleomycin (BLM) -induced pulmonary fibrosis and cell models of transforming growth factor-beta (TGF-β)-treated A549, MRC5 or primary lung fibroblasts (PLFs) were established. Epithelial DLK1 knockdown mice were constructed by an alveolar epithelial -specific adeno-associated virus (AAV) 6 vector system. Besides, primary AT2 cells were isolated from SPC-EGFP mice and cultured in 2D and 3D organoids.

RESULTS: In the present study, we found that DLK1, predominantly expressed in AT2 cells, was upregulated in both IPF lungs and the murine fibrotic lung induced by BLM. AAV-mediated epithelial-specific knockdown of DLK1 promoted the proliferation and differentiation of AT2 into AT1 and alleviated the established lung fibrosis in murine BLM-induced models. In addition, recombinant DLK1 inhibited the renewal of AT2 and aggravated TGF-β-induced fibrosis in vitro, which can be rescued by si-DLK1 intervention. Mechanically, conditional knockdown of DLK1 upregulated TTF-1, a transcriptional factor that controls AT2 differentiation via CLDN6.

CONCLUSION: DLK1 inhibition regulates AT2 differentiation and contributes to the mitigation of established fibrosis via TTF-1/CLDN6 pathway, which suggests that DLK1 may be a therapeutic target for IPF.

PMID:40380180 | DOI:10.1186/s12931-025-03264-z

BMC Psychiatry. 2025 May 16;25(1):498. doi: 10.1186/s12888-025-06951-9.

ABSTRACT

BACKGROUND: Observational studies reported altered levels of plasma metabolites in attention-deficit/hyperactivity disorder (ADHD). We aim to explore the causal link between plasma metabolites and ADHD.

METHODS: We utilized Mendelian randomization (MR) analysis to assess the causal relationship between plasma metabolites and ADHD and the Genome-wide association study (GWAS) summary datasets were sourced from public databases. GWAS summary datasets were used in the study, including ADHD (n = 292,548) and 871 plasma metabolites (n = 8,299). Moreover, we used DrugBank and ChEMBL to evaluate whether the identified metabolites are potential therapeutic targets, and in addition, Bayesian colocalization analyses were conducted to assess the shared genetic signals between these metabolites and ADHD.

RESULTS: Our MR analysis identified 20 plasma metabolites that conferred protective effects against the risk of ADHD, including dimethylglycine, 3-methoxytyramine sulfate, and adenosine 3',5'-cyclic monophosphate (OR: 0.97-0.98). Additionally, 22 metabolites were associated with an increased risk of ADHD, including N-acetylneuraminate and 3-indoleglyoxylic acid (OR:1.01-1.03). Druggability evaluation showed that 12 of the ADHD-related metabolites have been targeted by pharmacological interventions. For example, doconexent has been used to increase the levels of docosahexaenoic acid. Our reverse MR analysis showed that genetic liability to ADHD may affect the abundance of 91 metabolites. Notably, several plasma metabolites had bidirectional causal associations with ADHD, including docosahexaenoate (DHA; 22:6n3), docosatrienoate (22:3n3), N1-methyladenosine, S-adenosylhomocysteine, and 4-allylcatechol sulfate.

CONCLUSIONS: Our study supported a causal role of plasma metabolites in the susceptibility to ADHD, and the identified metabolites may provide a new avenue for the prevention and treatment of ADHD.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40380147 | DOI:10.1186/s12888-025-06951-9

Geroscience. 2025 May 16. doi: 10.1007/s11357-025-01698-3. Online ahead of print.

ABSTRACT

Functional ability and intrinsic capacity (IC) have been proposed as determinants of healthy aging, but the extent to which these indicators are affected by biological aging remains unknown. We explored the association of biological age acceleration (BAA) with functional ability and IC in older adults with physical and cognitive impairments. This cross-sectional study used data from 163 individuals (84.0 ± 5.2 years [range 72-99], 61.8% women) of the COGFRAIL cohort. Functional ability on basic (BADL-Katz Index) and instrumental activities of daily living (IADL-Lawton Index) was assessed. IC was measured as a composite score (0-100, higher is better) including the locomotion, cognition, psychology, sensory, and vitality domains. BAA was assessed by Horvath's, Hannum's, PhenoAge, and GrimAge epigenetic clocks. In the fully adjusted model, higher BAAPhenoAGe was associated to lower functional ability in BADLs (β = - 0.021, 95% confidence interval = - 0.038 to - 0.003, p = 0.022), with no significant results observed for the remaining clocks. No significant association was found between BAA and IC, but some associations were found with specific IC domains. Particularly, BAAGrimAge was associated with lower locomotion scores (β = - 1.179, 2.286 to - 0.072, p = 0.037), while BAAPhenoAge tended to be associated with lower scores in vitality (β = - 0.257, - 0.539 to 0.025, p = 0.073). Higher BAAPhenoage was associated with lower functional ability in very old adults with frailty and cognitive impairment. Although no biological clock was associated with a composite IC score, some associations were found between second-generation epigenetic clocks and specific IC domains.

PMID:40380021 | DOI:10.1007/s11357-025-01698-3

Nat Struct Mol Biol. 2025 May 16. doi: 10.1038/s41594-025-01559-9. Online ahead of print.

ABSTRACT

DEK is a highly conserved chromatin-associated oncoprotein that has important roles in regulating chromatin dynamics and stem cell fate. Dysregulation of DEK is associated with stem cell dysfunction and cancers, including acute myeloid leukemia. Despite its importance in chromatin regulation, the structural mechanisms underlying DEK's interaction with chromatin and its influence on gene regulation remain poorly understood. Here we combined cryogenic electron microscopy (cryo-EM), biochemical and cellular approaches to investigate the molecular mechanisms and functional importance of DEK's interaction with chromatin. Our cryo-EM structures reveal the structural basis of the DEK-nucleosome interaction. Biochemical and cellular results demonstrate that this interaction is crucial for DEK deposition onto chromatin. Furthermore, our results reveal that DEK safeguards mouse embryonic stem cells from acquiring primitive endoderm fates by modulating the repressive histone mark H3K27me3. Together, our study provides crucial molecular insights into the structure and function of DEK, establishing a framework for understanding its roles in chromatin biology and cell fate determination.

PMID:40379883 | DOI:10.1038/s41594-025-01559-9

Sci Data. 2025 May 16;12(1):800. doi: 10.1038/s41597-025-05082-w.

ABSTRACT

In oilseed crops, e.g. oilseed rape (OSR; Brassica napus), a key developmental process is seed maturation, during which the embryo transitions from the early, globular state to the mature state. Seed development involves cell division, differentiation, and oil accumulation in specific tissue types (embryo, endosperm, and seed coat). These developmental processes impact seed quality and oil yield. High quality RNA from Brassica spp. seed tissues, from heart to mature developmental stages, was obtained using previously reported methods for five Brassica genotypes comprising winter, semi-winter and spring OSR varieties, a B. napus heritage kale and a rapid-cycling double-haploid Brassica oleracea line. RNA-seq was performed on 240 sets of samples. The resulting dataset contains detailed spatio-temporal expression profiles during seed development. In addition to the repository data, we provide easy access to this through the Seed Oilseed Rape Developmental Expression Resource (SeedORDER), which enables users to search for genes of interest and visualise expression patterns. Knowledge of where and when genes are expressed during seed development will inform future breeding efforts.

PMID:40379636 | DOI:10.1038/s41597-025-05082-w

Cell Genom. 2025 May 13:100878. doi: 10.1016/j.xgen.2025.100878. Online ahead of print.

ABSTRACT

Emerging evidence suggests that MYC interacts with RNAs. Here, we performed an integrative characterization of MYC as an RNA-binding protein in six cell lines. We found that MYC binds to a myriad of RNAs with high affinity for guanosine-rich RNAs. Global and specific depletion of RNAs reduces MYC chromatin occupancy. Mechanistically, two highly conserved sequences, amino acids 355-357 KRR and 364-367 RQRR, within the basic region of MYC are necessary for its RNA binding. Notably, alanine substitution of KRR abolishes MYC's RNA-binding ability both in vitro and in vivo, without affecting its ability to bind E-box DNA as part of the MYC:MAX dimer in vitro. The loss of RNA-binding function decreases MYC chromatin binding in vivo and attenuates its ability to promote gene expression, cell-cycle progression, and proliferation. Our study lays a foundation for future investigation into the role of RNAs in MYC-mediated transcriptional activation and oncogenic functions.

PMID:40378850 | DOI:10.1016/j.xgen.2025.100878

Cell Syst. 2025 May 12:101294. doi: 10.1016/j.cels.2025.101294. Online ahead of print.

ABSTRACT

Effective exploration and analysis tools are vital for the extraction of insights from single-cell data. However, current techniques for modeling single-cell studies performed across experimental conditions (e.g., samples) require restrictive assumptions or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that reduction and insight in single-cell exploration (RISE), an adaptation of the tensor decomposition method PARAFAC2, enables the dimensionality reduction and analysis of single-cell data across conditions. We demonstrate the benefits of RISE across distinct examples of single-cell RNA-sequencing experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus patient samples. RISE enables associations of gene variation patterns with patients or perturbations while connecting each coordinated change to single cells without requiring cell-type annotations. The theoretical grounding of RISE suggests a unified framework for many single-cell data modeling tasks while providing an intuitive dimensionality reduction approach for multi-sample single-cell studies across biological contexts. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40378843 | DOI:10.1016/j.cels.2025.101294

Cancer Cell. 2025 May 12:S1535-6108(25)00172-2. doi: 10.1016/j.ccell.2025.04.013. Online ahead of print.

ABSTRACT

MYC-driven group-3 medulloblastomas (MBs) are malignant pediatric brain cancers without cures. To define actionable metabolic dependencies, we identify upregulation of dihydrolipoyl transacetylase (DLAT), the E2-subunit of pyruvate dehydrogenase complex (PDC) in a subset of group-3 MB with poor prognosis. DLAT is induced by c-MYC and targeting DLAT lowers TCA cycle metabolism and glutathione synthesis. We also note upregulation of isocitrate dehydrogenase 1 (IDH1) gene expression in group-3 MB patient tumors and suppression of IDH1 epigenetically reduces c-MYC and downstream DLAT levels in multiple c-MYC amplified cancers. DLAT is a central regulator of cuproptosis (copper-dependent cell death) induced by the copper ionophore elesclomol. DLAT expression in group-3 MB cells correlates with increased sensitivity to cuproptosis. Elesclomol is brain-penetrant and suppresses tumor growth in vivo in multiple group-3 MB animal models. Our data uncover an IDH1/c-MYC dependent vulnerability that regulates DLAT levels and can be targeted to kill group-3 MB by cuproptosis.

PMID:40378837 | DOI:10.1016/j.ccell.2025.04.013

Chemosphere. 2025 May 15;381:144430. doi: 10.1016/j.chemosphere.2025.144430. Online ahead of print.

ABSTRACT

The presence of emerging pollutants in aquatic ecosystems due to human activities poses substantial concerns. While many studies explore detection and removal techniques for these compounds, conventional treatment methods often fail to address emerging pollutants. Moreover, there is a lack of legislation defining safe thresholds for these substances in water. Consequently, caffeine and N,N-diethyl-meta-toluamide (DEET) persist in surface waters, including treated sources, with limited understanding of their genomic effects on human cells. This study aimed to assess the cytotoxic and genotoxic effects of caffeine and DEET, individually and in combination, at concentrations detected in drinking water, using HepG2 cells. Additionally, through systems biology, we sought to understand the underlying molecular mechanisms of both substances. Cytotoxicity was evaluated using MTT and Trypan Blue assays, while genotoxicity was assessed using the comet assay. The chemoproteomic interaction network was constructed using STITCH and STRING databases, with subnetworks analyzed using Cytoscape plugins (MCODE, CentiScaPe, and BiNGO). Both compounds reduced HepG2 cell viability in a dose-dependent manner in both assays. Caffeine and DEET also induced DNA damage at all tested concentrations, including in co-exposure. Proteins related to the inflammatory response, signaling pathways, and xenobiotic metabolism were the main hub-bottlenecks of the chemoproteomic interaction network. These findings underscore the urgent need for further investigations into the presence of emerging pollutants in drinking water and their potential risks to human health.

PMID:40378806 | DOI:10.1016/j.chemosphere.2025.144430

J Pharmacokinet Pharmacodyn. 2025 May 16;52(3):32. doi: 10.1007/s10928-025-09979-2.

ABSTRACT

The assessment of drug-induced QT interval prolongation and associated proarrhythmic risks, such as Torsades de Pointes (TdP), has evolved significantly over the past decades. This review traces the development of nonclinical QT evaluation, highlighting key milestones and innovations that have shaped current practices in cardiac safety assessment. The emergence of regulatory guidelines, including International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B, established a nonclinical framework for evaluating drug effects on cardiac repolarization, addressing concerns raised by drug withdrawals in the 1990s. Advances in in vitro, in vivo, and in silico models have enhanced the predictive accuracy of nonclinical studies, with the hERG assay and telemetry-based animal models becoming gold standards. Recent initiatives, such as the Comprehensive in vitro Proarrhythmia Assay (CiPA) and the Japan iPS Cardiac Safety Assessment (JiCSA), emphasize integrating mechanistic insights from human-derived cardiomyocyte models and computational approaches to refine risk predictions. The 2020s mark a shift toward integrated nonclinical-clinical risk assessments, as exemplified by the ICH E14/S7B Questions and Answers. These highlight the need of best practices for study design, data analysis, and interpretation to support regulatory decision-making. Furthermore, the adoption of New Approach Methodologies (NAMs) and reinforced adherence to 3Rs principles (Reduce, Refine, Replace) reflect a commitment to ethical and innovative safety science. This review underscores the importance of harmonized and translational approaches in cardiac safety evaluation, providing a foundation for advancing drug development while safeguarding patient safety. Future directions include further integration of advanced methodologies and regulatory harmonization to streamline nonclinical and clinical risk assessments.

PMID:40379846 | DOI:10.1007/s10928-025-09979-2

J Immunother Cancer. 2025 May 15;13(5):e011312. doi: 10.1136/jitc-2024-011312.

ABSTRACT

Immune-related adverse events (irAEs) can cause severe morbidity and mortality, and they impair treatment with immune checkpoint inhibitors (ICI). Risk factors for irAEs are not well understood.We observed cases of patients having tumor deposits in their liver and lung during a workup of irAEs, which led us to hypothesize that the presence of tumor in an organ would increase the odds of developing severe irAEs in that organ. We then performed a retrospective cohort study that included patients who received an ICI for the treatment of cancer and were hospitalized between February 2011 and November 2021 at the Massachusetts General Hospital.We reviewed 384 patients hospitalized with concern for any irAE. A clinical diagnosis of ICI-related hepatitis occurred in 18% of patients with liver tumor deposits versus 8% of those without (OR 2.23, 95% CI (1.10 to 4.43), p=0.02). ICI-related pneumonitis occurred in 10% of patients with lung tumor deposits versus 4.4% of those without (OR 2.45, 95% CI (1.06 to 6.36), p=0.047). A combined analysis for liver and lung lesions demonstrated that the presence of tumor deposits in an organ increased the odds of having an irAE in that organ by over twofold (OR 2.31, 95% CI (1.34 to 3.99), p=0.002).Our results suggest that the presence of tumor deposits may represent a novel risk factor for severe irAEs in that organ.

PMID:40379269 | DOI:10.1136/jitc-2024-011312

J Control Release. 2025 May 14:113844. doi: 10.1016/j.jconrel.2025.113844. Online ahead of print.

ABSTRACT

Thoracic aortic dissection (TAD) is a life-threatening cardiovascular disease characterized by rapid progression and high morbidity. Current efforts to develop effective treatment strategies focus on targeting apoptotic aortic endothelial cells and mitigating inflammation. Here, inspired by the inflammation-neutralizing capacity of functional cells, we present multifunctional biomimetic nanovesicles (MM-LPs) co-assembled from macrophage membranes and synthetic lipids for the targeted delivery of Senkyunolide I (SEI) in TAD treatment. The integration of macrophage membranes endows MM-LPs with the ability to selectively target activated vascular endothelial cells (VECs) while adsorbing proinflammatory cytokines to suppress inflammation. Additionally, these nanoparticles enable the controlled release of SEI, leading to significant anti-apoptotic effects. Leveraging these advantages, MM-LPs effectively mitigated VEC activation, reduced apoptosis, and prevented disease progression and rupture in a BAPN-induced mouse model of TAD. Furthermore, this system significantly reduced SEI-associated toxicity and adverse effects on the liver and kidneys. These findings highlight the potential of combining natural macrophage membranes with synthetic lipids to develop a multifunctional biomimetic drug delivery system for treating VEC dysfunction while minimizing drug-related side effects.

PMID:40379216 | DOI:10.1016/j.jconrel.2025.113844

Gigascience. 2025 Jan 6;14:giaf045. doi: 10.1093/gigascience/giaf045.

ABSTRACT

BACKGROUND: In recent decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning (for example, machine-learning algorithms for translating natural language questions to SPARQL), if a sufficiently large number of examples are provided and published in a common, machine-readable, and standardized format across different resources.

FINDINGS: We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1,000 example questions and queries, including almost 100 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology.

CONCLUSIONS: We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services. URL: https://github.com/sib-swiss/sparql-examples.

PMID:40378136 | DOI:10.1093/gigascience/giaf045

JMIR Med Inform. 2025 May 16;13:e66917. doi: 10.2196/66917.

ABSTRACT

BACKGROUND: The capabilities of large language models (LLMs) to self-assess their own confidence in answering questions within the biomedical realm remain underexplored.

OBJECTIVE: This study evaluates the confidence levels of 12 LLMs across 5 medical specialties to assess LLMs' ability to accurately judge their own responses.

METHODS: We used 1965 multiple-choice questions that assessed clinical knowledge in the following areas: internal medicine, obstetrics and gynecology, psychiatry, pediatrics, and general surgery. Models were prompted to provide answers and to also provide their confidence for the correct answers (score: range 0%-100%). We calculated the correlation between each model's mean confidence score for correct answers and the overall accuracy of each model across all questions. The confidence scores for correct and incorrect answers were also analyzed to determine the mean difference in confidence, using 2-sample, 2-tailed t tests.

RESULTS: The correlation between the mean confidence scores for correct answers and model accuracy was inverse and statistically significant (r=-0.40; P=.001), indicating that worse-performing models exhibited paradoxically higher confidence. For instance, a top-performing model-GPT-4o-had a mean accuracy of 74% (SD 9.4%), with a mean confidence of 63% (SD 8.3%), whereas a low-performing model-Qwen2-7B-showed a mean accuracy of 46% (SD 10.5%) but a mean confidence of 76% (SD 11.7%). The mean difference in confidence between correct and incorrect responses was low for all models, ranging from 0.6% to 5.4%, with GPT-4o having the highest mean difference (5.4%, SD 2.3%; P=.003).

CONCLUSIONS: Better-performing LLMs show more aligned overall confidence levels. However, even the most accurate models still show minimal variation in confidence between right and wrong answers. This may limit their safe use in clinical settings. Addressing overconfidence could involve refining calibration methods, performing domain-specific fine-tuning, and involving human oversight when decisions carry high risks. Further research is needed to improve these strategies before broader clinical adoption of LLMs.

PMID:40378406 | DOI:10.2196/66917

Neurology. 2025 Jun 10;104(11):e213691. doi: 10.1212/WNL.0000000000213691. Epub 2025 May 16.

ABSTRACT

BACKGROUND AND OBJECTIVES: Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique providing fast and reliable tissue property measurements. The aim of this study was to develop an MRF-based deep-learning (DL) framework for whole-brain FCD detection.

METHODS: We included patients with pharmacoresistant focal epilepsy and pathologically/radiologically diagnosed FCD, as well as age-matched and sex-matched healthy controls (HCs). All participants underwent 3D whole-brain MRF and clinical MRI scans. T1, T2, gray matter (GM), and white matter (WM) tissue fraction maps were reconstructed from a dictionary-matching algorithm based on the MRF acquisition. A 3D ROI was manually created for each lesion. All MRF maps and lesion labels were registered to the Montreal Neurological Institute space. Mean and SD T1 and T2 maps were calculated voxel-wise across using HC data. T1 and T2 z-score maps for each patient were generated by subtracting the mean HC map and dividing by the SD HC map. MRF-based morphometric maps were produced in the same manner as in the morphometric analysis program (MAP), based on MRF GM and WM maps. A no-new U-Net model was trained using various input combinations, with performance evaluated through leave-one-patient-out cross-validation. We compared model performance using various input combinations from clinical MRI and MRF to assess the impact of different input types on model effectiveness.

RESULTS: We included 40 patients with FCD (mean age 28.1 years, 47.5% female; 11 with FCD IIa, 14 with IIb, 12 with mMCD, 3 with MOGHE) and 67 HCs. The DL model with optimal performance used all MRF-based inputs, including MRF-synthesized T1w, T1z, and T2z maps; tissue fraction maps; and morphometric maps. The patient-level sensitivity was 80% with an average of 1.7 false positives (FPs) per patient. Sensitivity was consistent across subtypes, lobar locations, and lesional/nonlesional clinical MRI. Models using clinical images showed lower sensitivity and higher FPs. The MRF-DL model also outperformed the established MAP18 pipeline in sensitivity, FPs, and lesion label overlap.

DISCUSSION: The MRF-DL framework demonstrated efficacy for whole-brain FCD detection. Multiparametric MRF features from a single scan offer promising inputs for developing a deep-learning tool capable of detecting subtle epileptic lesions.

PMID:40378378 | DOI:10.1212/WNL.0000000000213691

Anal Chem. 2025 May 16. doi: 10.1021/acs.analchem.5c02028. Online ahead of print.

ABSTRACT

Fluorescence lifetime imaging microscopy (FLIM) has been widely used as an essential multiplexing and sensing tool in frontier fields such as materials science and life sciences. However, the accuracy of lifetime estimation is compromised by limited time-correlated photon counts, and data processing is time-demanding due to the large data volume. Here, we introduce Phasor U-Net, a deep learning method designed for rapid and accurate FLIM imaging. Phasor U-Net incorporates two lightweight U-Net subnetworks to perform denoising and deconvolution to reduce the noise and calibrate the data caused by the instrumental response function, thus facilitating the downstream phasor analysis. Phasor U-Net is solely trained on computer-generated datasets, circumventing the necessity for large experimental datasets. The method reduced the modified Kullback-Leibler divergence on the phasor plots by 1.5-8-fold compared with the direct phasor method and reduced the mean absolute error of the lifetime images by 1.18-4.41-fold. We then show that this method can be used for multiplexed imaging on the small intestine samples of mice labeled by two fluorescence dyes with almost identical emission spectra. We further demonstrate that the size of quantum dots can be better estimated with measured lifetime information. This general method will open a new paradigm for more fundamental research with FLIM.

PMID:40378347 | DOI:10.1021/acs.analchem.5c02028

PLoS One. 2025 May 16;20(5):e0320669. doi: 10.1371/journal.pone.0320669. eCollection 2025.

ABSTRACT

Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification of leukemia is important to treating and saving patients' lives. Acute and myelogenous lymphocytic, chronic and myelogenous leukemia are the four kinds of leukemia. Manual inspection of microscopic images is frequently used to identify these malignant growth cells. Leukemia symptoms include fatigue, a lack of enthusiasm, a dull appearance, recurring illnesses, and easy blood loss. Identifying subtypes of leukemia for specialized therapy is one of the hurdles in this area. The suggested work predicts and classifies leukemia subtypes in gene data CuMiDa (GSE9476) using feature selection and ML techniques. The Curated Microarray Database (CuMiDa) collected 64 samples representing five classes of leukemia genes out of 22283 genes. The proposed approach utilizes the 25 most differentiating selected features for classification using machine and deep learning techniques. This study has a classification accuracy of 96.15% using Random Fores, 92.30 using Linear Regression, 96.15% using SVM, and 100% using LSTM. Deep learning methods have been shown to outperform traditional methods in leukemia gene classification by utilizing specific features.

PMID:40378164 | DOI:10.1371/journal.pone.0320669

J Agric Food Chem. 2025 May 16. doi: 10.1021/acs.jafc.5c02477. Online ahead of print.

ABSTRACT

Mycotoxins are toxic fungal metabolites that pose significant health risks. Enzyme biotransformation is a promising option for detoxifying mycotoxins and for elucidating their intracellular metabolism. However, few mycotoxin-biotransformation enzymes have been identified thus far. Here, we developed an enzyme promiscuity prediction for mycotoxin biotransformation (EPP-MB) model by fine-tuning a pretrained model using a cold protein data-splitting approach. The EPP-MB model leverages deep learning to predict enzymes capable of mycotoxin biotransformation, achieving a validation accuracy of 79% against a data set of experimentally confirmed mycotoxin-biotransforming enzymes. We applied the model to predict potential biotransformation enzymes for over 4000 mycotoxins and compiled these into the DeepMBEnzy database, which archives the predicted enzymes and related information for each mycotoxin, providing researchers with a user-friendly, publicly accessible interface at https://synbiodesign.com/DeepMBEnzy/. DeepMBEnzy is designed to facilitate the exploration and utilization of enzyme candidates in mycotoxin biotransformation, supporting further advancements in mycotoxin detoxification research and applications.

PMID:40378051 | DOI:10.1021/acs.jafc.5c02477

Aging (Albany NY). 2025 May 15;17. doi: 10.18632/aging.206250. Online ahead of print.

ABSTRACT

BACKGROUND: Both genetic and environmental factors can influence idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) development. The gut microbiota plays crucial roles in maintaining tissue homeostasis. Dysregulation of the gut microbiota can result in disease. However, whether the alteration of the gut microbiota influences IPF and COPD remains unknown.

RESEARCH QUESTION: What is the causal relationship between IPF, COPD and the gut microbiota-related metabolic pathways? What are the potential intermediate mediators in this relationship?

STUDY DESIGN AND METHODS: Intersect the gut microbiota and its metabolic pathways associated with IPF and COPD. Utilizing summary data from GWAS in public databases, a two-sample Mendelian randomization (MR) analysis was conducted on the gut microbiota-related metabolic pathway, the aspartate superpathway, in relation to IPF and COPD. Furthermore, we employed a two-step MR to quantify the proportion of influence mediated by monocytes and cDCs on the aspartate superpathway in relation to IPF and COPD.

RESULTS: The MR analysis found that the aspartate superpathway decreased the risk of developing IPF and COPD. Monocytes and cDCs acted as intermediary substances, participating in this with influence proportions of 7.88% and 6.27%, respectively.

INTERPRETATION: There is a causal link between the gut microbiota-related metabolic pathway, the aspartate superpathway, and IPF and COPD, where the influence is partially mediated by monocytes and cDCs. In clinical practice, we increase the focus on gut microbiota-mediated immune cells in relation to IPF and COPD.

PMID:40378019 | DOI:10.18632/aging.206250

Sci Adv. 2025 May 16;11(20):eadu2079. doi: 10.1126/sciadv.adu2079. Epub 2025 May 16.

ABSTRACT

The impact of SARS-CoV-2 in the lung has been extensively studied, yet the molecular regulators of host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. Some of the reasons include overreliance on transcriptomic profiling and use of nonprimary cell systems. Here we report a network-based analysis of single-cell transcriptomic profiles able to identify master regulator (MR) proteins controlling SARS-CoV-2-mediated reprogramming in pathophysiologically relevant human ciliated, secretory, and basal cells. This underscored chromatin remodeling, endosomal sorting, ubiquitin pathways, as well as proviral factors identified by CRISPR assays as components of the viral-host response in these cells. Large-scale drug perturbation screens revealed 11 candidate drugs able to invert the entire MR signature activated by SARS-CoV-2. Leveraging MR analysis and perturbational profiles of human primary cells represents an innovative approach to investigate pathogen-host interactions in multiple airway conditions for drug prioritization.

PMID:40378209 | DOI:10.1126/sciadv.adu2079