J Cheminform. 2025 May 21;17(1):79. doi: 10.1186/s13321-025-01017-0.

ABSTRACT

Named entities, such as chemicals/drugs, genes/proteins, and diseases, and their associations are not only important components of biomedical literature, but also the foundation of creating biomedical knowledgebases and knowledge graphs. This work addresses the challenges of expressing co-occurrence associations between named entities extracted from a biomedical literature corpus in a machine-readable format. We developed a Resource Description Framework (RDF) data model and integrated it into the PubChemRDF resource, which is freely accessible and publicly available. The developed co-occurrence data model was populated into a triplestore with named entities and their associations derived from text mining of millions of biomedical references found in PubMed. The utility of the data model was demonstrated through multiple use cases. Together with meta-data modeling of the references including the information about the author, journal, grant, and funding agency, this data model allows researchers to address pertinent biomedical questions through SPARQL queries and helps to exploit biomedical knowledge in various user perspectives and use cases.

PMID:40399973 | DOI:10.1186/s13321-025-01017-0

Child Adolesc Psychiatry Ment Health. 2025 May 21;19(1):60. doi: 10.1186/s13034-025-00915-3.

ABSTRACT

BACKGROUND: This study evaluated pharmacogenomic (PGx) testing in children and adolescents with autism spectrum disorder (ASD). ASD frequently presents with co-occurring depression and anxiety. This complex phenotype often results in psychotropic medication polypharmacy. Incorporating PGx testing into the medical work-up may reduce polypharmacy and improve quality of life with symptom reduction.

METHODS: A retrospective electronic health record (EHR) review between January 2017 and May 2023. Individuals either received PGx testing or treatment as usual (TAU). The co-primary outcomes were instance of polypharmacy and the Pediatric Quality of Life Enjoyment and Satisfaction Questionnaire (PQ-LES-Q). Secondary outcomes included length of stay, average number of psychotropic medications, readmissions and assessments measuring severity of symptoms or behavioral impact. When at least one daily psychotropic medication was prescribed and reported to have an increased probability of gene-drug interactions, the individual was classified as "incongruent" (PGx-I). Individuals were categorized as "congruent" (PGx-C) if all prescribed psychotropic medications were without potential gene-drug interactions. Polypharmacy was evaluated and compared within the PGx-C and PGx-I subgroups.

RESULTS: A total of 99 individuals with ASD were analyzed. At the time of admission, 93% of individuals were prescribed at least one psychotropic medication and over half of these individuals were prescribed medications with potential gene-drug interactions. Following PGx testing, there was an overall reduction in prescribed medications with potential gene-drug interactions. No differences were observed between the PGx and TAU groups in polypharmacy, quality of life, or symptom assessments of depression, anxiety, obsessive-compulsive disorder and body-focused repetitive behaviors. Subanalysis comparing congruent ("use as directed") or incongruent ("use with caution"), as well as exploratory analysis of only CYP2D6 and CYP2C19 gene-drug interactions, were observed to have a similar profile between treatment groups for all primary and secondary outcomes, except for the average number of psychotropic medications prescribed.

CONCLUSIONS: Incorporating PGx testing into the medical workup did not improve outcomes, with all treatment groups achieving similar levels of polypharmacy and quality of life. Analysis of secondary outcomes revealed some differences in medication prescribing when stratifying by congruency; however, no differences were observed between treatment groups for all other secondary outcomes.

PMID:40399951 | DOI:10.1186/s13034-025-00915-3

Sci Rep. 2025 May 21;15(1):17634. doi: 10.1038/s41598-025-02781-7.

ABSTRACT

This study examines the synthesis and biological evaluation of novel tetrazole derivatives of 3,3'-dimethoxybenzidine as potential anticancer agents, focusing on their cytotoxic, apoptotic, and anti-inflammatory properties. Ten derivatives were synthesized using thioureas as precursors, characterized through spectroscopic methods, and assessed for their in silico toxicological profiles using the ADMET-AI and ProTox 3.0 platforms. In vitro cytotoxic activity was evaluated against four human cancer cell lines (HTB-140, A549, HeLa, SW620) and one normal cell line (HaCaT) using MTT and LDH assays. Mechanistic studies included apoptosis assessment via flow cytometry and interleukin-6 (IL-6) analysis using ELISA. The synthesized tetrazole derivatives demonstrated significant anticancer potential, exhibiting selective cytotoxicity against cancer cell lines, robust induction of apoptosis, and a notable reduction in IL-6 levels. Their favorable toxicity profiles, as observed in both in silico and in vitro evaluations, support their potential as promising candidates for further development. The tested compounds showed strong inhibitory activity against the apoptosis regulator Bcl-2, with binding affinities comparable to those of native ligands. Western blot analysis revealed a dramatic loss of Bcl-2 protein expression in selected cancer cells during exposure to compound 5. Additionally, this research highlights the innovative use of hazardous substrates in drug discovery, aligning with the principles of green chemistry. Future efforts should focus on optimizing the most active derivatives and conducting in vivo studies to confirm their therapeutic potential and safety.

PMID:40399589 | DOI:10.1038/s41598-025-02781-7

Drug Resist Updat. 2025 May 13;81:101249. doi: 10.1016/j.drup.2025.101249. Online ahead of print.

ABSTRACT

AIMS: Genotoxic drug resistance is one of the major obstacles for cancer treatment. Our previous study demonstrates that cold shock domain containing E1 (CSDE1) is associated with drug resistance. In this study, we aim to demonstrate that CSDE1 regulates cellular response to genotoxic drugs and to investigate its mechanism of action in drug resistance.

METHODS: Tissues and blood samples from cancer patients were used to evaluate the relationship between CSDE1 and genotoxic drug response. Comet and immunofluorescence assays were conducted to investigate the role of CSDE1 in DNA damage repair. Systematic knockout mouse models were used to study the underlying mechanism involved. Biotin pull-down, EMSA and co-IP assays were used to probe the triplex structure of CSDE1-protein (eIF3a)-RNA (RPA2).

RESULTS: CSDE1 elevation correlates with poor response in patient and increased resistance in cell lines to genotoxic drugs. CSDE1 upregulated the nucleotide excision repair (NER) and homologous recombination (HR) pathways. In X-ray irradiation or bleomycin-induced DNA damage mouse model, systemic CSDE1 knockout resulted in increased DNA damage. In both a CSDE1 knockout mouse model and cancer cell lines, CSDE1 inhibited the cGAS-STING pathway through RPA2. Mechanistic studies indicated that CSDE1 serves as a hub for the binding of the CSDE1-protein (eIF3a)-RNA (RPA2) ternary complex.

CONCLUSIONS: This study reveals the new role of CSDE1 in enhancing resistance to genotoxic drugs, and the detailed zipper-like cross ternary structural of CSDE1. It provides a new strategy for enhancing genotoxic drugs sensitivity.

PMID:40398074 | DOI:10.1016/j.drup.2025.101249

Clin Pharmacokinet. 2025 May 21. doi: 10.1007/s40262-025-01507-2. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Following the development of cystic fibrosis transmembrane conductance regulator (CFTR) modulators (ivacaftor, tezacaftor, elexacaftor, and lumacaftor), the prognosis for people diagnosed with cystic fibrosis (pwCF) has improved. Understanding the pharmacokinetics (PK) of CFTR modulators is crucial to provide optimal care, particularly in special cystic fibrosis (CF) populations such as pwCF with hepatic impairment, pancreatic insufficiency, those who are pregnant or lactating, or who are children. We aim to provide an overview of the PK of CFTR modulators in these populations.

METHODS: A systematic literature search was carried out in PubMed and Embase on 20 June 2024. Studies were considered relevant when information on PK or exposure of CFTR-modulating drugs was available.

RESULTS: PwCF with mild/moderate hepatic impairment do not exhibit substantially higher exposure to CFTR modulators compared with those without liver involvement or healthy individuals. Similarly, exocrine pancreatic insufficiency has no effect on the PK of CFTR modulators in adult pwCF. In contrast, pediatric pwCF are exposed to higher levels of CFTR modulators relative to adults, as children receive higher weight-based doses (mg/kg) to ensure equivalent therapeutic efficacy.

CONCLUSIONS: The PK of CFTR modulators have been more extensively studied in adults, pwCF with mild/moderate hepatic impairment, and children. However, ensuring adequate dosing remains challenging. Knowledge gaps persist for adults with severe hepatic impairment (Child-Pugh Class C), children with CF-induced hepatic impairment, and pregnant or lactating pwCF. Future research addressing these gaps, through incorporating routine clinical data, is crucial for improving clinical guidelines and optimizing dosing regimens, thereby advancing towards evidence-based utilization of CFTR modulators.

PMID:40399734 | DOI:10.1007/s40262-025-01507-2

BMC Cancer. 2025 May 21;25(1):907. doi: 10.1186/s12885-025-14301-x.

ABSTRACT

BACKGROUND: [18F]FDG PET/CT scan combined with [18F]PSMA-1007 PET/CT scan is commonly conducted for detecting bone metastases in prostate cancer (PCa). However, it is expensive and may expose patients to more radiation hazards. This study explores deep learning (DL) techniques to synthesize [18F]PSMA-1007 PET bone images from CT bone images for the early detection of bone metastases in PCa, which may reduce additional PET/CT scans and relieve the burden on patients.

METHODS: We retrospectively collected paired whole-body (WB) [18F]PSMA-1007 PET/CT images from 152 patients with clinical and pathological diagnosis results, including 123 PCa and 29 cases of benign lesions. The average age of the patients was 67.48 ± 10.87 years, and the average lesion size was 8.76 ± 15.5 mm. The paired low-dose CT and PET images were preprocessed and segmented to construct the WB bone structure images. 152 subjects were randomly stratified into training, validation, and test groups in the number of 92:41:19. Two generative adversarial network (GAN) models-Pix2pix and Cycle GAN-were trained to synthesize [18F]PSMA-1007 PET bone images from paired CT bone images. The performance of two synthesis models was evaluated using quantitative metrics of mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM), as well as the target-to-background ratio (TBR).

RESULTS: The results of DL-based image synthesis indicated that the synthesis of [18F]PSMA-1007 PET bone images from low-dose CT bone images was highly feasible. The Pix2pix model performed better with an SSIM of 0.97, PSNR of 44.96, MSE of 0.80, and MAE of 0.10, respectively. The TBRs of bone metastasis lesions calculated on DL-synthesized PET bone images were highly correlated with those of real PET bone images (Pearson's r > 0.90) and had no significant differences (p < 0.05).

CONCLUSIONS: It is feasible to generate synthetic [18F]PSMA-1007 PET bone images from CT bone images by using DL techniques with reasonable accuracy, which can provide information for early detection of PCa bone metastases.

PMID:40399853 | DOI:10.1186/s12885-025-14301-x

Pediatr Blood Cancer. 2025 May 21:e31797. doi: 10.1002/pbc.31797. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate determination of B/T-cell lineage and the presence of the ETV6-RUNX1 translocation is critical for diagnosing acute lymphoblastic leukemia (ALL), as these factors influence treatment decisions and outcomes. However, these diagnostic processes often rely on advanced tools unavailable in low-resource settings, creating a need for alternative solutions.

PROCEDURE: We developed a deep learning pipeline to analyze Giemsa-stained bone marrow (BM) aspirate smears. The models were trained to distinguish between ALL, acute myeloid leukemia (AML), and non-leukemic BM samples, predict B- and T-cell lineage in ALL, and detect the presence of the ETV6-RUNX1 translocation. The performance was evaluated using cross-validation (CV) and an external validation cohort.

RESULTS: The models achieved a statistically significant area under the curve (AUC) of 0.99 in distinguishing ALL from AML and control samples. In cross-validation (CV), the models achieved a cross-validation AUC of 0.74 for predicting B/T subtypes. For predicting ETV6-RUNX1 translocation, the models achieved an AUC of 0.80. External cohort validation confirmed significant AUCs of 0.72 for B/T subtype classification and 0.69 for ETV6-RUNX1 translocation prediction.

CONCLUSIONS: Convolutional neural networks (CNNs) demonstrate potential as a diagnostic tool for pediatric ALL, enabling the identification of B/T lineage and ETV6-RUNX1 translocation from Giemsa-stained smears. These results pave the way for future utilization of CNNs as a diagnostic modality for pediatric leukemia in low-resource settings, where access to advanced diagnostic techniques is limited.

PMID:40399768 | DOI:10.1002/pbc.31797

Anal Chem. 2025 May 21. doi: 10.1021/acs.analchem.4c05842. Online ahead of print.

ABSTRACT

Spectrum-structure correlation is crucial to identify and quantify chemicals, in which classification of mixtures and identification of functional groups are two central tasks. Deep learning-driven algorithms have made significant strides to these two tasks. However, many of these algorithms are merely adaptations of models originally designed for computer vision applications. As a result, the models often suffer from either low accuracy or limited generality when applied to spectral data due to the overlooked inherent limitations in feature richness and volume of spectral data. Here, in light of the distinctive difference in the attention of global and local information in spectral data between these two tasks, we developed contrapuntally two CNN-based algorithms, incorporating multiscale convolution and attention mechanism, to address the unique requirements of each task. It was found that the lightweight CNN-Peak algorithm is favored for the classification of a mixture, a type of single-label task, in which the feature fusion of global information is more important. Meanwhile, the more complex ResNet-ResPeak algorithm is ideally suited for the identification of functional groups, a type of multilabel task, in which the feature extraction of local information takes precedence. The task-oriented, conceptual design of deep learning algorithms not only enhances the efficacy and accuracy of spectrum-structure correlation analysis but also feeds back to achieve a more rigorous experimental design and implementation, forming a closed loop of AI for Science.

PMID:40399767 | DOI:10.1021/acs.analchem.4c05842

Sci Rep. 2025 May 21;15(1):17617. doi: 10.1038/s41598-025-99515-6.

ABSTRACT

Emotion recognition in text is a fundamental task in natural language processing, underpinning applications such as sentiment analysis, mental health monitoring, and content moderation. Although transformer-based models like RoBERTa have advanced contextual understanding in text, they still face limitations in identifying subtle emotional cues, handling class imbalances, and processing noisy or informal input. To address these challenges, this paper introduces Emotion-Aware RoBERTa, an enhanced framework that integrates an Emotion-Specific Attention (ESA) layer and a TF-IDF based gating mechanism. These additions are designed to dynamically prioritize emotionally salient tokens while suppressing irrelevant content, thereby improving both classification accuracy and robustness. The model achieved 96.77% accuracy and a weighted F1-score of 0.97 on the primary dataset, outperforming baseline RoBERTa and other benchmark models such as DistilBERT and ALBERT with a relative improvement ranging from 9.68% to 10.87%. Its generalization capability was confirmed across two external datasets, achieving 88.03% on a large-scale corpus and 65.67% on a smaller, noisier dataset. An ablation study revealed the complementary impact of the ESA and TF-IDF components, balancing performance and inference efficiency. Attention heatmaps were used to visualize ESA's ability to focus on key emotional expressions, while inference-time optimizations using FP16 and Automatic Mixed Precision (AMP) reduced memory consumption and latency. Additionally, McNemar's statistical test confirmed the significance of the improvements over the baseline. These findings demonstrate that Emotion-Aware RoBERTa offers a scalable, interpretable, and deployment-friendly solution for fine-grained emotion recognition, making it well-suited for real-world NLP applications in emotion-aware systems.

PMID:40399457 | DOI:10.1038/s41598-025-99515-6

Sci Rep. 2025 May 21;15(1):17698. doi: 10.1038/s41598-025-02168-8.

ABSTRACT

The evaluation of students in physical education remains a formidable challenge due to the limitations of traditional assessment approaches, which are often excessively one-dimensional. This study proposes a solution utilizing deep learning via multi-attribute user evaluation modelling to address these issues. This development proposal utilizes all available data, including physical activities, cognitive tasks, emotional responses, and social interactions, for a comprehensive assessment of student performance. The methodology comprises a ten-step process that involves information collection, preparation, model construction, and deployment, followed by regular review and adjustments. The model has considerable efficacy, with a high level of accuracy and reduced errors. Moreover, an experimental investigation illustrates its robustness, having attained a low mean score. The analysis indicates that the current models exhibit more flexibility in providing personalized feedback to improve educational outcomes and enhance decision-making. Moreover, the model incorporates visualization tools such as heatmaps, which affirm the system's ability to monitor performance and progressively adjust to the dynamics of students. The developed approach incorporates automated, objective, and scalable attributes that improve student assessment. This also aids in tackling many multi-faceted challenges in physical education while formulating effective interventions for student advancement. Subsequent research may focus on the integration of real-time sensor data, enhancement of computational efficiency, and wider application across diverse educational organizations.

PMID:40399440 | DOI:10.1038/s41598-025-02168-8

Sci Rep. 2025 May 21;15(1):17697. doi: 10.1038/s41598-025-98427-9.

ABSTRACT

Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging techniques, such as CT and MRI, rely on manual interpretation, which can be both time-intensive and subject to variability. This study introduces FasNet, an innovative hybrid deep learning model that combines ResNet-50 and VGG-16 architectures, incorporating Channel and Spatial Attention mechanisms alongside Monte Carlo Dropout to improve segmentation precision and reliability. FasNet leverages ResNet-50's robust feature extraction and VGG-16's detailed spatial feature capture to deliver superior liver tumor segmentation accuracy. Channel and spatial attention mechanisms could selectively focus on the most relevant features and spatial regions for suitable segmentation with good accuracy and reliability. Monte Carlo Dropout estimates uncertainty and adds robustness, which is critical for high-stakes medical applications. Tested on the LiTS17 dataset, FasNet achieved a Dice Coefficient of 0.8766 and a Jaccard Index of 0.8487, surpassing several state-of-the-art methods. The Channel and Spatial Attention mechanisms in FasNet enhance feature selection, focusing on the most relevant spatial and channel information, while Monte Carlo Dropout improves model robustness and uncertainty estimation. These results position FasNet as a powerful diagnostic tool, offering precise and automated liver tumor segmentation that aids in early detection and precise treatment, ultimately enhancing patient outcomes.

PMID:40399406 | DOI:10.1038/s41598-025-98427-9

Sci Rep. 2025 May 21;15(1):17571. doi: 10.1038/s41598-025-02378-0.

ABSTRACT

In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer architecture, crafted to adeptly handle 256 × 256 pixel images across three color channels (RGB). Beginning with an input layer complemented by a Zero Padding 2D layer to preserve spatial dimensions, our model ensures the retention of crucial geographical information across its depth. The innovative use of a convolutional layer with 64 7 × 7 filters, followed by batch normalization and Rel U activation, allows for the extraction and representation of intricate patterns from the input data. Key to our model's design is the incorporation of residual blocks, facilitating the learning of deeper networks by alleviating the vanishing gradient problem. These blocks combine Conv2D layers, batch normalization, activation layers, and shortcut connections, ensuring robust and efficient feature extraction at various levels of abstraction. The GlobalAveragePooling2D layer towards the model's end succinctly summarizes the extracted features, preparing the model for the final classification stage. This stage features a dropout layer for regularization, a dense layer with 512 units for further pattern learning, and a final dense layer with 8 units and a soft max activation function, producing a probability distribution across different disease classes. Our model's architecture is not just a testament to the sophistication of modern deep learning techniques but also highlights the novelty of applying such complex structures to the challenges of agricultural disease detection. We utilized a datasets consisting of 4000 high-resolution images of tea leaves, encompassing both diseased and healthy states, meticulously captured in the tea gardens of Pathantula, Sylhet, Bangladesh. Employing the Canon EOS 250d Camera ensured detailed representation crucial for training a robust deep learning model for disease detection in tea plants. By achieving remarkable accuracy in identifying diseases in tea leaves, this research not only sets a new benchmark for precision in agricultural diagnostics but also opens avenues for future innovations in the field of precision agriculture.

PMID:40399405 | DOI:10.1038/s41598-025-02378-0

Sci Rep. 2025 May 21;15(1):17594. doi: 10.1038/s41598-025-02144-2.

ABSTRACT

Masked identification of faces is necessary for authentication purposes. Face masks are frequently utilized in a wide range of professions and sectors including public safety, health care, schooling, catering services, production, sales, and shipping. In order to solve this issue and provide precise identification and verification in masked events, masked facial recognition equipment has emerged as a key innovation. Although facial recognition is a popular and affordable biometric security solution, it has several difficulties in correctly detecting people who are wearing masks. As a result, a reliable method for identifying the masked faces is required. In this developed model, a deep learning-assisted masked face identification framework is developed to accurately recognize the person's identity for security concerns. At first, the input images are aggregated from standard datasets. From the database, both the masked face images and mask-free images are used for training the Generative Adversarial Network (GAN) model. Then, the collected input images are given to the GAN technique. If the input is a masked face image, then the GAN model generates a mask-free face image and it is considered as feature set 1. If the input is a mask-free image, then the GAN model generates a masked face image and these images are considered as feature set 2. If the input images contain both masked and mask-free images, then it is directly given to Dual Scale Adaptive Efficient Attention Network (DS-AEAN). Otherwise, generated feature set 1 and feature set 2 are given to the DS-AEAN for recognizing the faces to ensure the person's identity. The effectiveness of this model is further maximized using the Enhanced Addax Optimization Algorithm (EAOA). This model is helpful for a precise biometric verification process. The outcomes of the designed masked face recognition model are evaluated with the existing models to check its capability.

PMID:40399389 | DOI:10.1038/s41598-025-02144-2

Sci Rep. 2025 May 21;15(1):17593. doi: 10.1038/s41598-025-02209-2.

ABSTRACT

The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety of sizes, shapes, and visual characteristics, requiring individualized treatment strategies for each patient. Radiologists require considerable proficiency to manually detect brain malignancies. However, tumor recognition remains inefficient, imprecise, and labor-intensive in manual procedures, underscoring the need for automated methods. This study introduces an effective approach for identifying brain lesions in magnetic resonance imaging (MRI) images, minimizing dependence on manual intervention. The proposed method improves image clarity by combining guided filtering techniques with anisotropic Gaussian side windows (AGSW). A morphological analysis is conducted prior to segmentation to exclude non-tumor regions from the enhanced MRI images. Deep neural networks segment the images, extracting high-quality regions of interest (ROIs) and multiscale features. Identifying salient elements is essential and is accomplished through an attention module that isolates distinctive features while eliminating irrelevant information. An ensemble model is employed to classify brain tumors into different categories. The proposed technique achieves an overall accuracy of 99.94% and 99.67% on the publicly available brain tumor datasets BraTS2020 and Figshare, respectively. Furthermore, it surpasses existing technologies in terms of automation and robustness, thereby enhancing the entire diagnostic process.

PMID:40399378 | DOI:10.1038/s41598-025-02209-2

Sci Rep. 2025 May 21;15(1):16886. doi: 10.1038/s41598-025-00303-z.

ABSTRACT

Manual Small-Incision Cataract Surgery (SICS) is a prevalent technique in low- and middle-income countries (LMICs) but understudied with respect to computer assisted surgery. This prospective cross-sectional study introduces the first SICS video dataset, evaluates effectiveness of phase recognition through deep learning (DL) using the MS-TCN + + architecture, and compares its results with the well-studied phacoemulsification procedure using the Cataract-101 public dataset. Our novel SICS-105 dataset involved 105 patients recruited at Sankara Eye Hospital in India. Performance is evaluated with frame-wise accuracy, edit distance, F1-score, Precision-Recall AUC, sensitivity, and specificity. The MS-TCN + + architecture performs better on the Cataract-101 dataset, with an accuracy of 89.97% [CI 86.69-93.46%] compared to 85.56% [80.63-92.09%] on the SICS-105 dataset (ROC AUC 99.10% [98.34-99.51%] vs. 98.22% [97.16-99.26%]). The accuracy distribution and confidence-intervals overlap and the ROC AUC values range 46.20 to 94.18%. Even though DL is found to be effective for phase recognition in SICS, the larger number of phases and longer duration makes it more challenging compared to phacoemulsification. To support further developments, we make our dataset open access. This research marks a crucial step towards improving postoperative analysis and training for SICS.

PMID:40399321 | DOI:10.1038/s41598-025-00303-z

J Pharm Biomed Anal. 2025 May 14;264:116967. doi: 10.1016/j.jpba.2025.116967. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible respiratory disease with poor survival rates. Despite significant research efforts, IPF still lacks a curative treatment. Excessive epithelial-mesenchymal transition (EMT) contributes to approximately one-third of fibroblasts in pulmonary fibrosis and plays a critical role in IPF pathogenesis. Identifying factors that regulate EMT is essential for developing effective therapeutic strategies for IPF. In this study, functional metabolomics revealed significant alterations in multiple metabolites in transforming growth factor-beta 1 (TGF-β1)-induced A549 cells, with pyroglutamic acid and 5-oxoprolinase (OPLAH) being identified as the most critical factors. Cellular experiments demonstrated that pyroglutamic acid effectively inhibited TGF-β1-induced EMT in A549 cells. Mechanistically, pyroglutamic acid inhibited IPF by suppressing EMT through the inhibition of Smad2/3 expression in TGF-β1-induced A549 cells. Bioinformatics analysis further elucidated the pyroglutamate is a potential metabolite that inhibits EMT. In addition, this study is the first to highlight the pivotal role of pyroglutamic acid and OPLAH in regulating EMT in IPF, offering novel insights into the metabolic mechanisms involved in IPF inhibition and providing a foundation for developing innovative therapeutic approaches for IPF.

PMID:40398246 | DOI:10.1016/j.jpba.2025.116967

BMC Microbiol. 2025 May 21;25(1):310. doi: 10.1186/s12866-025-04024-1.

ABSTRACT

BACKGROUND: Slum dwellers face significant infrastructure and public health challenges like poor housing and drainage, inadequate sanitation, and limited access to clean water, leading to increased disease transmission and resistance to antibiotic treatments. This study evaluated the impact of heavy metals on antibiotic resistance patterns of Escherichia coli in wastewater from slums of Bwaise II, Bwaise III, Kazo, and Makerere III in Kawempe division, Kampala.

METHODS: Levels of heavy metals (lead, mercury, cadmium, chromium, and arsenic) in wastewater were determined using inductively coupled plasma mass spectroscopy. Escherichia coli were isolated from wastewater using MacConkey agar and their susceptibility to 50 µl of stock antibiotics (tetracycline, amoxicillin, ceftriaxone at 30 µg/ml, and ciprofloxacin at 5 µg/ml) determined. The potential of heavy metals to induce antibiotic resistance in Escherichia coli was determined by culturing susceptible isolates in 200 µl of Luria-Bertina broth containing stock antibiotics (10 µl), or stock antibiotics (10 µl) and stock heavy metals (10 µl). Stock heavy metals were prepared from the average concentration of heavy metals detected in wastewater.

RESULTS: Detectable levels of heavy metals were reported in wastewater from Bwaise II, Kazo and Makerere III only. Lead, cadmium and arsenic, mercury and chromium, were highest in Bwaise II, Kazo, and Makerere III, respectively. The occurrence of Escherichia coli resistant to at least an antibiotic was 72.8% (169 of 232) and resistance to tetracycline, ceftriaxone, amoxicillin, and ciprofloxacin were 34.1%, 28.9%, 35.3%, and 34.5%, respectively. Study findings further revealed a positive correlation (R2 = 0.371-0.985) between the presence of heavy metals in wastewater and antibiotic resistance patterns of Escherichia coli. Also, heavy metals; lead (77.41 µg/ml), mercury (1.44 µg/ml), and cadmium (10.21 µg/ml) significantly (p < 0.05) induced antibiotic resistance in susceptible Escherichia coli.

CONCLUSION: Wastewater in Kawempe slums is polluted with heavy metals and high prevalence of antibiotic-resistant Escherichia coli. Inadequate infrastructure in slums facilitate discharge of wastewater polluted with heavy metals, which in turn play a role in increasing antibiotic resistance. There is need for proper wastewater management to contain the prevalence of antibiotic resistance.

PMID:40399779 | DOI:10.1186/s12866-025-04024-1

Nat Biotechnol. 2025 May 21. doi: 10.1038/s41587-025-02649-1. Online ahead of print.

ABSTRACT

Cell-tagging strategies with DNA barcodes have enabled the analysis of clone size dynamics and clone-restricted transcriptomic landscapes in heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here we present a multi-kingdom genetic barcoding system, CloneSelect, which enables a target cell clone to be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first stably tagged with DNA barcodes and propagated so that their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored during the experiment using CRISPR base editing. CloneSelect is scalable and compatible with single-cell RNA sequencing. We demonstrate the versatility of CloneSelect in human embryonic kidney 293T cells, mouse embryonic stem cells, human pluripotent stem cells, yeast cells and bacterial cells.

PMID:40399693 | DOI:10.1038/s41587-025-02649-1

Nature. 2025 May 21. doi: 10.1038/s41586-025-09041-8. Online ahead of print.

ABSTRACT

Current approaches used to track stem cell clones through differentiation require genetic engineering1,2 or rely on sparse somatic DNA variants3,4, which limits their wide application. Here we discover that DNA methylation of a subset of CpG sites reflects cellular differentiation, whereas another subset undergoes stochastic epimutations and can serve as digital barcodes of clonal identity. We demonstrate that targeted single-cell profiling of DNA methylation5 at single-CpG resolution can accurately extract both layers of information. To that end, we develop EPI-Clone, a method for transgene-free lineage tracing at scale. Applied to mouse and human haematopoiesis, we capture hundreds of clonal differentiation trajectories across tens of individuals and 230,358 single cells. In mouse ageing, we demonstrate that myeloid bias and low output of old haematopoietic stem cells6 are restricted to a small number of expanded clones, whereas many functionally young-like clones persist in old age. In human ageing, clones with and without known driver mutations of clonal haematopoieis7 are part of a spectrum of age-related clonal expansions that display similar lineage biases. EPI-Clone enables accurate and transgene-free single-cell lineage tracing on hematopoietic cell state landscapes at scale.

PMID:40399669 | DOI:10.1038/s41586-025-09041-8

Nat Commun. 2025 May 21;16(1):4720. doi: 10.1038/s41467-025-59656-8.

ABSTRACT

Ribosome biogenesis follows a cascade of pre-rRNA folding and processing steps, coordinated with ribosomal protein incorporation. Nucleolar 90S pre-ribosomes are well-described stable intermediates, composed of pre-18S rRNA, ribosomal S-proteins, U3 snoRNA, and ~70 assembly factors. However, how numerous snoRNAs control pre-rRNA modification and folding during early maturation events remains unclear. We identify snR30 (human U17), the only essential H/ACA snoRNA in yeast, which binds with Cbf5-Gar1-Nop10-Nhp2 to a pre-18S rRNA subdomain containing platform helices and ES6 of the 40S central domain. Integration into the 90S is blocked by RNA hybridization with snR30. The snoRNP complex coordinates the recruitment of early assembly factors Krr1-Utp23-Kri1 and ribosomal proteins uS11-uS15, enabling isolated subdomain assembly. Krr1-dependent release of snR30 culminates in integration of the platform into the 90S. Our study reveals the essential role of snR30 in chaperoning central domain formation as a discrete assembly unit externalized from the pre-ribosomal core.

PMID:40399280 | DOI:10.1038/s41467-025-59656-8