Heart Lung Circ. 2025 Apr 21:S1443-9506(24)01910-3. doi: 10.1016/j.hlc.2024.11.011. Online ahead of print.

ABSTRACT

BACKGROUND: The incidence of sudden cardiac death (SCD) in patients with schizophrenia is three to four times higher than in the general population. While the majority of SCD in patients with schizophrenia are due to ischaemic or structural heart disease, about 10% of deaths remain unexplained. In recent reviews of premature deaths in patients with schizophrenia, these deaths were postulated to be secondary to malignant cardiac arrhythmias.

METHODS: A retrospective study conducted jointly by the Victorian Institute of Forensic Medicine and the Department of Genomic Medicine, The Royal Melbourne Hospital, Australia, was designed to identify novel genomic loci that link schizophrenia and sudden unexplained death. Cases included deceased patients over a 5-year period (2016-2021) with an in-life diagnosis of schizophrenia and an unascertained cause of death after comprehensive post-mortem histopathological and toxicological assessment. Individuals also required a source of DNA to be available.

RESULTS: Thirty-six individuals, 26 males and 10 females, age range 18-65 years, met the study inclusion criteria. Autopsy revealed 10 individuals had cardiomegaly, six had cardiac hypertrophy, six had a body mass index (BMI) >40, and four had mild myocardial fibrosis. Thirteen next of kin (NOK) (36%) consented to involvement in the study and 12 individuals (92%) underwent whole exome sequencing (WES) via a research platform. Two clinically actionable results were detected-a pathogenic Desmoplakin (DES) variant and a dihydropyrimidine dehydrogenase (DPYD) pharmacogenomic variant.

CONCLUSION: Our study, with comprehensive autopsy examination adds to the literature on SCD in schizophrenia. Genes that are currently associated with inherited arrhythmias or schizophrenia such as Neuregulin 1 were not found in this study group. The pathogenic DES variant would likely have been found had the family accepted referral to a Cardiac Genetics service, at the time of death.

PMID:40263070 | DOI:10.1016/j.hlc.2024.11.011

BMJ. 2025 Apr 22;389:r699. doi: 10.1136/bmj.r699.

NO ABSTRACT

PMID:40262835 | DOI:10.1136/bmj.r699

Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2025 May;39(5):496-500. doi: 10.13201/j.issn.2096-7993.2025.05.020.

ABSTRACT

Objective:Ménière's disease（MD） is a common disorder of the inner ear. The fluctuating clinical symptoms and the absence of gold standards for diagnosis have posed serious problems for clinical diagnosis and treatment over the years. With the development of science and technology, artificial intelligence （AI） has been widely used in the field of medicine, and the potential of AI application to MD is demonstrated. The purpose of this review is to outline the use of AI in MD. Initially, specific instances where AI aids in differentiating MD from other causes of vertigo are presented. Furthermore, the role of AI in the evaluation of Endolymphatic Hydrops （EH）, particularly through imaging and biochemical assays, is highlighted due to its correlation with MD. Additionally, the effectiveness of AI in managing MD patients and forecasting disease progression is examined. In conclusion, the prevalent challenges hindering the clinical integration of AI in MD treatment are discussed, alongside potential strategies to surmount these barriers.

PMID:40263665 | DOI:10.13201/j.issn.2096-7993.2025.05.020

Sci Rep. 2025 Apr 22;15(1):13964. doi: 10.1038/s41598-025-99131-4.

ABSTRACT

Finite element analysis (FEA), a biomechanical simulation technique capable of providing direct mechanical visualization for CT-based digital models, has been extensively applied to fossil image datasets to address key evolutionary questions in paleontology. However, the rock matrix filling intertrabecular space of fossils often causes severe deviations in FEA results. Segmentation strategies such as thresholding and manual labeling have been employed to mitigate these disturbances. However, the efficiency of manual segmentation and the accuracy of thresholding remain questionable. In this study, we applied FEA combined with deep learning-based segregation on a femoral specimen of Jeholosaurus (a small bipedal dinosaur). This novel methodology efficiently generates the FE model with stress distribution that closely reflects the trabecular architecture in fossils of extinct taxa, reflecting a more natural state of biomechanical performance with high biological reality. Our approach provides a practical strategy for studying the biomechanics, functional morphology, and taxonomy of extinct species.

PMID:40263619 | DOI:10.1038/s41598-025-99131-4

Sci Rep. 2025 Apr 22;15(1):13942. doi: 10.1038/s41598-025-98195-6.

ABSTRACT

The fault of one of the key systems in artificial satellites, the Control Moment Gyroscope (CMG), can lead to significant economic losses and irreparable consequences. Therefore, it is crucial to diagnose its faults promptly. Traditional fault diagnosis methods, however, face challenges such as local feature traps and difficulty in feature extraction when dealing with CMG vibration signals, making it hard to meet the requirements for accuracy and robustness. Hence, it is essential to design a high-accuracy model to assess the health status of CMG on time. To address these issues, a fault diagnosis method that combines the Joint Attention Mechanism (JAM) with one-dimensional dilated convolutional networks and residual connections is proposed. The method efficiently learns feature information through the JAM, effectively addressing the time-varying characteristics of vibration signals and focusing more on fault-related features. The influence of rotational speed on the model is overcome to some extent through JAM. The three rotational speeds are mixed as datasets, and the model achieves high accuracy. The proposed method significantly enhances the accuracy and robustness of CMG fault diagnosis. Experimental results on a self-collected dataset demonstrate that the proposed method achieves excellent accuracy (98.14%) and robustness in CMG fault diagnosis.

PMID:40263562 | DOI:10.1038/s41598-025-98195-6

Sci Rep. 2025 Apr 22;15(1):13904. doi: 10.1038/s41598-025-98015-x.

ABSTRACT

Global food security depends on tomato growing, but several fungal, bacterial, and viral illnesses seriously reduce productivity and quality, therefore causing major financial losses. Reducing these impacts depends on early, exact diagnosis of diseases. This work provides a deep learning-based ensemble model for tomato leaf disease classification combining MobileNetV2 and ResNet50. To improve feature extraction, the models were tweaked by changing their output layers with GlobalAverage Pooling2D, Batch Normalization, Dropout, and Dense layers. To take use of their complimentary qualities, the feature maps from both models were combined. This study uses a publicly available dataset from Kaggle for tomato leaf disease classification. Training on a dataset of 11,000 annotated pictures spanning 10 disease categories, including bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, target spot, yellow leaf curl virus, mosaic virus, and healthy leaves. Data preprocessing included image resizing and splitting, along with an 80-10-10 split, allocating 80% for training, 10% for testing, and 10% for validation to ensure a balanced evaluation. The proposed model with a 99.91% test accuracy, the suggested model was quite remarkable. Furthermore, guaranteeing strong classification performance across all disease categories, the model showed great precision (99.92%), recall (99.90%), and an F1-score of 99.91%. With few misclassifications, the confusion matrix verified almost flawless classification even further. These findings show how well deep learning can automate tomato disease diagnosis, therefore providing a scalable and quite accurate solution for smart agriculture. By means of early intervention and precision agriculture techniques, the suggested strategy has the potential to improve crop health monitoring, reduce economic losses, and encourage sustainable farming practices.

PMID:40263518 | DOI:10.1038/s41598-025-98015-x

Sci Rep. 2025 Apr 22;15(1):13830. doi: 10.1038/s41598-025-98571-2.

ABSTRACT

Driven by the rapid development of the Internet of Things (IoT), deploying deep learning models on resource-constrained hardware has become an increasingly critical challenge, which has propelled the emergence of TinyML as a viable solution. This study aims to deploy lightweight deep learning models for human activity recognition (HAR) using TinyML on edge devices. We designed and evaluated three models: a 2D Convolutional Neural Network (2D CNN), a 1D Convolutional Neural Network (1D CNN), and a DeepConv LSTM. Among these, the DeepConv LSTM outperformed existing lightweight models by effectively capturing both spatial and temporal features, achieving an accuracy of 98.24% and an F1 score of 98.23%. After performing full integer quantization on the best model, its size was reduced from 513.23 KB to 136.51 KB and was successfully deployed on the Arduino Nano 33 BLE Sense Rev2 using the Edge Impulse platform. The device's memory usage was 29.1 KB, flash usage was 189.6 KB, and the model's average inference time was 21 milliseconds, requiring approximately 0.01395 GOP, with a computational performance of around 0.664 GOPS. Even after quantization, the model maintained an accuracy of 97% and an F1 score of 97%, ensuring efficient utilization of computational resources. This deployment highlights the potential of TinyML in achieving low-latency and efficient HAR systems, making it suitable for real-time human activity recognition applications.

PMID:40263516 | DOI:10.1038/s41598-025-98571-2

Sci Rep. 2025 Apr 22;15(1):14000. doi: 10.1038/s41598-025-98269-5.

ABSTRACT

Habesha kemis, an Ethiopian attire traditionally donned by women belonging to the Habesha community, has undergone variations of designs over time. Initially, it comprised a lengthy dress with a fitted bodice and sleeves extending to the ankles. In the Amhara region, various ethnic groups such as Gojjam, Gondar, Shewa, Agew, and Wollo uphold their distinct cultural customs. While these Habesha garments may appear similar outwardly, their embroidered motifs exhibit unique patterns, shapes, and hues, symbolizing the rich cultural legacy of Gojjam, Gondar, Shewa, Agew, and Wollo. The study aimed to identify the most appropriate model for recognizing and classifying the quality of Habesha kemis embroidery design. Digital image processing methods and CNN models incorporating VGG16, VGG19, and ResNet50v2 classifiers were used. Following the gathering of datasets, image preprocessing and segmentation were employed to enhance the model's performance. In segmentation, we used canny edge detection, local binary pattern, and dilation with contour detection for segmenting and automatically cropping each habesha kemis. After applying the segmentation process, the individual habesha kemis and foreign matters are placed in a folder based on their corresponding categories. This resulted in 320 images before augmenting for each class amount representative. The performance of VGG16, VGG19, and ResNet50v2 for Agew, Gojjam, Gonder, Shewa, and Wollo was evaluated. This process resulted in an image size of 224 × 224 in the CNN model with a VGG16 architecture and a SoftMax classifier of course we try also 64 × 64 and 128 × 128. Augmentation techniques were applied to increase the dataset size from 1600 to 3,270. Finally, the model was evaluated and achieved an accuracy of 95.72% in test data and 99.62% in training data compared to the VGG19 and ResNet50v2 models.

PMID:40263488 | DOI:10.1038/s41598-025-98269-5

Sci Rep. 2025 Apr 22;15(1):13854. doi: 10.1038/s41598-025-98281-9.

ABSTRACT

With the rapid advancement of medical informatics, the accumulation of electronic medical records and clinical diagnostic data provides unprecedented opportunities for intelligent medical text classification. However, challenges such as class imbalance, semantic heterogeneity, and data sparsity limit the effectiveness of traditional classification models. In this study, we propose an enhanced medical text classification framework by integrating a self-attentive adversarial augmentation network (SAAN) for data augmentation and a disease-aware multi-task BERT (DMT-BERT) strategy. The proposed SAAN incorporates adversarial self-attention, improving the generation of high-quality minority class samples while mitigating noise. Furthermore, DMT-BERT simultaneously learns medical text representations and disease co-occurrence relationships, enhancing feature extraction from rare symptoms. Extensive experiments on the private clinical datasets and the public CCKS 2017 dataset demonstrate that our approach significantly outperforms baseline models, achieving the highest F1-score and ROC-AUC values. The proposed innovations address key limitations in medical text classification and contribute to the development of robust clinical decision-support systems.

PMID:40263477 | DOI:10.1038/s41598-025-98281-9

Sci Rep. 2025 Apr 22;15(1):13825. doi: 10.1038/s41598-025-96918-3.

ABSTRACT

Leukocytes or white blood cells plays an important role in protecting the body from various contagious diseases and infectious agents. Different conventional leukocyte analysis approaches often face several problems like inaccuracies, demanding the need for sophisticated approaches to improve diagnostic precision. Therefore, a holistic structure namely a novel Attention-based Dual Channel U-shaped Network (ADCU-Net) utilizing three datasets is introduced in this paper for effective leukocyte classification. The image quality is boosted in the preprocessing phase through noise reduction, contrast enhancement, and background removal, significantly improving clarity. Then, the Dung Beetle Optimization (DBO) algorithm enhanced with Levy flight optimization is implemented for effective image segmentation processes. A dung beetle with a levy flight strategy assists in streamlined exploration of the search space thereby the detection and delineation of specific regions within images are improved, which results in higher boundary detection accuracy. The evaluation of major quantitative measures such as standard deviation, mean and entropy is comprised in the feature extraction process which offers crucial insights into the structural characteristics of leukocytes. Finally, a novel ADCU-Net model is utilized for the effective classification process. This ADCU-Net model is particularly selected to effectively capture various features and preserve spatial data, achieving98.4% accuracy. Overall, this paper highlights the performance of integrated sophisticated deep-learning structures for accurate leukocyte classification and segmentation, enabling the path for improved diagnostic tools in clinical settings.

PMID:40263470 | DOI:10.1038/s41598-025-96918-3

Sci Data. 2025 Apr 22;12(1):678. doi: 10.1038/s41597-025-05021-9.

ABSTRACT

Accurate mapping of vegetation canopy height and biomass distribution is essential for effective forest monitoring, climate change mitigation, and sustainable forestry. Here we present high-resolution remote sensing-based canopy height (10 m resolution) and above ground biomass (AGB, 50 m resolution) maps for the forests of the Iberian Peninsula from 2017 to 2021, using a deep learning framework that integrates Sentinel-1, Sentinel-2, and LiDAR data. Two UNET models were developed: one trained on Airborne Laser Scanning (ALS) data (MAE: 1.22 m), while another using Global Ecosystem Dynamics Investigation (GEDI) footprints (MAE: 3.24 m). External validation with 6,308 Spanish National Forest Inventory (NFI) plots (2017-2019) confirmed canopy height reliability, showing MAEs of 2-3 m in tree-covered areas. AGB estimates were obtained through Random Forest models that linked UNET derived height predictions to NFI AGB data, achieves an MAE of ~29 Mg/ha. The creation of high-resolution maps of canopy height and biomass across various forest landscapes in the Iberian Peninsula provides a valuable new tool for environmental researchers, policy makers, and forest management professionals, offering detailed insights that can inform conservation strategies, carbon sequestration efforts, and sustainable forest management practices.

PMID:40263468 | DOI:10.1038/s41597-025-05021-9

Sci Rep. 2025 Apr 22;15(1):13914. doi: 10.1038/s41598-025-93975-6.

ABSTRACT

Accurate anatomical measurements of the eyelids are essential in periorbital plastic surgery for both disease treatment and procedural planning. Recent researches in eye diseases have adopted deep learning works to measure MRD. However, such works encounter challenges in practical implementation, and the model accuracy needs to be improved. Here, we have introduced a deep learning-based adaptive and automatic measurement (DeepAAM) by employing the U-Net architecture enhanced through attention mechanisms and multiple algorithms. DeepAAM enables adaptive image recognition and adjustment in practical application, and improves the measurement accuracy of Marginal Reflex Distance (MRD). Meanwhile, it for the first time measures the Margin Iris Intersectant Angle (MIA) as an innovative evaluation index. Besides, this fully automated method surpasses other models in terms of accuracy for iris and sclera segmentation. DeepAAM offers a novel, comprehensive, and objective approach to the quantification of ocular morphology.

PMID:40263452 | DOI:10.1038/s41598-025-93975-6

Gene Ther. 2025 Apr 22. doi: 10.1038/s41434-025-00536-7. Online ahead of print.

ABSTRACT

Developing new strategies for local monitoring and delivery of immunosuppression is critical to making allografts safer and more accessible. Ex vivo genetic modification of grafts using machine perfusion presents a promising approach to improve graft function and modulate immune responses while minimizing risks of off-target effects and systemic immunogenicity in vivo. This proof-of-concept study demonstrates the feasibility of using normothermic machine perfusion (NMP) to mimic in vitro conditions for effective gene delivery. In this study, lentiviral vectors encoding the secreted biomarker Gaussia Luciferase (GLuc) and red fluorescent protein (RFP) were introduced ex vivo to rodent livers during a 72-h machine perfusion protocol. After an initial 24-h exposure to viral vectors, the organs were maintained in perfusion for an additional 48 h to monitor gene expression, aligning with in vitro benchmarks. Control livers were perfused in similar fashion, but without viral injections. Virally perfused livers exhibited nearly a 10-fold increase in luminescence compared to controls (p < 0.0001), indicating successful genetic modification of the organs. These findings validate the use of machine perfusion systems and viral vectors to genetically engineer whole organs ex vivo, laying the groundwork for a broad range of applications in transplantation through genetic manipulation of organ systems. Future studies will focus on refining this technology to enhance precision in gene expression and explore its implications for clinical translation.

PMID:40263629 | DOI:10.1038/s41434-025-00536-7

EMBO J. 2025 Apr 22. doi: 10.1038/s44318-024-00354-4. Online ahead of print.

NO ABSTRACT

PMID:40263601 | DOI:10.1038/s44318-024-00354-4

Mol Syst Biol. 2025 Apr 22. doi: 10.1038/s44320-025-00100-w. Online ahead of print.

ABSTRACT

Type 2 diabetes (T2D) presents a global health concern, with evidence highlighting the role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in T2D. Glucose levels, diet, 16S sequences and metadata were collected for 1866 individuals. In addition, microbial community models, and ecological interactions were simulated for the gut microbiomes. Our findings revealed a significant decrease in metabolic fluxes provided by the host's diet to the microbiome in T2D patients, accompanied by increased within-community exchanges. Moreover, the diabetic microbiomes shift towards increased exploitative ecological interactions at the expense of collaborative interactions. The reduced microbiome-to-host butyrate flux, along with decreased fluxes of amino acids (including tryptophan), nucleotides, and B vitamins from the host's diet, further highlight the dysregulation in microbial-host interactions in diabetes. In addition, microbiomes of T2D patients exhibit enrichment in energy metabolism, indicative of increased metabolic activity and antagonism. This study sheds light on the increased microbiome autonomy and antagonism accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validation.

PMID:40263590 | DOI:10.1038/s44320-025-00100-w

Nat Plants. 2025 Apr 22. doi: 10.1038/s41477-025-01984-0. Online ahead of print.

ABSTRACT

Auxins are a group of phytohormones that control plant growth and development. Their crucial role in plant physiology has inspired development of potent synthetic auxins that can be used as herbicides. Phenoxyacetic acid derivatives are a widely used group of auxin herbicides in agriculture and research. Despite their prevalence, the identity of the transporters required for distribution of these herbicides in plants is both poorly understood and the subject of controversial debate. Here we show that PIN-FORMED auxin transporters transport a range of phenoxyacetic acid herbicides across the membrane. We go on to characterize the molecular determinants of substrate specificity using a variety of different substrates as well as protein mutagenesis to probe the binding site. Finally, we present cryogenic electron microscopy structures of Arabidopsis thaliana PIN8 bound to either 2,4-dichlorophenoxyacetic acid or 4-chlorophenoxyacetic acid. These structures represent five key states from the transport cycle, allowing us to describe conformational changes associated with the transport cycle. Overall, our results reveal that phenoxyacetic acid herbicides use the same export machinery as endogenous auxins and exemplify how transporter binding sites undergo transformations that dictate substrate specificity. These results provide a foundation for future development of novel synthetic auxins and for precision breeding of herbicide-resistant crop plants.

PMID:40263580 | DOI:10.1038/s41477-025-01984-0

Sci Rep. 2025 Apr 22;15(1):13986. doi: 10.1038/s41598-025-96783-0.

ABSTRACT

Baeyer-Villiger monooxygenases (BVMOs) are versatile biocatalysts that catalyse the oxidation of ketones to esters with high regio- and enantioselectivity, operating under mild reaction conditions while reducing hazardous waste. Some BVMOs can convert cellulose-derived alkyl levulinates to 3-acetoxypropionates (3-APs), which are key intermediates in the production of 3-hydroxypropionic acid (3-HP), a versatile building block chemical. In this study, a BVMO from Acinetobacter radioresistens (Ar-BVMO) was tested as a biocatalyst for the conversion of three marketed alkyl levulinates: methyl, ethyl and butyl levulinate. The enzyme showed 4-fold higher catalytic efficiency (kcat/KM) and enhanced regioselectivity for the desired 3-AP product (4:1 ratio) when using butyl levulinate as a substrate. Escherichia coli whole-cells over-expressing Ar-BVMO were exploited to increase the product yield, achieving 85% conversion in 9 h. To further improve the sustainability of this biotransformation, butyl levulinate was obtained via microwave-assisted alcoholysis of pulp, a renewable cellulose feedstock, achieving 92.7% selectivity. Despite challenges posed by poor solubility of the resulting mixture in aqueous environment, Ar-BVMO in cell lysates was able to fully convert butyl levulinate within 24 h, efficiently producing 3-HP precursors without additional purification steps. These findings highlight the feasibility of this chemoenzymatic approach to convert cellulose-based raw materials to platform chemicals.

PMID:40263398 | DOI:10.1038/s41598-025-96783-0

Sci Rep. 2025 Apr 22;15(1):13844. doi: 10.1038/s41598-025-97105-0.

ABSTRACT

Accurate species identification is essential in biology, ecology, medicine, and agriculture, yet traditional methods relying on morphological characteristics often fail due to phenotypic plasticity and cryptic species. These limitations are particularly pronounced in small organisms with minimal distinguishing features. DNA barcoding has become a popular alternative; however, it requires invasive tissue sampling, making it unsuitable for delicate or rare organisms like insects and spiders. To address this challenge, we propose a non-invasive molecular method using proteomic analysis focused on species-specific protein sequences in spider silk, offering a viable solution for species identification without harming specimens. We developed a universal silk-dissolving method, followed by sequence similarity analysis to classify species into those identifiable at the species level and those distinguishable only to a group of closely related species. A bioinformatics pipeline was established to analyze peptide sequences, achieving 96% accuracy across 15 spider species, even in the presence of contaminants. This technique complements DNA barcoding and can be extended to other organisms producing biological materials. It holds promise in pest management, medical diagnostics, and improving public health by enabling accurate species identification without invasive procedures.

PMID:40263346 | DOI:10.1038/s41598-025-97105-0

Nature. 2025 Apr 22. doi: 10.1038/s41586-025-09021-y. Online ahead of print.

ABSTRACT

Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes1-4 to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing3,5,6. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif7 (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.

PMID:40262634 | DOI:10.1038/s41586-025-09021-y

Cell Syst. 2025 Apr 15:101266. doi: 10.1016/j.cels.2025.101266. Online ahead of print.

ABSTRACT

Biological analyses conducted at the single-cell scale have revealed profound impacts of heterogeneity and plasticity of chromatin states and gene expression on physiology and cancer. Here, we developed Parallel-seq, a technology for simultaneously measuring chromatin accessibility and gene expression in the same single cells. By combining combinatorial cell indexing and droplet overloading, Parallel-seq generates high-quality data in an ultra-high-throughput fashion and at a cost two orders of magnitude lower than alternative technologies (10× Multiome and ISSAAC-seq). We applied Parallel-seq to 40 lung tumor and tumor-adjacent clinical samples and obtained over 200,000 high-quality joint scATAC-and-scRNA profiles. Leveraging this large dataset, we characterized copy-number variations (CNVs) and extrachromosomal circular DNA (eccDNA) heterogeneity in tumor cells, predicted hundreds of thousands of cell-type-specific regulatory events, and identified enhancer mutations affecting tumor progression. Our analyses highlight Parallel-seq's power in investigating epigenetic and genetic factors driving cancer development at the cell-type-specific level and its utility for revealing vulnerable therapeutic targets.

PMID:40262617 | DOI:10.1016/j.cels.2025.101266