NPJ Antimicrob Resist. 2025 Apr 12;3(1):27. doi: 10.1038/s44259-025-00093-4.

ABSTRACT

Two of the three most commonly used classes of antifungal drugs target the fungal membrane through perturbation of sterol biosynthesis or function. In addition to these triazole and polyene antifungals, recent research is identifying new antifungal molecules that perturb lipid biosynthesis and function. Here, we review fungal lipid biosynthesis pathways and their potential as targets for antifungal drug development. An emerging goal is discovering new molecules that potentiate contemporary antifungal drugs in part through perturbation of lipid form and function.

PMID:40221522 | DOI:10.1038/s44259-025-00093-4

BMC Genomics. 2025 Apr 12;26(1):367. doi: 10.1186/s12864-025-11534-9.

ABSTRACT

BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

RESULTS: In this study, we found that predicting pathogenicity for variants in disordered regions is less accurate than in ordered regions, particularly for mutations at the first N-Methionine site. Investigations into the efficacy of variant effect predictors on intrinsically disordered regions (IDRs) indicated that mutations in IDRs are predicted with lower sensitivity and the gap between sensitivity and specificity is largest in disordered regions, especially for AlphaMissense and VARITY.

CONCLUSIONS: The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.

PMID:40221640 | DOI:10.1186/s12864-025-11534-9

Sci Rep. 2025 Apr 12;15(1):12552. doi: 10.1038/s41598-025-97074-4.

ABSTRACT

Landslides are among the geological disasters that frequently occur worldwide and significantly restrict the sustainable development of society. Therefore, it is of great practical significance to perform landslide susceptibility assessment. In addressing issues such as limited training samples, inadequate utilization of spatially effective features, and high computational costs associated with existing methods, we propose a landslide susceptibility assessment method (DS-DRN), which uses a lightweight dense residual network with emphasis on deep spatial features. To minimize computational costs, we design a depthwise separable residual module that optimizes traditional convolution on residual branches into depthwise separable convolution. Additionally, to prevent vanishing gradient and improve the reuse rate of landslide feature information, dense connections are employed to construct a deep feature extraction module. Finally, the output of the model is fed into the Softmax classifier for landslide susceptibility prediction. Taking Ya'an City in Sichuan Province as the study area, we compare the proposed DS-DRN method with three widely used deep learning methods: CNN, CPCNN-RF, and U-net. Evaluating model accuracy and performance, the DS-DRN method exhibits the highest prediction accuracy while also saving computational costs. Therefore, the proposed model can better fit the complex nonlinear relationship in landslide susceptibility, effectively mine deep spatial features, and address the high computational costs associated with complex networks.

PMID:40221608 | DOI:10.1038/s41598-025-97074-4

Sci Rep. 2025 Apr 12;15(1):12661. doi: 10.1038/s41598-025-97331-6.

ABSTRACT

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

PMID:40221571 | DOI:10.1038/s41598-025-97331-6

Sci Rep. 2025 Apr 12;15(1):12576. doi: 10.1038/s41598-025-97946-9.

ABSTRACT

The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. The results indicate that: (1) In Yuzhong District, 58.9% of streets have a GVI within a low to moderate range, suggesting room for improvement. Higher GVI levels are generally associated with elevated Digital Elevation Models (DEM), while slope, aspect, and terrain undulation have relatively minor overall impacts on GVI. (2) The GVI is highest in the western regions and lowest in the eastern regions, with streets along the riversides exhibiting lower GVI levels. (3) GWR analysis reveals that road type and NDVI significantly influence the GVI. Higher DEM values promote increased GVI, whereas high road density suppresses it. (4) The interaction between influencing factors drives the differentiated distribution of GVI within the study area. The interaction effects between Road type, NDVI, and DEM are particularly notable among these.

PMID:40221555 | DOI:10.1038/s41598-025-97946-9

Ann Surg Oncol. 2025 Apr 12. doi: 10.1245/s10434-025-17228-6. Online ahead of print.

ABSTRACT

BACKGROUND: Bladder cancer is one of the top ten cancers globally, with its incidence steadily rising in China. Early detection and prognosis risk assessment play a crucial role in guiding subsequent treatment decisions for bladder cancer. However, traditional diagnostic methods such as bladder endoscopy, imaging, or pathology examinations heavily rely on the clinical expertise and experience of clinicians, exhibiting subjectivity and poor reproducibility.

MATERIALS AND METHODS: With the rise of artificial intelligence, novel approaches, particularly those employing deep learning technology, have shown significant advancements in clinical tasks related to bladder cancer, including tumor detection, molecular subtyping identification, tumor staging and grading, prognosis prediction, and recurrence assessment.

RESULTS: Artificial intelligence, with its robust data mining capabilities, enhances diagnostic efficiency and reproducibility when assisting clinicians in decision-making, thereby reducing the risks of misdiagnosis and underdiagnosis. This not only helps alleviate the current challenges of talent shortages and uneven distribution of medical resources but also fosters the development of precision medicine.

CONCLUSIONS: This study provides a comprehensive review of the latest research advances and prospects of artificial intelligence technology in the precise diagnosis and treatment of bladder cancer.

PMID:40221553 | DOI:10.1245/s10434-025-17228-6

Sci Rep. 2025 Apr 12;15(1):12659. doi: 10.1038/s41598-025-97159-0.

ABSTRACT

In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images distributed across 9 disease classes, including Black spot, Canker, Greening, Scab, Melanose, and healthy examples of fruits and leaves. Both InceptionV3 and DenseNet121 achieved a test accuracy of 99.12%, with a macro average F1-score of approximately 0.986 and a weighted average F1-score of 0.991, indicating exceptional performance in terms of precision and recall across the majority of the classes. ResNet50 and EfficientNetB0 attained test accuracies of 84.58% and 80.18%, respectively, reflecting moderate performance in comparison. These research results underscore the promise of modern convolutional neural networks for accurate and timely detection of citrus diseases, thereby providing effective tools for farmers and agricultural professionals to implement proactive disease management, reduce crop losses, and improve yield quality.

PMID:40221550 | DOI:10.1038/s41598-025-97159-0

Sci Rep. 2025 Apr 12;15(1):12548. doi: 10.1038/s41598-025-85815-4.

ABSTRACT

Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease. With the growth of technology, patient-related information is attained rapidly and in big sizes. Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. The main aim of the CADHDD-SFLNHM approach is to enhance the detection and classification of haematologic disorders. At first, the Sobel filter (SF) technique is utilized for preprocessing to improve the quality of blood cell images. Additionally, the modified LeNet-5 model is used in the feature extractor process to capture the essential characteristics of blood cells relevant to disorder classification. The convolutional neural network and bi-directional gated recurrent unit with attention (CNN-BiGRU-A) method is employed to classify and detect haematologic disorders. Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. The experimental result analysis of the CADHDD-SFLNHM model was accomplished using a benchmark database. The performance validation of the CADHDD-SFLNHM model portrayed a superior accuracy value of 97.91% over other techniques.

PMID:40221445 | DOI:10.1038/s41598-025-85815-4

Sci Rep. 2025 Apr 12;15(1):12647. doi: 10.1038/s41598-025-93549-6.

ABSTRACT

A distributed system comprises several independent units, each planned to track its tasks without interconnecting with the rest of them, excluding messaging services. This indicates that a solitary point of failure can reduce a method incapable without caution since no single point can achieve all essential processes. Malicious node recognition is a crucial feature of safeguarding the safety and reliability of distributed methods. Numerous models, ranging from anomaly recognition techniques to machine learning (ML) methods, are used to examine node behaviour and recognize deviances from usual patterns that may designate malicious intent. Advanced cryptographic protocols and intrusion detection devices are often combined to improve the flexibility of these methods against attacks. Moreover, real-time observing and adaptive plans are vital in quickly identifying and answering emerging attacks, contributing to the complete sturdiness of safe distributed methods. This study designs a Hybrid Bald Eagle-Crow Search Algorithm and Deep Learning for Enhanced Malicious Node Detection (HBECSA-DLMND) technique in Secure Distributed Systems. The HBECSA-DLMND technique follows the concept of metaheuristic feature selection with DL-based detection of malicious nodes in distributed systems. To accomplish this, the HBECSA-DLMND technique performs data normalization using the linear scaling normalization (LSN) approach, and the ADASYN approach is employed to handle class imbalance data. Besides, the HBECSA-DLMND method utilizes the HBECSA technique to choose a better subset of features. Meanwhile, the convolutional sparse autoencoder (CSAE) model detects malicious nodes. Finally, the dung beetle optimization (DBO) method is employed for the parameter range of the CSAE method. The experimental evaluation of the HBECSA-DLMND methodology is examined on a benchmark WSN-DS database. The performance validation of the HBECSA-DLMND methodology illustrated a superior accuracy value of 98.99% over existing approaches.

PMID:40221436 | DOI:10.1038/s41598-025-93549-6

Sci Rep. 2025 Apr 12;15(1):12631. doi: 10.1038/s41598-025-92429-3.

ABSTRACT

As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments' findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development.

PMID:40221419 | DOI:10.1038/s41598-025-92429-3

Sci Data. 2025 Apr 12;12(1):615. doi: 10.1038/s41597-025-04467-1.

ABSTRACT

Inter-modal image registration (IMIR) and image segmentation with abdominal Ultrasound (US) data have many important clinical applications, including image-guided surgery, automatic organ measurement, and robotic navigation. However, research is severely limited by the lack of public datasets. We propose TRUSTED (the Tridimensional Renal Ultra Sound TomodEnsitometrie Dataset), comprising paired transabdominal 3DUS and CT kidney images from 48 human patients (96 kidneys), including segmentation, and anatomical landmark annotations by two experienced radiographers. Inter-rater segmentation agreement was over 93% (Dice score), and gold-standard segmentations were generated using the STAPLE algorithm. Seven anatomical landmarks were annotated, for IMIR systems development and evaluation. To validate the dataset's utility, 4 competitive Deep-Learning models for kidney segmentation were benchmarked, yielding average DICE scores from 79.63% to 90.09% for CT, and 70.51% to 80.70% for US images. Four IMIR methods were benchmarked, and Coherent Point Drift performed best with an average Target Registration Error of 4.47 mm and Dice score of 84.10%. The TRUSTED dataset may be used freely to develop and validate segmentation and IMIR methods.

PMID:40221416 | DOI:10.1038/s41597-025-04467-1

Cell Death Discov. 2025 Apr 12;11(1):168. doi: 10.1038/s41420-025-02444-6.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and chronic interstitial lung disease with unclear underlying pathogenic mechanisms. Dysbiosis of the lung microbiota is believed to be associated with the development of fibrosis; however, the roles of the microbiome in the respiratory functions of hosts with IPF remain poorly understood. To investigate the relationship between the lung microbiome and the pathological processes of idiopathic pulmonary fibrosis under laboratory conditions, C57BL/6 J mice were exposed to bleomycin and observed at 7, 14, 21, and 28 days post-exposure. 16S rDNA analysis revealed that the lung microbial community exhibited dysbiosis in the bleomycin-induced pulmonary fibrosis model, characterized by an abnormally high proportion of Klebsiella quasipneumoniae (K. quasipneumoniae), as confirmed by RNA fluorescence in situ hybridization. Throughout the progression of experimental pulmonary fibrosis, Tax4Fun analysis indicated that the abundance of K. quasipneumoniae differed significantly between model mice and control mice, correlating with the sustained activation of reactive oxygen species (ROS) pathways. Importantly, the dysbiosis of K. quasipneumoniae may serve as a critical factor triggering increased ROS levels, accompanied by macrophage mitophagy, ultimately leading to the overexpression of TGF-β1, a key player in the pathogenesis of pulmonary fibrosis. These findings suggest that lung microbiota dysbiosis exacerbates the progression of bleomycin-induced pulmonary fibrosis related to macrophage mitophagy.

PMID:40221415 | DOI:10.1038/s41420-025-02444-6

Stem Cell Res Ther. 2025 Apr 12;16(1):174. doi: 10.1186/s13287-025-04282-w.

ABSTRACT

BACKGROUND: The kidney's endocrine function is essential for maintaining body homeostasis. Erythropoietin (EPO) is one of the key endocrine factors produced by the kidney, and kidney disease patients frequently experience anemia due to impaired EPO production. In the present study we explored the potential of human induced pluripotent stem cell (iPSC)-derived kidney organoids to restore EPO production.

METHODS: EPO secretion by kidney organoids was examined under 1% and 20% oxygen levels. To increase the EPO secreting capacity of kidney organoids, iPSC were genetically engineered with a non-integrating scaffold/matrix attachment region (S/MAR) DNA vector containing the EPO gene and generated EPO-overexpressing (EPO+) kidney organoids. To assess the physiological effects of EPO + organoids, 2-8 organoids were implanted subcutaneously in immunodeficient mice.

RESULTS: Kidney organoids produced low amounts of EPO under 1% oxygen. EPO S/MAR DNA vectors persisted and continued to robustly express EPO during iPSC expansion and kidney organoid differentiation without interfering with cellular proliferation. EPO + iPSC demonstrated efficient differentiation into kidney organoids. One-month post-implantation, EPO + organoids displayed continuously elevated EPO mRNA levels and significantly increased endothelial cell numbers compared to control organoids. Hematocrit levels were notably elevated in mice implanted with EPO + organoids in an organoid number-dependent manner. EPO + organoids furthermore influenced bone homeostasis in their hosts, evidenced by a change in trabecular bone composition.

CONCLUSION: Kidney organoids modified by EPO S/MAR DNA vector allow stable long-term delivery of EPO. The observed physiological effects following the implantation of EPO + organoids underscore the potential of gene-edited kidney organoids for endocrine restoration therapy.

PMID:40221815 | DOI:10.1186/s13287-025-04282-w

BMC Genomics. 2025 Apr 12;26(1):367. doi: 10.1186/s12864-025-11534-9.

ABSTRACT

BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

RESULTS: In this study, we found that predicting pathogenicity for variants in disordered regions is less accurate than in ordered regions, particularly for mutations at the first N-Methionine site. Investigations into the efficacy of variant effect predictors on intrinsically disordered regions (IDRs) indicated that mutations in IDRs are predicted with lower sensitivity and the gap between sensitivity and specificity is largest in disordered regions, especially for AlphaMissense and VARITY.

CONCLUSIONS: The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.

PMID:40221640 | DOI:10.1186/s12864-025-11534-9

Trends Endocrinol Metab. 2025 Apr 11:S1043-2760(25)00054-2. doi: 10.1016/j.tem.2025.03.009. Online ahead of print.

ABSTRACT

Pyruvate kinase is a key regulator in hepatic glucose metabolism, encoded by the gene pyruvate kinase liver/red blood cells (PKLR). Systems biology-based approaches, including metabolic and gene co-expression networks analyses, as well as genome-wide association studies (GWAS), have led to the identification of PKLR as a pivotal gene influencing liver metabolism in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and hepatocellular carcinoma (HCC). Here, we review the critical role of PKLR in MASLD and HCC progression and examine the effects of PKLR modulation both in vitro and in vivo. We also discuss the development of therapeutic strategies for patients with MASLD and HCC by modulating PKLR, highlighting its promising future in a broader range of liver diseases.

PMID:40221236 | DOI:10.1016/j.tem.2025.03.009

Infect Genet Evol. 2025 Apr 10:105751. doi: 10.1016/j.meegid.2025.105751. Online ahead of print.

ABSTRACT

BACKGROUND: Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protein-protein interactions (PPIs), is crucial for developing therapeutic strategies. While experimental approaches are essential, they are often labor-intensive and costly. Researchers have been able to predict HPIs more efficiently due to recent advances in artificial intelligence and machine learning. However, existing reviews lack a systematic evaluation of different machine learning methodologies and their effectiveness.

METHODS: This scoping review critically examines recent studies on machine learning-based Host-Pathogen Interaction (HPI) prediction, categorizing them by host and pathogen types, machine learning algorithms, and key evaluation metrics. The methodology is based on the study beginning with a preliminary search in reputable using key phrases related to host-pathogen interactions from 2019 to 2024. This process yielded 46 relevant articles, from which 30 were selected for review after evaluating titles and abstracts.

RESULTS: Our findings indicate that tree-based algorithms, particularly Random Forest and Gradient Boosting, are the most prevalent in Host-Pathogen Interaction (HPI) prediction. The filter articles were categorized by host and pathogen type and further subdivided into four subcategories based on the prediction type and machine learning algorithms: classic, tree-based, vector-based, and neural network algorithms. Convolutional and recurrent neural networks are among the deep learning models that demonstrate promising accuracy, but they require a lot of labeled data for effective training. Additionally, the analysis uncovers significant gaps in dataset standardization and model interpretability, which pose challenges to the broader applicability of these predictive models.

CONCLUSION: In this review, we emphasize the potential of machine learning in HPI prediction and highlight the important challenges that must be addressed to improve predictive accuracy. Unlike previous reviews, our study systematically compares different computational approaches, offering a roadmap for future research. The findings emphasize the importance of dataset quality, feature selection, and model transparency in advancing AI-driven pathogen research.

PMID:40220943 | DOI:10.1016/j.meegid.2025.105751

Cancer Lett. 2025 Apr 10:217715. doi: 10.1016/j.canlet.2025.217715. Online ahead of print.

ABSTRACT

Esophageal squamous cell carcinoma (ESCC) stands out as a common cancer type worldwide, characterized by its notably high rates of occurrence and mortality. The epidermal growth factor receptor (EGFR) is one of the main targets for cancer treatment as it is one of the genes whose expression is often altered by overexpression, amplification, and mutation in a variety of solid tumors. Substantial efforts have been made to develop EGFR-targeted therapeutic agents, including monoclonal antibodies and tyrosine kinase inhibitors (TKIs). However, these agents exhibited limited efficacy due to the emergence of acquired resistance. Therefore, novel treatment strategies targeting EGFR are urgently needed. Recent studies have identified a few natural compounds that can efficiently inhibit EGFR, indicating that natural products may be potential sources for the development of new EGFR inhibitors. Here, using the Drug Affinity Responsive Target Stability (DARTS) assay combined with liquid chromatography/tandem mass spectrometry analysis, co-crystal method, we discovered that Bufalin directly interacts with EGFR and causes EGFR endocytosis and degradation in the lysosome. Moreover, Bufalin exhibits superior anti-tumor activity compared with another EGFR TKIs. Our study identified Bufalin as the first natural small-molecule EGFR degrader, which suppresses EGFR signaling by inducing the degradation of EGFR via the endosome-lysosome pathway.

PMID:40220852 | DOI:10.1016/j.canlet.2025.217715

Cell Metab. 2025 Apr 8:S1550-4131(25)00116-0. doi: 10.1016/j.cmet.2025.03.013. Online ahead of print.

ABSTRACT

Polycystic ovary syndrome (PCOS), the most common endocrinopathy in women, causes significant reproductive and metabolic comorbidities, with no current cure. Gestational androgen and anti-Müllerian hormone (AMH) excess are linked to PCOS, and prenatal aberrant exposure to these hormones induces PCOS-like traits in animal models. However, whether the AMH effects on PCOS programming could extend to early postnatal life remains unknown. Clinical observations showed higher AMH levels during minipuberty in infants of mothers with PCOS, but whether this contributes to PCOS development is uncertain. Here, we show that exposure to high AMH levels during minipuberty in mice causes PCOS-like reproductive and metabolic defects in both sexes. A neutralizing antibody targeting AMH receptor 2 (AMHR2) prevented these defects when administered during minipuberty and alleviated symptoms when given in adulthood. These findings highlight the causal role of elevated AMH in PCOS and suggest AMHR2-targeting therapy as a potential preventive or curative approach.

PMID:40220763 | DOI:10.1016/j.cmet.2025.03.013

Cell Syst. 2025 Apr 9:101261. doi: 10.1016/j.cels.2025.101261. Online ahead of print.

ABSTRACT

Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40220761 | DOI:10.1016/j.cels.2025.101261

Cell Rep Methods. 2025 Apr 8:101026. doi: 10.1016/j.crmeth.2025.101026. Online ahead of print.

ABSTRACT

We present hypergraph variational autoencoder (HyperG-VAE), a Bayesian deep generative model that leverages hypergraph representation to model single-cell RNA sequencing (scRNA-seq) data. The model features a cell encoder with a structural equation model to account for cellular heterogeneity and construct gene regulatory networks (GRNs) alongside a gene encoder using hypergraph self-attention to identify gene modules. The synergistic optimization of encoders via a decoder improves GRN inference, single-cell clustering, and data visualization, as validated by benchmarks. HyperG-VAE effectively uncovers gene regulation patterns and demonstrates robustness in downstream analyses, as shown in B cell development data from bone marrow. Gene set enrichment analysis of overlapping genes in predicted GRNs confirms the gene encoder's role in refining GRN inference. Offering an efficient solution for scRNA-seq analysis and GRN construction, HyperG-VAE also holds the potential for extending GRN modeling to temporal and multimodal single-cell omics.

PMID:40220759 | DOI:10.1016/j.crmeth.2025.101026