Arch Bronconeumol. 2025 Apr 28:S0300-2896(25)00140-1. doi: 10.1016/j.arbres.2025.04.004. Online ahead of print.

NO ABSTRACT

PMID:40345955 | DOI:10.1016/j.arbres.2025.04.004

Ocul Surf. 2025 May 7:S1542-0124(25)00066-7. doi: 10.1016/j.jtos.2025.05.005. Online ahead of print.

ABSTRACT

The tear film is a complex structure with rich interactions with the human body. A growing body of evidence suggests that measuring changes in protein, lipid, or other metabolite concentration in the tear film can be used to help detect disease. Particularly in the era of precision medicine, the tear film serves as a promising source of non-invasive insights into systemic health for early diagnosis and treatment. This paper analyzes the latest research in tear film biomarkers for systemic diseases. The review was conducted through PubMed and Embase databases using the PRISMA protocol and includes 54 articles. This paper first reviews the anatomy and physiology of tear film, as well as the latest proteomic analysis techniques on the tear film. We then provide a disease-by-disease review on the tear film as a biomarker including 5 articles related to Alzheimer's Disease, 10 articles related to Cancers, 1 article related to Cystic Fibrosis, 1 article related to Migraines, 4 articles related to Multiple Sclerosis, 15 articles related to Parkinson's Disease, 7 articles related to Rheumatoid Arthritis, and 11 articles related to Thyroid Disease. This paper highlights the promising results of these studies yet also reviews the challenges with limited sample sizes, reproducibility, and biological understanding of biomarkers. We conclude this paper with insights for future work to ensure clinical validity and generalizability. Ultimately, the tear film is a clinically accessible, complex structure that provides a wealth of information that may contribute to a more comprehensive understanding of systemic health.

PMID:40345388 | DOI:10.1016/j.jtos.2025.05.005

Int J Neural Syst. 2025 May 9:2550037. doi: 10.1142/S0129065725500376. Online ahead of print.

ABSTRACT

Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing the complex dynamics of Electroencephalogram (EEG) signals and generalizing across diverse patient populations. In this study, the graph attention network and the transformer model are integrated for EEG signal classification, leveraging the enhanced capability to dynamically compute attention weights and adapt to the variable relevance of brain regions. The proposed approach is capable of modeling the intricate relationships within EEG activities by learning context-dependent attention scores. We conducted a comprehensive evaluation of the proposed approach comparing with the state-of-the-art algorithms. Experimental outcomes show that it surpasses the competing models. The superior performance is attributed to the proposed approach's dynamic attention mechanism, which better captures the nuanced patterns in EEG signals across different subjects and seizure types. In the experiments, the CHB-MIT dataset was exploited, which served as a benchmark for evaluating the performance of the proposed framework in distinguishing interictal, ictal, and normal EEG patterns. The results prove the usefulness of our work in advancing EEG signal classification. The findings suggest that the combination of graph attention and self-attention mechanisms is a promising approach for improving the accuracy and reliability of EEG-based diagnostics, potentially improving the management of neurological disorders.

PMID:40346731 | DOI:10.1142/S0129065725500376

BMC Biol. 2025 May 9;23(1):120. doi: 10.1186/s12915-025-02222-x.

ABSTRACT

BACKGROUND: Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to drug discovery, design, and reuse. Among these, the sequence-based approach using 1D sequences of drugs and targets as inputs typically results in the loss of structural information, whereas the structure-based method frequently results in increased computing costs due to the intricate structure of the molecule graph.

RESULTS: We propose a sequential multifeature fusion method (SMFF-DTA) to achieve efficient and accurate prediction. SMFF-DTA uses sequential methods to represent the structural information and physicochemical properties of drugs and targets and introduces multiple attention blocks to capture interaction features closely.

CONCLUSIONS: As demonstrated by our extensive studies, SMFF-DTA outperforms the other methods in terms of various metrics, showing its advantages and effectiveness as a drug-target binding affinity predictor.

PMID:40346536 | DOI:10.1186/s12915-025-02222-x

BMC Bioinformatics. 2025 May 9;26(1):125. doi: 10.1186/s12859-025-06136-x.

ABSTRACT

BACKGROUND: The classification of DNA sequences is pivotal in bioinformatics, essentially for genetic information analysis. Traditional alignment-based tools tend to have slow speed and low recall. Machine learning methods learn implicit patterns from data with encoding techniques such as k-mer counting and ordinal encoding, which fail to handle long sequences or sacrifice structural and sequential information. Frequency chaos game representation (FCGR) converts DNA sequences of arbitrary lengths into fixed-size images, breaking free from the constraints of sequence length while preserving more sequential information than other representations. However, existing works merely consider local information, ignoring long-range dependencies and global contextual information within FCGR image.

RESULTS: We propose PCVR, a Pre-trained Contextualized Visual Representation for DNA sequence classification. PCVR encodes FCGR with a vision transformer into contextualized features containing more global information. To meet the substantial data requirements of the training of vision transformer and learn more robust features, we pre-train the encoder with a masked autoencoder. Pre-trained PCVR exhibits impressive performance on three datasets even with only unsupervised learning. After fine-tuning, PCVR outperforms existing methods on superkingdom and phylum levels. Additionally, our ablation studies confirm the contribution of the vision transformer encoder and masked autoencoder pre-training to performance improvement.

CONCLUSIONS: PCVR significantly improves DNA sequence classification accuracy and shows strong potential for new species discovery due to its effective capture of global information and robustness. Codes for PCVR are available at https://github.com/jiaruizhou/PCVR .

PMID:40346458 | DOI:10.1186/s12859-025-06136-x

Insights Imaging. 2025 May 9;16(1):99. doi: 10.1186/s13244-025-01977-9.

ABSTRACT

OBJECTIVES: Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk.

METHODS: We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits.

RESULTS: The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for DM (p < 0.05).

CONCLUSIONS: The combined model demonstrated promising potential for accurately predicting the DM risk and stratifying the DMFS risk in RPS patients undergoing complete surgical resection, providing a valuable tool for guiding treatment decisions and follow-up strategies.

CRITICAL RELEVANCE STATEMENT: The intratumoral heterogeneity analysis facilitates the identification of high-risk retroperitoneal sarcoma patients prone to distant metastasis and poor prognoses, enabling the selection of candidates for more aggressive surgical and post-surgical interventions.

KEY POINTS: Preoperative identification of retroperitoneal sarcoma (RPS) with a high potential for distant metastasis (DM) is crucial for targeted interventional strategies. Quantitative assessment of intratumoral heterogeneity achieved reasonable performance for predicting DM. The integrated model combining clinicoradiological variables, ITH radiomics, and deep-learning features effectively predicted distant metastasis-free survival.

PMID:40346399 | DOI:10.1186/s13244-025-01977-9

EJNMMI Res. 2025 May 9;15(1):55. doi: 10.1186/s13550-025-01245-3.

ABSTRACT

BACKGROUND: We aimed to use deep learning (DL) techniques to accurately differentiate Parkinson's disease (PD) from multiple system atrophy (MSA), which share similar clinical presentations. In this retrospective analysis, 206 patients who underwent PET/MR imaging at the Chinese PLA General Hospital were included, having been clinically diagnosed with either PD or MSA; an additional 38 healthy volunteers served as normal controls (NC). All subjects were randomly assigned to the training and test sets at a ratio of 7:3. The input to the model consists of 10 two-dimensional (2D) slices in axial, coronal, and sagittal planes from multi-modal images. A modified Residual Block Network with 18 layers (ResNet18) was trained with different modal images, to classify PD, MSA, and NC. A four-fold cross-validation method was applied in the training set. Performance evaluations included accuracy, precision, recall, F1 score, Receiver operating characteristic (ROC), and area under the ROC curve (AUC).

RESULTS: Six single-modal models and seven multi-modal models were trained and tested. The PET models outperformed MRI models. The 11C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (11C-CFT) -Apparent Diffusion Coefficient (ADC) model showed the best classification, which resulted in 0.97 accuracy, 0.93 precision, 0.95 recall, 0.92 F1, and 0.96 AUC. In the test set, the accuracy, precision, recall, and F1 score of the CFT-ADC model were 0.70, 0.73, 0.93, and 0.82, respectively.

CONCLUSIONS: The proposed DL method shows potential as a high-performance assisting tool for the accurate diagnosis of PD and MSA. A multi-modal and multi-sequence model could further enhance the ability to classify PD.

PMID:40346391 | DOI:10.1186/s13550-025-01245-3

NPJ Digit Med. 2025 May 10;8(1):265. doi: 10.1038/s41746-025-01693-0.

ABSTRACT

Clinical validation studies are important to translate artificial intelligence (AI) technology in healthcare but may be underperformed in Otolaryngology - Head & Neck Surgery (OHNS). This scoping review examined deep learning publications in OHNS between 1996 and 2023. Searches on MEDLINE, EMBASE, and Web of Science databases identified 3236 articles of which 444 met inclusion criteria. Publications increased exponentially from 2012-2022 across 48 countries and were most concentrated in otology and neurotology (28%), most targeted extending health care provider capabilities (56%), and most used image input data (55%) and convolutional neural network models (63%). Strikingly, nearly all studies (99.3%) were in silico, proof of concept early-stage studies. Three (0.7%) studies conducted offline validation and zero (0%) clinical validation, illuminating the "AI chasm" in OHNS. Recommendations to cross this chasm include focusing on low complexity and low risk tasks, adhering to reporting guidelines, and prioritizing clinical translation studies.

PMID:40346307 | DOI:10.1038/s41746-025-01693-0

Sci Rep. 2025 May 9;15(1):16191. doi: 10.1038/s41598-025-90249-z.

ABSTRACT

Wildlife biologists increasingly use camera traps for monitoring animal populations. However, manually sifting through the collected images is expensive and time-consuming. Current deep learning studies for camera trap images do not adequately tackle real-world challenges such as imbalances between animal and empty images, distinguishing similar species, and the impact of backgrounds on species identification, limiting the models' applicability in new locations. Here, we present a novel two-stage deep learning framework. First, we train a global deep-learning model using all animal species in the dataset. Then, an agglomerative clustering algorithm groups animals based on their appearance. Subsequently, we train a specialized deep-learning expert model for each animal group to detect similar features. This approach leverages Transfer Learning from the MegaDetectorV5 (YOLOv5 version) model, already pre-trained on various animal species and ecosystems. Our two-stage deep learning pipeline uses the global model to redirect images to the appropriate expert models for final classification. We validated this strategy using 1.3 million images from 91 camera traps encompassing 24 mammal species and used 120,000 images for testing, achieving an F1-Score of 96.2% using expert models for final classification. This method surpasses existing deep learning models, demonstrating improved precision and effectiveness in automated wildlife detection.

PMID:40346172 | DOI:10.1038/s41598-025-90249-z

Sci Rep. 2025 May 10;15(1):16275. doi: 10.1038/s41598-025-00607-0.

ABSTRACT

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems and cities. It has worldwide economic consequences. Climate change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With a focus on Al-Qassim Region, Saudi Arabia, the model assesses temperature, air temperature dew point, visibility distance, and atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) to reduce dataset imbalance. The CNN-GRU-LSTM model was compared to 5 classic regression models: DTR, RFR, ETR, BRR, and K-Nearest Neighbors. Five main measures were used to evaluate model performance: MSE, MAE, MedAE, RMSE, and R². After Min-Max normalization, the dataset was split into training (70%), validation (15%), and testing (15%) sets. The paper shows that the CNN-GRU-LSTM model beats standard regression methods in all four climatic scenarios, with R² values of 99.62%, 99.15%, 99.71%, and 99.60%. Deep learning predicts climate change well and can guide environmental policy and urban development decisions.

PMID:40346151 | DOI:10.1038/s41598-025-00607-0

Sci Rep. 2025 May 9;15(1):16273. doi: 10.1038/s41598-025-00996-2.

ABSTRACT

Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performance across species and habitats, especially in complex soundscapes. In this study, we explore the effectiveness of transfer learning in large-scale bird sound classification across various conditions, including single- and multi-label scenarios, and across different model architectures such as CNNs and Transformers. Our experiments demonstrate that both finetuning and knowledge distillation yield strong performance, with cross-distillation proving particularly effective in improving in-domain performance on Xeno-canto data. However, when generalizing to soundscapes, shallow finetuning exhibits superior performance compared to knowledge distillation, highlighting its robustness and constrained nature. Our study further investigates how to use multi-species labels, in cases where these are present but incomplete. We advocate for more comprehensive labeling practices within the animal sound community, including annotating background species and providing temporal details, to enhance the training of robust bird sound classifiers. These findings provide insights into the optimal reuse of pretrained models for advancing automatic bioacoustic recognition.

PMID:40346144 | DOI:10.1038/s41598-025-00996-2

Sci Rep. 2025 May 9;15(1):16239. doi: 10.1038/s41598-025-01270-1.

ABSTRACT

The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages I-II and III-IV of LSCC. Data from 401 histologically confirmed LSCC patients were collected from two centers (training set: 213; internal test set: 91; external test set: 97). Radiomics features were extracted from the MRI images, and seven radiomics models based on single and combined sequences were developed via random forest (RF). A DL model was constructed via ResNet 18, where DL features were extracted from its final fully connected layer. These features were fused with crucial radiomics features to create a combined model. The performance of the models was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with the radiologist performances. The predictive capability of the combined model for Progression-Free Survival (PFS) was evaluated via Kaplan-Meier survival analysis and the Harrell's Concordance Index (C-index). In the external test set, the combined model had an AUC of 0.877 (95% CI 0.807-0.946), outperforming the DL model (AUC: 0.811) and the optimal radiomics model (AUC: 0.835). The combined model significantly outperformed both the DL (p = 0.017) and the optimal radiomics models (p = 0.039), and the radiologists (both p < 0.050). Moreover, the combined model demonstrated great prognostic predictive value in patients with LSCC, achieving a C-index of 0.624 for PFS. This combined model enhances preoperative LSCC staging, aiding in making more informed clinical decisions.

PMID:40346120 | DOI:10.1038/s41598-025-01270-1

Int J Cancer. 2025 May 9. doi: 10.1002/ijc.35471. Online ahead of print.

ABSTRACT

Tumor-associated fibrosis contributes to an immunosuppressive microenvironment that hinders effective anti-tumor immune responses. This study investigates the potential of IOA-289, a novel autotaxin (ATX) inhibitor, which blocks lysophosphatidate (LPA) production and signaling, in modulating fibrosis in breast tumors. Bioinformatic analysis of human breast tumors revealed a strong correlation between levels of LPA1,-4 receptors and extracellular matrix (ECM) genes. Interaction of ECM molecules and integrin β1/CD44 between myofibroblasts and other cell types had the highest contribution to cell-cell communication. We showed that LPA induced α-smooth muscle actin mRNA in mouse mammary fibroblasts and increased expressions of collagen type-I α1 chain (COL1A1) and lamininγ1. IOA-289 decreased the expressions of COL1A1, fibronectin-1, and transforming growth factor β1 (TGFβ1) in E0771 breast tumors in mice. Masson's trichrome staining revealed a marked decrease in collagen deposition within breast tumors of IOA-289-treated mice. Decreased tumor fibrosis aligns with previous findings that IOA-289 enhanced the infiltration of CD8+ cytotoxic T cells and decreased fibrotic factors including leukemia inhibitory factor and transforming growth factor-beta1 in tumors. We also demonstrated that E0771 cells express negligible ATX and LPA receptors. Therefore, ATX inhibition did not affect cancer cells directly in our model. These results underscore the potential of ATX inhibitors in reprogramming the tumor microenvironment to favor anti-tumor immunity and attenuate fibrosis. ATX inhibitors are in clinical trials for treating idiopathic pulmonary fibrosis and pancreatic cancer. Our results support the development of ATX inhibitors as a strategy for improving the treatment of breast cancer and other diseases involving fibrosis.

PMID:40345856 | DOI:10.1002/ijc.35471

J Heart Lung Transplant. 2025 May 7:S1053-2498(25)01961-8. doi: 10.1016/j.healun.2025.04.022. Online ahead of print.

ABSTRACT

As we have previously shown, Idiopathic pulmonary fibrosis lung transplant recipients (IPF-LTRs) with short-telomere length (STL) are prone to develop significant cytopenias and poor tolerance to cell cycle inhibitors, specifically Mycophenolate mofetil (MMF), post-transplant. We investigated the use of Belatacept as an alternative immunosuppressive agent in a prospective, open-label cohort of 9 ST-IPF-LTRs at our institution. These patients were either challenged with MMF (majority) or immediately started on Belatacept post-transplant with the goal to bridge to Everolimus, an mTOR inhibitor that is commonly used post-transplant. We describe outcomes in the first-year post-transplant including the incidence of Acute Cellular Rejection (ACR), Epstein-Barr Virus (EBV) viremia, and one case of Post-Transplant Lymphoproliferative Disorder (PTLD) at 13 months. The use of Belatacept post-lung transplant may be an acceptable short-term alternative therapy to cell cycle inhibitors in ST-IPF-LTRs with cytopenias but may lead to higher risk of EBV viremia and PTLD when Belatacept is used long-term in these patients.

PMID:40345564 | DOI:10.1016/j.healun.2025.04.022

BMC Ecol Evol. 2025 May 9;25(1):44. doi: 10.1186/s12862-025-02382-y.

ABSTRACT

BACKGROUND: The evolution of differences in gamete size and number between sexes is a cornerstone of sexual selection theories. The green macroalga Ulva, with incipient anisogamy and parthenogenetic gametes, provides a unique system to investigate theoretical predictions regarding the evolutionary pressures that drive the transition from isogamy to anisogamy, particularly in relation to gamete size differentiation and sexual selection. Its minimal gamete dimorphism and facultative parthenogenesis enable a rare window into early evolutionary steps toward anisogamy.

RESULTS: By analyzing the expression profiles of sex-biased genes (SBGs) during gametogenesis, we found that SBGs evolve faster than unbiased genes, driven by higher rates of non-synonymous substitution (dN), indicating that SBGs are under stronger selective pressures. Mating type minus-biased genes (mt-BGs) exhibit higher dN/dS values than mating type plus-biased genes (mt+BGs), suggesting stronger selective pressures on mt-BGs, although this difference was not statistically significant (P = 0.08). Using branch-site and RELAX models, we found positive selection and relaxed purifying selection acting on a significant proportion of SBGs, particularly those associated with flagella function.

CONCLUSIONS: This study highlights the selective pressures shaping anisogamy and provides insights into the molecular mechanisms underlying its evolution. The faster evolution of SBGs, particularly mt-BGs, and the positive selection on genes associated with motility, such as those related to flagella function, suggest the importance of enhanced gamete motility in the transition to anisogamy. These findings contribute to our understanding of sexual selection and the evolutionary forces that drive the differentiation of gamete size and number between sexes.

PMID:40346481 | DOI:10.1186/s12862-025-02382-y

NPJ Digit Med. 2025 May 9;8(1):263. doi: 10.1038/s41746-025-01684-1.

ABSTRACT

Accurate medical decision-making is critical for both patients and clinicians. Patients often struggle to interpret their symptoms, determine their severity, and select the right specialist. Simultaneously, clinicians face challenges in integrating complex patient data to make timely, accurate diagnoses. Recent advances in large language models (LLMs) offer the potential to bridge this gap by supporting decision-making for both patients and healthcare providers. In this study, we benchmark multiple LLM versions and an LLM-based workflow incorporating retrieval-augmented generation (RAG) on a curated dataset of 2000 medical cases derived from the Medical Information Mart for Intensive Care database. Our findings show that these LLMs are capable of providing personalized insights into likely diagnoses, suggesting appropriate specialists, and assessing urgent care needs. These models may also support clinicians in refining diagnoses and decision-making, offering a promising approach to improving patient outcomes and streamlining healthcare delivery.

PMID:40346344 | DOI:10.1038/s41746-025-01684-1

Sci Rep. 2025 May 9;15(1):16263. doi: 10.1038/s41598-025-01067-2.

ABSTRACT

The basic forms of motor and possibly emotion replication include behavioral contagion and rapid motor mimicry (RMM). RMM-mainly demonstrated during play-occurs when an individual perceives and rapidly (< 1 s) replicates the exact motor sequence of another individual. We collected data on an African Savanna Elephant (Loxodonta africana; N = 15) group housed at the Parque de la Naturaleza de Cabárceno (Spain) on play target movements of both trunk and head. We demonstrated the presence of RMM. Elephants that were more prone in mimicking others' target movements were also more prone to play after observing others playing. RMM-as behavioral contagion-can enhance action coordination between players. As RMM was associated with more offensive play patterns than unreplicated target movements, RMM may allow competitive play sessions to occur, possibly replacing agonistic interactions. Neither individual (age, sex) nor social (affiliation levels) factors modulated the RMM. These findings can be related to the elephant high tolerance levels and the wide presence of play across age (including adults) and sex. Concluding, African elephants have the potential to share their affective states (emotional contagion) via RMM which is relevant to the investigation of the evolution of empathy in mammals including humans.

PMID:40346099 | DOI:10.1038/s41598-025-01067-2

Nat Commun. 2025 May 9;16(1):4330. doi: 10.1038/s41467-025-59164-9.

ABSTRACT

Cellular motion is a key feature of tissue morphogenesis and is often driven by migration. However, migration need not explain cell motion in contexts where there is little free space or no obvious substrate, such as those found during organogenesis of mesenchymal organs including the embryonic skull. Through ex vivo imaging, biophysical modeling, and perturbation experiments, we find that mechanical feedback between cell fate and stiffness drives bone expansion and controls bone size in vivo. This mechanical feedback system is sufficient to propagate a wave of differentiation that establishes a collagen gradient which we find sufficient to describe patterns of osteoblast motion. Our work provides a mechanism for coordinated motion that may not rely upon cell migration but on emergent properties of the mesenchymal collective. Identification of such alternative mechanisms of mechanochemical coupling between differentiation and morphogenesis will help in understanding how directed cellular motility arises in complex environments with inhomogeneous material properties.

PMID:40346043 | DOI:10.1038/s41467-025-59164-9

Immunity. 2025 May 6:S1074-7613(25)00178-5. doi: 10.1016/j.immuni.2025.04.015. Online ahead of print.

ABSTRACT

Recurrent waves of viral infection necessitate vaccines and therapeutics that remain effective against emerging viruses. Our ability to evaluate interventions is currently limited to assessments against past or circulating variants, which likely differ in their immune escape potential compared with future variants. To address this, we developed EVE-Vax, a computational method for designing antigens that foreshadow immune escape observed in future viral variants. We designed 83 SARS-CoV-2 spike proteins that transduced ACE2-positive cells and displayed neutralization resistance comparable to variants that emerged up to 12 months later in the COVID-19 pandemic. Designed spikes foretold antibody escape from B.1-BA.4/5 bivalent booster sera seen in later variants. The designed constructs also highlighted the increased neutralization breadth elicited by nanoparticle-based, compared with mRNA-based, boosters in non-human primates. Our approach offers targeted panels of synthetic proteins that map the immune landscape for early vaccine and therapeutic evaluation against future viral strains.

PMID:40345199 | DOI:10.1016/j.immuni.2025.04.015

Cell Rep Methods. 2025 Apr 30:101035. doi: 10.1016/j.crmeth.2025.101035. Online ahead of print.

ABSTRACT

Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis-regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples.

PMID:40345198 | DOI:10.1016/j.crmeth.2025.101035