Adv Mater. 2025 Apr 24:e2419383. doi: 10.1002/adma.202419383. Online ahead of print.

ABSTRACT

Snapshot spectral imaging is an emerging technology for fast data acquisition in dynamic environments, capturing high-volume spatial-spectral information in a single snapshot. However, it suffers from bulky cascading optics and cannot be directly used in space-restricted scenarios such as endoscope-assisted brain microsurgery and real-time cellular tissue imaging. In this work, an ultracompact strategy of parallelized metasurface computed tomography empowered by generative deep learning is proposed, which can effectively reduce the optics volume in snapshot spectral imaging from cm3 scale to sub-mm3 scale while retaining high resolution and speed of imaging so that the above-mentioned pain point problem is well addressed. The system comprises seven multifunctional sub-metasurfaces simultaneously acquiring multi-angle spectral projection and integration information of the target, uses the system-calibrated point spread functions as wavelength and spatial position distributions, and incorporates a generative adversarial deep neural network for fast reconstruction of spatial-spectral multiplexed images. Experimental results show that single snapshot imaging can be achieved in 38 ms with a spectral resolution of 10 nm in the spectral range of 450-650 nm. This technique paves the way for snapshot spectral imaging integration into various highly miniaturized microscopy and endoscopic imaging systems in applications such as advanced medical diagnosis.

PMID:40270309 | DOI:10.1002/adma.202419383

Adv Mater. 2025 Apr 24:e2419245. doi: 10.1002/adma.202419245. Online ahead of print.

ABSTRACT

The combination of artificial neural networks (ANN) and spiking neural networks (SNN) holds great promise for advancing artificial general intelligence (AGI). However, the reported ANN and SNN computational architectures are independent and require a large number of auxiliary circuits and external algorithms for fusion training. Here, a novel vertical bulk heterojunction neuromorphic transistor (VHNT) capable of emulating both ANN and SNN computational functions is presented. TaOx-based electrochemical reactions and PDVT-10/N2200-based bulk heterojunctions are used to realize spike coding and voltage coding, respectively. Notably, the device exhibits remarkable efficiency, consuming a mere 0.84 nJ of energy consumption for a single multiply accumulate (MAC) operation with excellent linearity. Moreover, the device can be switched to spiking neuron and self-activation neuron by simply changing the programming without auxiliary circuits. Finally, the VHNT-based artificial spiking neural network (ASNN) fusion simulation architecture is demonstrated, achieving 95% accuracy for Canadian-Institute-For-Advanced-ResearchResearch-10 (CIFARResearch-10) dataset while significantly enhancing training speed and efficiency. This work proposes a novel device strategy for developing high-performance, low-power, and environmentally adaptive AGI.

PMID:40270224 | DOI:10.1002/adma.202419245

Perioper Med (Lond). 2025 Apr 23;14(1):47. doi: 10.1186/s13741-025-00531-x.

ABSTRACT

BACKGROUND: Interest in artificial intelligence (AI) research in anesthesia is growing rapidly. However, there is a lack of bibliometric analysis to measure and analyze global scientific publications in this field. The aim of this study was to identify the hotspots and trends in AI research in anesthesia through bibliometric analysis.

METHODS: English articles and reviews published from 2000 to 2023 were retrieved from the Web of Science Core Collection (WoSCC) database. The extracted data were summarized and analyzed using Microsoft Excel, and bibliometric analysis were conducted with VOSviewer software.

RESULTS: AI research literature in anesthesia has exhibited rapid growth in recent years. The United States leads in the number of publications and citations, with Stanford University as the most prolific institution. Hyung-Chul Lee is the author with the highest number of publications. The journal Anesthesiology is highly recognized and authoritative in this field. Recent keywords include "musculoskeletal pain", "precision medicine", "stratification", "images", "mean arterial pressure", " enhanced recovery after surgery", "frailty", "telehealth", "postoperative delirium" and "postoperative mortality" indicating hot topics in AI research in anesthesia.

CONCLUSIONS: Publications on AI research in the field of anesthesia have experienced rapid growth over the past two decades and are likely to continue increasing. Research areas such as depth of anesthesia (DOA) and drug infusion (including electroencephalography and deep learning), perioperative risk assessment and prediction (covering mean arterial pressure, frailty, postoperative delirium, and mortality), image classification and recognition (for applications such as ultrasound-guided nerve blocks, vascular access, and difficult airway assessment), and perioperative pain management (particularly musculoskeletal pain) have garnered significant attention. Additionally, topics such as precision medicine, enhanced recovery after surgery, and telehealth are emerging as new hotspots and future directions in this field.

PMID:40270031 | DOI:10.1186/s13741-025-00531-x

NMR Biomed. 2025 Jun;38(6):e70036. doi: 10.1002/nbm.70036.

ABSTRACT

In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires an exceptional amount of skill because abnormalities on scans are frequently inconsistent with clinical symptoms, making it difficult to convert the findings into effective treatment strategies. Furthermore, MRI is an expensive process, and its frequent utilization to monitor an illness increases healthcare costs. To overcome these drawbacks, this research employs advanced technological approaches to develop a deep learning system for classifying types of MS through clinical brain MRI scans. The major innovation of this model is to influence the convolution network with attention concept and recurrent-based deep learning for classifying the disorder; this also proposes an optimization algorithm for tuning the parameter to enhance the performance. Initially, the total images as 3427 are collected from database, in which the collected samples are categorized for training and testing phase. Here, the segmentation is carried out by adaptive and attentive-based mask regional convolution neural network (AA-MRCNN). In this phase, the MRCNN's parameters are finely tuned with an enhanced pine cone optimization algorithm (EPCOA) to guarantee outstanding efficiency. Further, the segmented image is given to recurrent MobileNet with long short term memory (RM-LSTM) for getting the classification outcomes. Through experimental analysis, this deep learning model is acquired 95.4% for accuracy, 95.3% for sensitivity, and 95.4% for specificity. Hence, these results prove that it has high potential for appropriately classifying the sclerosis disorder.

PMID:40269999 | DOI:10.1002/nbm.70036

Breast Cancer Res. 2025 Apr 23;27(1):62. doi: 10.1186/s13058-025-02008-7.

ABSTRACT

BACKGROUND: Streamlining the clinical procedure of human epidermal growth factor receptor 2 (HER2) examination is challenging. Previous studies neglected the intra-class variability within both HER2-positive and -negative groups and lacked multi-cohort validation. To address this deficiency, this study collected data from multiple cohorts to develop a robust model for HER2 scoring utilizing only Hematoxylin&Eosin-stained whole slide images (WSIs).

METHODS: A total of 578 WSIs were collected from five cohorts, including three public and two private datasets. Each WSI underwent adaptive scale cropping. The transfer-learning-based probabilistic aggregation (TL-PA) model and multi-instance learning (MIL)-based models were compared, both of which were trained on Cohort A and validated on Cohorts B-D. The model demonstrating superior performance was further evaluated in the neoadjuvant therapy (NAT) cohort. Scoring performance was assessed using the area under the receiver operating characteristic curve (AUC). Correlation between the model scores and specific grades (HER2 levels, pathological complete response (pCR) status, residual cancer burden (RCB) grades) were evaluated using Spearman rank correlation and Dunn's test. Patch analysis was performed with manually defined features.

RESULTS: For HER2 scoring, the TL-PA significantly outperformed the MIL-based models, achieving robust AUCs in four validation cohorts (Cohort A: 0.75, Cohort B: 0.75, Cohort C: 0.77, Cohort D: 0.77). Correlation analysis confirmed a moderate association between model scores and manual reader-defined HER2-IHC status (Coefficient(Spearman) = 0.37, P(Spearman) = 0.001) as well as RCB grades (Coefficient(Spearman) = 0.45, P(Spearman) = 0.0006). In Cohort NAT, with the non-pCR as the positive control, the AUC was 0.77. Patch analysis revealed a core-to-peritumoral probability decrease pattern as malignancy spread outward from the lesion's core.

CONCLUSION: TL-PA shows robust generalization for HER2 scoring with minimal data; however, it still inadequately capture intra-class variability. This indicates that future deep-learning endeavors should incorporate more detailed annotations to better align the model's focus with the reasoning of pathologists.

PMID:40269991 | DOI:10.1186/s13058-025-02008-7

Mol Cancer. 2025 Apr 23;24(1):123. doi: 10.1186/s12943-025-02321-x.

ABSTRACT

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment.

PMID:40269930 | DOI:10.1186/s12943-025-02321-x

BMC Musculoskelet Disord. 2025 Apr 23;26(1):405. doi: 10.1186/s12891-025-08667-z.

ABSTRACT

BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, can reduce subjective influence and speed up processing. The TPR is a critical diagnostic parameter for developmental cervical spinal stenosis (DCSS), as it normalizes variations in radiographic magnification and provides a cost-effective alternative to CT/MRI in resource-limited settings. No study focusing on automatic measurement was reported. The aim was to develop a deep learning-based model for automatically measuring the TPR, and then to establish the distribution of asymptomatic Chinese TPR.

METHODS: A total of 1623 lateral cervical X-ray images from normal individuals were collected. 1466 and 157 images were used as the training dataset and testing dataset, respectively. We adopted a neural network called High-Resolution Vision Transformer (HRViT), which was trained on the annotated X-ray image dataset to automatically locate the landmarks and calculate the TPR. The accuracy of the TPR measurement was evaluated using mean absolute error (MAE), intra-class correlation coefficient (ICC), r value and Bland-Altman plot.

RESULTS: The TPR at C2-C7 was 1.26, 0.92, 0.90, 0.93, 0.92, and 0.89, respectively. The MAE between HRViT and surgeon R1 was 0.01, between surgeon R1 and surgeon R2 was 0.17, between surgeon R1 and surgeon R3 was 0.17. The accuracy of HRViT for DCSS diagnosis was 84.1%, which was greatly higher than those of both surgeon R2 (57.3%) and surgeon R3 (56.7%). The consistency of TPR measurements was 0.77-0.9 (ICC) and 0.78-0.9 (r value) between HRViT and surgeon R1.

CONCLUSIONS: We have explored a deep-learning algorithm for automated measurement of the TPR on cervical lateral radiographs to diagnose DCSS, which had outstanding performance comparable to clinical senior doctors.

PMID:40269821 | DOI:10.1186/s12891-025-08667-z

BMC Cardiovasc Disord. 2025 Apr 23;25(1):310. doi: 10.1186/s12872-025-04746-0.

ABSTRACT

INTRODUCTION: Percutaneous coronary intervention (PCI) has been the main treatment of coronary artery disease (CAD). In this review, we aimed to compare the performance of machine learning (ML) vs. logistic regression (LR) models in predicting different outcomes after PCI.

METHODS: Studies using ML or deep learning (DL) models to predict mortality, MACE, in-hospital bleeding, and acute kidney injury (AKI) after PCI or primary PCI were included. Articles were excluded if they did not provide a c-statistic, solely used ML models for feature selection, were not in English, or only used logistic or LASSO regression models. Best-performing ML and LR-based models (LR model or conventional risk score) from the same studies were pooled separately to directly compare the performance of ML versus LR. Risk of bias was assessed using the PROBAST and CHARMS checklists.

RESULTS: A total of 59 studies were included. Meta-analysis showed that ML models resulted in a higher c-statistic compared to LR in long-term mortality (0.84 vs. 0.79, P-value = 0.178), short-term mortality (0.91 vs. 0.85, P = 0.149), bleeding (0.81 vs. 0.77 P = 0.261), acute kidney injury (AKI; 0.81 vs. 0.75, P = 0.373), and major adverse cardiac events (MACE; 0.85 vs. 0.75, P = 0.406). PROBAST analysis showed that 93% of long-term mortality, 70% of short-term mortality, 89% of bleeding, 69% of AKI, and 86% of MACE studies had a high risk of bias.

CONCLUSION: No statistical significance existed between ML and LR model. In addition, the high risk of bias in ML studies and complexity in interpretation undermines their validity and may impact their adaption in a clinical settings.

PMID:40269704 | DOI:10.1186/s12872-025-04746-0

BMC Genomics. 2025 Apr 23;26(1):395. doi: 10.1186/s12864-025-11589-8.

ABSTRACT

BACKGROUND: Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission. A comprehensive risk assessment framework based on PB2 sequences is necessary, which should encompass detailed analyses of specific residues and mutations while maintaining sufficient generality for application to non-PB2 segments.

RESULTS: In this study, we developed two complementary approaches: a regression-based model for accurately distinguishing among risk groups, and a SHAP-based risk assessment model for more meaningful risk analyses. For the regression-based risk models, we compared various methodologies, including tree ensemble methods, conventional regression models, and deep learning architectures. The optimized regression model, combined with SHAP value analysis, identified and ranked individual residues contributing to zoonotic potential. The SHAP-based risk model enabled intra-class analyses within the zoonotic risk assessment framework and quantified risk yields from specific mutations.

CONCLUSION: Experimental analyses demonstrated that the Random Forest regression model outperformed other models in most cases, and we validated the target value settings for risk regression through ablation studies. Our SHAP-based analysis identified key residues (271A, 627K, 591R, 588A, 292I, 684S, 684A, 81M, 199S, and 368Q) and mutations (T271A, Q368R/K, E627K, Q591R, A588T/I/V, and I292V/T) critical for zoonotic risk assessment. Using the SHAP-based risk assessment model, we found that influenza A viruses from Phasianidae showed elevated zoonotic risk scores compared to those from other avian species. Additionally, mutations I292V/T, Q368R, A588T/I, V598A/I/T, and E/V627K were identified as significant mutations in the Phasianidae. These PB2-focused quantitative methods provide a robust and generalizable framework for both rapid screening of avians' zoonotic potential and analytical quantification of risks associated with specific residues or mutations.

PMID:40269678 | DOI:10.1186/s12864-025-11589-8

Network. 2025 Apr 23:1-39. doi: 10.1080/0954898X.2025.2492375. Online ahead of print.

ABSTRACT

The existing approaches suffer from scalability and security issues while transmitting data. Blockchain is a recently emerged technology, and it is an emerging platform that allows secure transmission. A distributed design is required to address these issues and abide by security regulations. Blockchain has been recently introduced as an alternative solution to solve complex and challenging security issues while storing data. Thus, an intelligent blockchain-assisted IoT architecture is provided in this work to perform secure healthcare data transmission. The first aim of our model is to detect malware attacks in IoT networks. To detect the malware activities, the attack detection data was gathered, and it was fed as input to the Hybrid Adaptive Deep Learning Method. For further enhancement, the FUPOA performs the parameter tuning. A privacy preservation model is employed to secure healthcare data by generating the optimal key formation, in which the key is optimized using FUPOA. This secured data can be stored in the blockchain to increase data integrity and privacy. The optimal feature selection is done by the FUPOA approach. Further, the acquired optimal features are fed to the HADL-AM for predicting the data. The experimental analysis has been done and compared among different approaches.

PMID:40269520 | DOI:10.1080/0954898X.2025.2492375

Respir Res. 2025 Apr 23;26(1):157. doi: 10.1186/s12931-025-03237-2.

ABSTRACT

BACKGROUND: Interstitial lung disease (ILD), represented by idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis (PPF), shows poor prognosis due to progressive fibrosis. Early therapeutic intervention is required to enhance the efficacy of antifibrotic drugs, highlighting the importance of early detection of ILD progression. Although candidate biomarkers for predicting ILD progression have been recently reported through omics analyses, clinically measurable biomarkers remain unestablished. This study aimed to identify clinically measurable biomarkers that could predict the degree of ILD progression.

METHODS: The serum levels of 13 candidate biomarkers were prospectively measured by chemiluminescent enzyme immunoassay and the utilities for predicting ILD progression were compared in the discovery cohort (total 252 patients). Moreover, we evaluated the utility of the identified biomarker in another independent cohort (154 patients with non-IPF-ILD) and examined the dynamics of the biomarker by immunoblotting and single-cell RNA sequencing (scRNA-seq) using samples of patients and a mouse model.

RESULTS: In the discovery cohort, C-C motif chemokine ligand (CCL)17 could reliably predict ILD progression, particularly in patients with ILD other than IPF, and showed significant associations with mortality (hazard ratio [HR] 3.70; 95% confidence interval [CI] 1.19-11.49; P = 0.015; cut-off value = 418 pg/mL). Consistently, in the validation cohort, the CCL17 high group showed significantly higher mortality (HR: 2.15; 95% CI 0.99-4.69; P = 0.049), and CCL17 was identified as an independent prognostic factor from corticosteroid or immunosuppressive agents use and ILD-gender-age-physiology index. Similar to the results of serum studies, CCL17 levels in the lungs of patients with PPF and model mice were higher than those in controls. They were positively correlated with CCL17 levels in the serum, suggesting that the increased serum CCL17 levels could reflect an increase in CCL17 levels in lung tissues. The scRNA-seq analysis of lung tissues from model mice suggested that the levels of CCL17 derived primarily from conventional dendritic cells and macrophages increased, especially during the profibrotic phase.

CONCLUSIONS: We identified serum CCL17 as a clinically measurable biomarker for predicting non-IPF-ILD progression. Serum CCL17 could enable the stratification of patients at risk of non-IPF-ILD progression, leading to appropriate early therapeutic intervention.

PMID:40269953 | DOI:10.1186/s12931-025-03237-2

J Diabetes. 2025 Apr;17(4):e70090. doi: 10.1111/1753-0407.70090.

ABSTRACT

AIM: Circulating Gremlin 2 (Grem2) has recently been linked to human obesity, but its role in type 2 diabetes (T2D) remains unclear. This study aims to explore the association of circulating Grem2 with β-cell function.

METHODS: A post hoc analysis was conducted using data from three clinical trials, in which all participants underwent the oral glucose tolerance test (OGTT). Circulating Grem2 levels were measured at 0, 1, and 2 h during the OGTT. In Trial 1, Grem2 levels were compared between participants with T2D (n = 59) and without T2D (n = 119). We further examined changes in Grem2 levels in response to oral antidiabetic drugs in participants with T2D in Trial 2 (n = 67) and calorie restriction in participants with prediabetes in Trial 3 (n = 231). The relationship between Grem2 levels and β-cell function was analyzed across all trials.

RESULTS: Fasting and 1-h Grem2 levels were lower in participants with T2D compared with those without T2D (728 ± 25 vs. 649 ± 31 pg/mL, p = 0.020; 631 ± 26 vs. 537 ± 31 pg/mL, p = 0.007). Fasting Grem2 levels were restored after antidiabetic treatment (550 ± 12 vs. 575 ± 12 pg/mL, p = 0.019), and 1-h Grem2 levels increased following calorie restriction (1118 ± 89 vs. 1144 ± 90 vs. 1253 ± 89 pg/mL, p for trend = 0.002). The 1-h Grem2 levels were positively associated with β-cell function assessed by the oral disposition index and HOMA-β.

CONCLUSION: Reduced circulating Grem2 levels are associated with impaired β-cell function in T2D, and could be restored through antidiabetic interventions.

TRIAL REGISTRATION: ClinicalTrials.gov: NCT01959984, NCT01758471, NCT03856762.

PMID:40270326 | DOI:10.1111/1753-0407.70090

Plant Physiol. 2025 Mar 28;197(4):kiaf107. doi: 10.1093/plphys/kiaf107.

ABSTRACT

Elevated temperatures resulting from climate change adversely affect natural and crop ecosystems, necessitating the development of heat-tolerant crops. Here, we established a framework to precisely identify protein phosphorylation sites associated with varying temperature sensitivities in wheat (Triticum aestivum). We identified specific kinases primarily associated with particular temperatures, but our results also suggest a striking overlap between cold and heat signaling. Furthermore, we propose that the phosphorylation state of a specific set of proteins may represent a signature for heat stress tolerance. These findings can potentially aid in the identification of targets for breeding or genome editing to enhance the sub- and supra-optimal temperature tolerance of crops.

PMID:40270188 | DOI:10.1093/plphys/kiaf107

Biol Sex Differ. 2025 Apr 23;16(1):28. doi: 10.1186/s13293-025-00707-6.

ABSTRACT

Gonadal testosterone stimulates skeletal muscle anabolism and contributes to sexually differentiated adipose distribution through incompletely understood mechanisms. Observations in humans and animal models have indicated a major role for androgen receptor (AR) in mediating sex differences in body composition throughout the lifespan. Traditional surgical, genetic and pharmacological studies have tested systemic actions of circulating androgens, and more recent transgenic approaches have allowed for tests of AR gene function in specific androgen responsive niches contributing to body composition, including: skeletal muscle and surrounding interstitial cells, white and brown adipose, as well as trabecular and cortical bone. Less well understood is how these functions of gonadal androgens interact with exercise. Here, we summarize the understood mechanisms of action of AR and its interactions with exercise, specifically on outcomes of body composition and muscle function, and the global- and tissue-specific role of AR in regulating skeletal muscle, adipose, and bone morphology. Additionally, we describe the known effects of androgen and AR manipulation on female body composition, muscle morphology, and sport performance, while highlighting a need for greater inclusion of female subjects in human and animal muscle physiology and endocrinology research.

PMID:40269952 | DOI:10.1186/s13293-025-00707-6

J Pept Sci. 2025 Jun;31(6):e70020. doi: 10.1002/psc.70020.

ABSTRACT

Peptide-based therapeutics have gained attention in cancer treatment because of their good specificity, low toxicity, and ability to modulate immune responses. However, challenges such as enzymatic degradation and poor bioavailability limit their clinical application. Peptide-functionalized poly(lactic-co-glycolic acid) (PLGA) systems have emerged as a transformative platform in cancer therapy that offers unique advantages, including enhanced stability, sustained release, and precise delivery of therapeutic agents. This review highlights the synergistic integration of peptides with PLGA and addresses key challenges of peptide-based therapeutics. The application of peptide-functionalized PLGA systems encompasses a diverse range of strategies for cancer therapy. In chemotherapy, peptides disrupt critical tumor pathways, induce apoptosis, and inhibit angiogenesis, demonstrating their versatility in targeting various aspects of tumor progression. In immunotherapy, peptides act as antigens to stimulate robust immune responses or as immune checkpoint inhibitors to restore T cell activity, overcoming tumor immune evasion. These systems also harness the enhanced permeability and retention effect, facilitating preferential accumulation in tumor tissues while leveraging tumor microenvironment (TME)-responsive mechanisms, such as pH-sensitive or enzyme-triggered drug release, to achieve controlled, localized delivery. Collectively, peptide-functionalized PLGA systems represent a promising, versatile approach for precise cancer therapy that integrates innovative delivery strategies with highly specific, potent therapeutic agents.

PMID:40269479 | DOI:10.1002/psc.70020

Environ Microbiol Rep. 2025 Apr;17(2):e70088. doi: 10.1111/1758-2229.70088.

ABSTRACT

The Amazon rainforest has been subjected to high rates of deforestation, mostly for pasturelands, over the last few decades. This change in plant cover is known to alter the soil microbiome and the functions it mediates, but the genomic changes underlying this response are still unresolved. In this study, we used a combination of deep shotgun metagenomics complemented by a supervised machine learning approach to compare the metabolic strategies of tropical soil microbial communities in pristine forests and long-term established pastures in the Amazon. Machine learning-derived metagenome analysis indicated that microbial community structures (bacteria, archaea and viruses) and the composition of protein-coding genes were distinct in each plant cover type environment. Forest and pasture soils had different genomic diversities for the above three taxonomic groups, characterised by their protein-coding genes. These differences in metagenome profiles in soils under forests and pastures suggest that metabolic strategies related to carbohydrate and energy metabolisms were altered at community level. Changes were also consistent with known modifications to the C and N cycles caused by long-term shifts in aboveground vegetation and were also associated with several soil physicochemical properties known to change with land use, such as the C/N ratio, soil temperature and exchangeable acidity. In addition, our analysis reveals that these alterations in land use can also result in changes to the composition and diversity of the soil DNA virome. Collectively, our study indicates that soil microbial communities shift their overall metabolic strategies, driven by genomic alterations observed in pristine forests and long-term established pastures with implications for the C and N cycles.

PMID:40269473 | DOI:10.1111/1758-2229.70088

Nat Plants. 2025 Apr 23. doi: 10.1038/s41477-025-01978-y. Online ahead of print.

ABSTRACT

Ferns are essential for understanding plant evolution; however, their large and intricate genomes have kept their genetic landscape largely unexplored, with only a few genomes sequenced and limited transcriptomic data available. To bridge this gap, we generated extensive RNA-sequencing data across various organs from 22 representative fern species, resulting in high-quality transcriptome assemblies. These data enabled us to construct a time-calibrated phylogeny for ferns, encompassing all major clades, which revealed numerous instances of whole-genome duplication. We highlighted the distinctiveness of fern genetics, discovering that half of the identified gene families are unique to ferns. Our exploration of fern cell walls through biochemical and immunological analyses uncovered the presence of the lignin syringyl unit, along with evidence of its independent evolution in ferns. Additionally, the identification of an unusual sugar in fern cell walls suggests a divergent evolutionary trajectory in cell wall biochemistry, probably influenced by gene duplication and sub-functionalization. To facilitate further research, we have developed an online database that includes preloaded genomic and transcriptomic data for ferns and other land plants. We used this database to demonstrate the independent evolution of lignocellulosic gene modules in ferns. Our findings provide a comprehensive framework illustrating the unique evolutionary journey ferns have undertaken since diverging from the last common ancestor of euphyllophytes more than 360 million years ago.

PMID:40269175 | DOI:10.1038/s41477-025-01978-y

NPJ Precis Oncol. 2025 Apr 24;9(1):117. doi: 10.1038/s41698-025-00907-8.

ABSTRACT

Triple-negative breast cancer (TNBC) accounts for approximately 15% of all Breast Cancer (BC) cases with poorer prognosis and clinical outcomes compared to other BC subtypes due to greater tumor heterogeneity and few therapeutically targetable oncogenic drivers. To reveal actionable pathways for anti-cancer treatment, we use a proteomic approach to quantitatively compare the abundances of 6306 proteins across 55 formalin-fixed and paraffin-embedded (FFPE) TNBC tumors. We identified four major TNBC clusters by unsupervised clustering analysis of protein abundances. Analyses of clinicopathological characteristics revealed associations between the proteomic profiles and clinical phenotypes exhibited by each subtype. We validate the findings by inferring immune and stromal cell type composition from genome-wide DNA methylation profiles. Finally, quantitative proteomics on TNBC cell lines was conducted to identify in vitro models for each subtype. Collectively, our data provide subtype-specific insights into molecular drivers, clinicopathological phenotypes, tumor microenvironment (TME) compositions, and potential pharmacologic vulnerabilities for further investigations.

PMID:40269124 | DOI:10.1038/s41698-025-00907-8

J Biol Rhythms. 2025 Apr 23:7487304251326628. doi: 10.1177/07487304251326628. Online ahead of print.

ABSTRACT

Circadian clocks regulate many aspects of human physiology, including cardiovascular function and drug metabolism. Administering drugs at optimal times of the day may enhance effectiveness and reduce side effects. Certain cardiac antiarrhythmic drugs have been withdrawn from the market due to unexpected proarrhythmic effects such as fatal Torsade de Pointes (TdP) ventricular tachycardia. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a recent global initiative to create guidelines for the assessment of drug-induced arrhythmias that recommends a central role for computational modeling of ion channels and in silico evaluation of compounds for TdP risk. We simulated circadian regulation of cardiac excitability and explored how dosing time of day affects TdP risk for 11 drugs previously classified into risk categories by CiPA. The model predicts that a high-risk drug taken at the most optimal time of day may actually be safer than a low-risk drug taken at the least optimal time of day. Based on these proof-of-concept results, we advocate for the incorporation of circadian clock modeling into the CiPA paradigm for assessing drug-induced TdP risk. Since cardiotoxicity is the leading cause of drug discontinuation, modeling cardiac-related chronopharmacology has significant potential to improve therapeutic outcomes.

PMID:40269490 | DOI:10.1177/07487304251326628

Sci Rep. 2025 Apr 23;15(1):13154. doi: 10.1038/s41598-025-95853-7.

ABSTRACT

In this study, we explore machine learning models for predicting emotional states in ridden horses. We manually label the images to train the models in a supervised manner. We perform data exploration and use different cropping methods, mainly based on Yolo and Faster R-CNN, to create two new datasets: 1) the cropped body, and 2) the cropped head dataset. We train various convolutional neural network (CNN) models on both cropped and uncropped datasets and compare their performance in emotion prediction of ridden horses. Despite the cropped head dataset lacking important regions like the tail (commonly annotated by experts), it yields the best results with an accuracy of 87%, precision of 79%, and recall of 97%. Furthermore, we update our models using various techniques, such as transfer learning and fine-tuning, to further improve their performance. Finally, we employ three interpretation methods to analyze the internal workings of our models, finding that LIME effectively identifies features similar to those used by experts for annotation.

PMID:40269006 | DOI:10.1038/s41598-025-95853-7