Npj Viruses. 2024 Nov 14;2(1):54. doi: 10.1038/s44298-024-00066-w.

ABSTRACT

Quasispecies theory revolutionized our understanding of viral evolution by describing viruses as dynamic populations of genetically diverse variants constantly adapting. This article explores the theory's role in virus-host interactions, including immune evasion, drug resistance, and viral emergence. We review the original model, recent advances, and key virus dynamics needing incorporation into quasispecies theory. We introduce the ultracube concept as a more realistic multidimensional sequence space to investigate virus evolutionary dynamics.

PMID:40295874 | DOI:10.1038/s44298-024-00066-w

Npj Viruses. 2024 Mar 25;2(1):16. doi: 10.1038/s44298-024-00026-4.

ABSTRACT

Influenza A(H7N9) virus showed high pathogenicity in humans when it emerged in 2013. Cigarette smoke (CS) causes pulmonary diseases including bronchitis, emphysema, and lung cancer. Although habitual smoking is thought to increase the risk of severe seasonal influenza virus infection, its effect on A(H7N9) virus infection is poorly understood. Here, we employed a mouse model of long-term exposure to CS to investigate the effect of CS on the pathogenicity of A(H7N9) virus infection. Unexpectedly, body weight loss for mice exposed to CS was milder than that for mock-treated mice upon A(H7N9) virus infection. CS exposure improved the survival rate of A(H7N9) virus-infected mice even though virus titers and pathological changes in the lungs were not significantly different between CS-exposed and control mice. Microarray analysis showed that CS-exposure activates cytokine/chemokine activity, immune response, and cell cycle activities that resemble reactivities against A(H7N9) virus infection. Therefore, under conditions where cytokine and chemokine expression in the lungs is already high due to CS exposure, the enhanced expression of cytokines and chemokines caused by A(H7N9) virus infection might be less harmful to the organs compared to the rapid increase in cytokine and chemokine expression in the air-exposed mice due to the infection. CS may thus induce immunoregulatory effects that attenuate severe pulmonary disease during A(H7N9) virus infection. However, these findings do not support CS exposure due to its many other proven negative health effects.

PMID:40295873 | DOI:10.1038/s44298-024-00026-4

Npj Viruses. 2025 Apr 4;3(1):25. doi: 10.1038/s44298-025-00107-y.

ABSTRACT

Foodborne viruses are significant public health threats, capable of causing life-threatening infections and posing major risks for future pandemics. However, the development of vaccines and treatments remains limited due to gaps in understanding their biophysical properties. Among these viruses, noroviruses are currently the leading cause of viral gastroenteritis globally and are responsible for numerous foodborne outbreaks. In this review, we explore the use of biophysical methods, with a focus on atomic force microscopy (AFM), to study foodborne viruses. We demonstrate how AFM can provide crucial insights into virus-host interactions, transmission dynamics, and environmental stability. We also show that the integration of various biophysical approaches offers new opportunities for advancing our understanding of foodborne viruses, ultimately guiding the development of effective prevention strategies and antiviral therapies.

PMID:40295860 | DOI:10.1038/s44298-025-00107-y

Npj Viruses. 2024 Apr 11;2(1):12. doi: 10.1038/s44298-024-00022-8.

ABSTRACT

Ebola virus (EBOV) is able to persist and actively replicate in the reproductive tract of male disease survivors months or years after recovery from Ebola virus disease (EVD)1. Persistent EBOV infections are usually asymptomatic and can be transmitted sexually, but the host and viral factors that mediate these infections have not been characterized2,3. We investigated the interaction between host and viral factors during EBOV infection of the blood testis barrier (BTB), with a focus on Sertoli cells as a potential reservoir for viral persistence. We assessed viral replication kinetics and host responses of mouse testicular Leydig cells and Sertoli cells infected with EBOV Makona (i.e. infectious EBOV) and collected samples up to 28 days post-infection. Viral replication was apparent in both cell lines, but intracellular early viral loads were much higher in Leydig cells compared to Sertoli cells. We used RNAseq analysis to characterize transcriptomic responses of Leydig cells and Sertoli cells to EBOV infection over time. Further investigation of early interactions between host cells and EBOV was performed using virus-like particles (EBOV trVLP) and assays of phosphorylation-based cell signaling. Our findings indicate that virus-treated Sertoli cells responded more rapidly and robustly than Leydig cells, and with a particular emphasis on detection of, and response to, external stimuli. We discuss how the roles played by Sertoli cells in immune privilege and spermatogenesis may affect their initial and continued response to EBOV infection in a manner that could facilitate asymptomatic persistence.

PMID:40295798 | DOI:10.1038/s44298-024-00022-8

Light Sci Appl. 2025 Apr 28;14(1):177. doi: 10.1038/s41377-025-01866-2.

ABSTRACT

This review examines imaging-based nanophotonic biosensing and interferometric label-free imaging, with a particular focus on vesicle detection. It specifically compares dielectric and plasmonic metasurfaces for label-free protein and extracellular vesicle detection, highlighting their respective advantages and limitations. Key topics include: (i) refractometric sensing principles using resonant dielectric and plasmonic surfaces; (ii) state-of-the-art developments in both plasmonic and dielectric nanostructured resonant surfaces; (iii) a detailed comparison of resonance characteristics, including amplitude, quality factor, and evanescent field enhancement; and (iv) the relationship between sensitivity, near-field enhancement, and analyte overlap in different sensing platforms. The review provides insights into the fundamental differences between plasmonic and dielectric platforms, discussing their fabrication, integration potential, and suitability for various analyte sizes. It aims to offer a unified, application-oriented perspective on the potential of these resonant surfaces for biosensing and imaging, aiming at addressing topics of interest for both photonics experts and potential users of these technologies.

PMID:40295495 | DOI:10.1038/s41377-025-01866-2

Ann Bot. 2025 Apr 29:mcaf078. doi: 10.1093/aob/mcaf078. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Flowering phenology has been suggested as an important factor to explain invasions of non-native plant species. Invasive species success may be enhanced by flowering at different times (the vacant niche hypothesis) or flowering for longer periods (the niche breath hypothesis) than native species. However, comprehensive regional assessments of the flowering phenology of invasive and native floras are lacking in the literature. In this study, we evaluated the flowering phenology of invasive and native plant species pools to test the above-mentioned hypotheses within a biogeographically meaningful region.

METHODS: We investigated the start, end, and length of flowering between the invasive and native floras that occur at the same elevation range in the Cantabrian Mixed Forests ecoregion (NW Iberian Peninsula), a biogeographical hotspot for invasive plants in SW Europe. We also accounted for species habitat preferences and climatic and biogeographic origin of the invasive species.

KEY RESULTS: We found a mismatch in flowering time between the ecoregional invasive and native floras. Invasive species had a delayed and longer flowering phenology compared to native species. These differences in flowering time were more pronounced in man-made habitats and in invaders from temperate and tropical regions.

CONCLUSIONS: Our results are consistent with the vacant niche hypothesis; the asynchrony in flowering time could allow invaders to exploit a temporally empty niche. Our results are also consistent with the niche breath hypothesis, suggesting that invasive species exhibit a longer flowering period than natives, which may allow them to have prolonged access to resources. Future studies should explore the phenological patterns of invasive and native species across biogeographically relevant regions to enhance our understanding of large-scale invasion events.

PMID:40295227 | DOI:10.1093/aob/mcaf078

Exp Neurol. 2025 Apr 26:115271. doi: 10.1016/j.expneurol.2025.115271. Online ahead of print.

ABSTRACT

Sodium-glucose cotransporter 2 inhibitor (SGLT2-i) and dipeptidyl peptidase-4 inhibitor (DPP4-i) are known to ameliorate Alzheimer's disease (AD)-like pathology and cognitive decline through distinct mechanisms. In this study, we investigated how these antidiabetic drugs elevate ketone levels and subsequently reduce amyloid-β (Aβ) and tau pathology via the NLR family pyrin domain containing 3 (NLRP3) inflammasome pathway in microglia, using a type 2 diabetes (T2D)-AD mouse model. Male C57BL/6 mice were fed a high-fat diet and injected with low doses of streptozotocin to establish a T2D-AD model. The mice were then treated with either SGLT2-i or DPP4-i. Our results revealed that both the inhibitors markedly enhanced brain ketone metabolism by upregulating key metabolic enzymes and transporters. They also reduced neuroinflammation by suppressing the expression of pro-inflammatory cytokines, such as IL-1β, and increasing the expression of the anti-inflammatory cytokine IL-4. A critical mechanism for this anti-inflammatory effect involved the inhibition of the expression of the NLRP3 inflammasome, a key driver of neuroinflammation. Notably, SGLT2-i appeared to inhibit NLRP3 inflammasome expression by disrupting the pTau-CX3C1 interaction, whereas DPP4-i exerted its effects through the Aβ-TLR4-NF-κB pathway. Moreover, our results showed that both the inhibitors promoted a shift in microglial activation from the pro-inflammatory M1 phenotype to the anti-inflammatory M2 phenotype, as indicated by the changes in CD206 and CD86 expression. These findings suggest that SGLT2-i and DPP4-i provide neuroprotective benefits through multiple mechanisms, including enhanced ketone metabolism, reduced neuroinflammation, and modulation of microglial activity in T2D-AD mouse model. This research offers a scientific basis for considering these inhibitors as potential therapeutic agents for neurodegenerative diseases, particularly in cognitive impairment patients with metabolic dysfunction.

PMID:40294740 | DOI:10.1016/j.expneurol.2025.115271

J Biol Chem. 2025 Apr 26:108553. doi: 10.1016/j.jbc.2025.108553. Online ahead of print.

ABSTRACT

FabZ, a β-hydroxyacyl-ACP dehydratase in the Type II fatty acid synthesis pathway, is essential for the viability of Pseudomonas aeruginosa by ensuring proper fatty acid elongation and membrane stability. However, the precise genetic interactions between fabZ and lipid A biosynthesis genes, such as lpxA and lpxC, as well as the potential existence of other suppressor genes of fabZ in P. aeruginosa, remain unclear. To explore these genetic interactions and identify potential suppressor genes, we constructed a conditional fabZ mutant, ΔfabZ(p_ts-fabZ), by deleting the chromosomal fabZ gene and complementing it with a temperature-sensitive plasmid-borne copy. The ΔfabZ(p_ts-fabZ) mutant exhibited lethality and cell morphology defects at a restrictive temperature, confirming its essentiality. Genetic interaction analyses revealed that deletion of lpxA or lpxC failed to rescue ΔfabZ(p_ts-fabZ) lethality at restrictive temperature. Through suppressor screening, we isolated a mutant strain capable of rescuing ΔfabZ lethality and identified lpxH as the suppressor gene using genome resequencing. Further analysis revealed that the fabZ and lpxH double mutant (ΔfabZΔlpxH) produced odd-chain fatty acids, identified as pentadecanoic acid (C15:0) and heptadecanoic acid (C17:0) through fatty acid methyl ester (FAME) analysis coupled with gas chromatography-mass spectrometry (GC-MS), and supplementation with these fatty acids restored the growth and morphology of ΔfabZ(p_ts-fabZ) and ΔlpxH(p_ts-lpxH) mutants at restrictive temperature, suggesting their critical role in membrane stability. These results indicate that deletion of lpxH serves as a genetic suppressor of ΔfabZ lethality, highlighting a previously unrecognized compensatory mechanism involving odd-chain fatty acid synthesis essential for membrane stability in P. aeruginosa.

PMID:40294648 | DOI:10.1016/j.jbc.2025.108553

JMIR Form Res. 2025 Apr 28;9:e59640. doi: 10.2196/59640.

ABSTRACT

BACKGROUND: Adolescents increasingly communicate through text-based messaging platforms such as SMS and social media messaging. These are now the dominant platforms for communication between adolescents, and adolescents use them to obtain emotional support from parents and other adults. The absence of nonverbal cues can make it challenging to communicate emotions on these platforms, however, so users rely on emojis to communicate sentiment or imbue messages with emotional tone. While research has investigated the functions of emojis in adult communication, less is known about adolescent emoji use.

OBJECTIVE: This study sought to understand whether the pragmatic functions of adolescent emoji use resemble those of adults, and to gain insight into the semantic meanings of emojis sent by adolescents.

METHODS: Web-based focus groups were conducted with a convenience sample of adolescents, in which participants responded to questions about their use and interpretation of emojis and engaged in unstructured interactions with one another. Two trained coders analyzed transcripts using a constant comparative coding procedure to identify themes in the discussion.

RESULTS: A total of 6 focus groups were conducted with 31 adolescent participants (mean age 16.2, SD 1.5 years). Discussion in the groups generally fell into 4 themes: emojis as humorous or absurd, emokis as insincere or complex expressions of setiment, emojis as straightforward experssions of sentiment, and emojis as having context-dependent meanings. Across themes, participants often described important differences between their own emoji use and emoji use by adults.

CONCLUSIONS: Adolescent focus group participants described patterns of emoji use that largely resembled those observed in studies of adults. Like adults, our adolescent participants described emojis' semantic meanings as being highly flexible and context-dependent. They also described both phatic and emotive functions of emoji use but described both functions in ways that differed from the patterns of emoji use described in adult samples. Adolescents described their phatic emoji use as absurd and described their emotive emoji use as most often sarcastic. These findings suggest that emoji use serves similar pragmatic functions for both adolescents and adults, but that adolescents see their emoji use as more complex than adult emoji use. This has important implications for adults who communicate with adolescents through text-based messaging and for researchers interested in adolescents' text-based communication.

PMID:40294434 | DOI:10.2196/59640

Annu Rev Genomics Hum Genet. 2025 Apr 28. doi: 10.1146/annurev-genom-111522-013953. Online ahead of print.

ABSTRACT

Breast cancer is a major public health burden that disproportionately affects women of African descent. Substantial progress has been made in understanding the genetic and biological drivers of breast cancer worldwide. However, this knowledge is unevenly distributed among all women with breast cancer, particularly those of African descent. The highlights of nearly three decades of research among women of African descent point mainly to a young age at diagnosis, aggressive disease, and distinct biomarkers, as well as a clear geographical distribution of disease subtypes and genetic variants. Despite this growing wealth of information, the African population's access to genetic care and understanding of inherited risk and disease biology remain limited. This review summarizes the state of knowledge on genetic risk in African populations with breast cancer, evaluates the strengths and limitations of the methodological approaches used, and suggests innovative strategies to ensure equitable participation in cancer genetic and genomic research. We discuss genotype-phenotype correlations and the inherited risk of breast cancer, including both rare and common genetic variants. We also address the role of somatic drivers of breast cancer, disease biomarkers, treatment targets, and pharmacogenomics in this population. Finally, we provide recommendations to enable future progress in diagnosis and treatment.

PMID:40294412 | DOI:10.1146/annurev-genom-111522-013953

Comput Methods Programs Biomed. 2025 Apr 23;267:108801. doi: 10.1016/j.cmpb.2025.108801. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patients, ignoring important information about radiomics features such as shape and texture of individual brain regions in structural images. To this end, this study proposed a novel deep learning approach to integrate multimodal brain connectome information and regional radiomics features for brain disease diagnosis.

METHODS: A dual-branch autoencoder (ConnectomeAE) based on multimodal brain connectomes was proposed for brain disease diagnosis. Specifically, a matrix of radiomics feature extracted from structural magnetic resonance image (MRI) was used as Rad_AE branch inputs for learning important brain region features. Functional brain network built from functional MRI image was used as inputs to Cycle_AE for capturing brain disease-related connections. By separately learning node features and connection features from multimodal brain networks, the method demonstrates strong adaptability in diagnosing different brain diseases.

RESULTS: ConnectomeAE was validated on two publicly available datasets. The experimental results show that ConnectomeAE achieved excellent diagnostic performance with an accuracy of 70.7 % for autism spectrum disorder and 90.5 % for Alzheimer's disease. A comparison of training time with other methods indicated that ConnectomeAE exhibits simplicity and efficiency suitable for clinical applications. Furthermore, the interpretability analysis of the model aligned with previous studies, further supporting the biological basis of ConnectomeAE.

CONCLUSIONS: ConnectomeAE could effectively leverage the complementary information between multimodal brain connectomes for brain disease diagnosis. By separately learning radiomic node features and connectivity features, ConnectomeAE demonstrated good adaptability to different brain disease classification tasks.

PMID:40294455 | DOI:10.1016/j.cmpb.2025.108801

J Med Internet Res. 2025 Apr 28;27:e58723. doi: 10.2196/58723.

ABSTRACT

In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.

PMID:40294410 | DOI:10.2196/58723

PLoS One. 2025 Apr 28;20(4):e0321529. doi: 10.1371/journal.pone.0321529. eCollection 2025.

ABSTRACT

Accurate power load forecasting helps reduce energy waste and improve grid stability. This paper proposes a hybrid forecasting model, BiStacking+TCN-GRU, which leverages both ensemble learning and deep learning techniques. The model first applies the Pearson correlation coefficient (PCC) to select features highly correlated with the power load. Then, BiStacking is used for preliminary predictions, followed by a temporal convolutional network (TCN) enhanced by a gated recurrent unit (GRU) to produce the final predictions. The experimental validation based on Panama's 2020 electricity load data demonstrated the effectiveness of the model, with the model achieving an RMSE of 29.1213 and an MAE of 22.5206, respectively, with an R² of 0.9719. These results highlight the model's superior performance in short-term load forecasting, demonstrating its strong practical applicability and theoretical contributions.

PMID:40294011 | DOI:10.1371/journal.pone.0321529

IEEE Trans Med Imaging. 2025 Apr 28;PP. doi: 10.1109/TMI.2025.3565000. Online ahead of print.

ABSTRACT

Due to the difficulty of collecting multi-label annotations for retinal diseases, fundus images are usually annotated with only one label, while they actually have multiple labels. Given that deep learning requires accurate training data, incomplete disease information may lead to unsatisfactory classifiers and even misdiagnosis. To cope with these challenges, we propose a co-pseudo labeling and active selection method for Fundus Single-Positive multi-label learning, named FSP. FSP trains two networks simultaneously to generate pseudo labels through curriculum co-pseudo labeling and active sample selection. The curriculum co-pseudo labeling adjusts the thresholds according to the model's learning status of each class. Then, the active sample selection maintains confident positive predictions with more precise pseudo labels based on loss modeling. A detailed experimental evaluation is conducted on seven retinal datasets. Comparison experiments show the effectiveness of FSP and its superiority over previous methods. Downstream experiments are also presented to validate the proposed method.

PMID:40293917 | DOI:10.1109/TMI.2025.3565000

IEEE Trans Neural Netw Learn Syst. 2025 Apr 28;PP. doi: 10.1109/TNNLS.2025.3558596. Online ahead of print.

ABSTRACT

Accurate prediction of pathological subtypes on radiological images is one of the most important deep learning (DL) tasks for the appropriate selection of clinical treatment. It is challenging for conventional DL models to obtain sufficient pathological labels for training because of the heavy workload, invasive surgery, and knowledge requirements in pathological analysis. However, existing methods based on limited annotations, such as active learning (AL) and semi-supervised learning (SSL), have difficulty in capturing lesion's effective features because of the complicated semantic information of radiologic images. In this article, we introduce an efficient domain knowledge-guided semantic prediction framework that integrates domain knowledge-guided AL and SSL methods. This framework can effectively predict pathological subtypes on the basis of radiologic images with limited pathological annotations via three key modules: 1) the discriminative spatial-semantic feature extraction module captures the spatial-semantic features of lesions as semantic information that can better reflect the semantic relationship and effectively mitigate overfitting risk; 2) the explicit sign-guided anchor attention module measures the multimodal semantic distribution of samples under the guidance of clinical domain knowledge, thus selecting the most representative AL samples for pathological labeling; and 3) the implicit radiomics-guided dual-task entanglement module exploits the inherent constraint relationships between implicit radiomics features (IRFs) and pathological subtypes, facilitating the aggregation of unlabeled data. Experiments have been extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs) and muscular invasiveness prediction in bladder cancer (BCa). The experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

PMID:40293902 | DOI:10.1109/TNNLS.2025.3558596

IEEE Trans Biomed Eng. 2025 Apr 28;PP. doi: 10.1109/TBME.2025.3561338. Online ahead of print.

ABSTRACT

Coronary CT angiography (CCTA) is an essential technique for clinical coronary assessment. However, the risks associated with ionizing radiation cannot be ignored, especially its stochastic effects, which increase the risk of cancer. Although it can effectively alleviate radiation problems, low-dose CCTA can reduce imaging quality and interfere with the diagnosis of the radiologist. Existing deep learning methods based on image restoration suffer from subtle structure degradation after noise suppression, leading to unclear coronary boundaries. Furthermore, in the absence of prior guidance on coronary location, subtle coronary branches will be lost after aggressive noise suppression and are difficult to restore successfully. To address the above issues, this paper proposes a novel Guided Refinement Network (GRN) model based on joint learning for restoring high-quality images from low-dose CCTA. GRN integrates coronary segmentation, which provides coronary location, into the denoising, and the two leverage mutual guidance for effective interaction and collaborative optimization. On the one hand, denoising provides images with lower noise levels for segmentation to assist in generating coronary masks. Furthermore, segmentation provides a prior coronary location for denoising, aiming to preserve and restore subtle coronary branches. GRN achieves noise suppression and subtle structure enhancement for low-dose CCTA imaging through joint denoising and segmentation, while also generating segmentation results with reference value. Quantitative and qualitative results show that GRN outperforms existing methods in noise suppression, subtle structure restoration, and visual perception improvement, and generates coronary masks that can serve as a reference for radiologists to assist in diagnosing coronary disease.

PMID:40293900 | DOI:10.1109/TBME.2025.3561338

IEEE J Biomed Health Inform. 2025 Apr 28;PP. doi: 10.1109/JBHI.2025.3561146. Online ahead of print.

ABSTRACT

Cerebrovascular segmentation is essential for diagnosing and treating cerebrovascular diseases. However, accurately segmenting cerebral vessels in TOF-MRA remains challenging due to significant interindividual variations in cerebrovascular morphology, low image con-trast, and class imbalance. The present study proposes an advanced deep learning model called PPA Net, consisting of VesselMRA Net and VesselConvLSTM components. Firstly, VesselMRA Net utilizes rectangular convolutional blocks to fuse multi-scale features, enhancing feature extraction per-formance. VesselMRA Net employs the attention mechanism to boost certain valuable semantic weighting, addressing segmentation challenges arising from class imbalance and low contrast. Secondly, VesselConvLSTM, a pixel-level prediction model, employs a gating mechanism to learn cerebral vessel morphology across individuals. It reduces individual differences in segmentation and restores inter-voxel correlations disrupted by data slicing, aiding VesselMRA Net in accurately segmenting cerebrovascular pixels. Lastly, integrating VesselMRA Net and VesselConv-LSTM results in a modular cerebral vessel segmentation framework, PPA Net, facilitating separate optimization of the backbone network and predicted model components. The performance of this model has been extensively validated through experimental evaluations on three publicly available datasets, obtaining significant competitiveness when compared to the state-of-the-art of the current cerebral vessel segmentation models.

PMID:40293899 | DOI:10.1109/JBHI.2025.3561146

IEEE J Biomed Health Inform. 2025 Apr 28;PP. doi: 10.1109/JBHI.2025.3564981. Online ahead of print.

ABSTRACT

Knee osteoarthritis (KOA) as a prevalent chronic disease, detrimentally impacts the quality of life among affected individuals. The knee adduction moment (KAM) during the stance phase has been identified as a potential biomechanical measure for assessing the severity of KOA. Traditional KAM assessment relies on expensive equipment, which limits its popularization. In contrast, current KAM estimation methods based on wearables and deep-learning technology offer the advantage of lower costs. However, it still suffers challenges in achieving accurate estimation. To address this challenge, a novel deep-learning framework is proposed in this work, which estimates the KAM from Inertial Measurement Units (IMU) and Electromyography (EMG) data by a well-designed self-aware fusion model. Walking data from 18 effective subjects were recorded with 4 IMUs and 6 EMGs. Results show that the model significantly improves KAM estimation accuracy. The relative root-mean-square error of the proposed model is 9.15% BW BH lower than counterpart estimation methods.

PMID:40293894 | DOI:10.1109/JBHI.2025.3564981

Drug Dev Res. 2025 May;86(3):e70094. doi: 10.1002/ddr.70094.

ABSTRACT

Idiopathic pulmonary fibrosis is a progressive, irreversible lung disease of unknown cause, characterized by gradual thickening and scarring of lung tissue, impairing oxygen transfer into the bloodstream. As a result, symptoms such as shortness of breath, fatigue, and a persistent dry cough occur. Currently, the FDA-approved antifibrotic agents Pirfenidone and Nintedanib can slow the progression of the disease. However, these treatments cannot completely stop the loss of lung function and do not provide a significant improvement in the quality of life of patients. As fibrosis progresses, lung capacity decreases, shortness of breath increases, and general health deteriorates significantly. Therefore, new more effective, and targeted therapies that can halt the progression of IPF are urgently needed. This review addresses novel strategies to slow or halt the disease-related loss of lung function by targeting key mechanisms involved in the pathogenesis of IPF. The molecular structure-activity relationships (SARs) of synthesized compounds targeting JAK/STAT, TGF-β/Smad, Wnt/β-catenin, PI3K, JNK1, and other critical signaling pathways were examined. These targeted approaches have great potential for the development of more potent and selective therapeutic agents for the treatment of IPF. The insights provided in this review may contribute to the future development of more efficient and selective antifibrotic drugs.

PMID:40293838 | DOI:10.1002/ddr.70094

Proc Natl Acad Sci U S A. 2025 May 6;122(18):e2410092122. doi: 10.1073/pnas.2410092122. Epub 2025 Apr 28.

ABSTRACT

Plasmacytoid Dendritic cells (pDCs) are the most potent producers of interferons, which are critical antiviral cytokines. pDC development is, however, compromised following a viral infection, and this phenomenon, as well as its relationship to conventional (c)DC development is still incompletely understood. By using lymphocytic choriomeningitis virus (LCMV) infection in mice as a model system, we observed that DC progenitors skewed away from pDC and toward cDC development during in vivo viral infection. Subsequent characterization of the transcriptional and epigenetic landscape of fms-like tyrosine kinase 3+ (Flt3+) DC progenitors and follow-up studies revealed increased apoptosis and reduced proliferation in different individual DC-progenitors as well as a profound type I interferon (IFN-I)-dependent ablation of pre-pDCs, but not pre-DC precursors, after both acute and chronic LCMV infections. In addition, integrated genomic analysis identified altered activity of 34 transcription factors in Flt3+ DC progenitors from infected mice, including two regulators of Glucocorticoid (GC) responses. Subsequent studies demonstrated that addition of GCs to DC progenitors led to downregulated pDC-primed-genes while upregulating cDC-primed-genes, and that endogenous GCs selectively decreased pDC, but not cDC, numbers upon in vivo LCMV infection. These findings demonstrate a significant ablation of pre-pDCs in infected mice and identify GCs as suppressors of pDC generation from early progenitors. This provides a potential explanation for the impaired pDC development following viral infection and links pDC numbers to the hypothalamic-pituitary-adrenal axis.

PMID:40294270 | DOI:10.1073/pnas.2410092122