Clin Infect Dis. 2025 May 24:ciaf265. doi: 10.1093/cid/ciaf265. Online ahead of print.

ABSTRACT

BACKGROUND: We report an interim analysis of safety and efficacy of pemivibart in individuals with (cohort A) or without (cohort B) significant immunocompromise in the phase 3 CANOPY trial.

METHODS: Eligible participants (≥18 years; negative for current SARS-CoV-2 infection) received 2 intravenous 4500-mg pemivibart infusions (cohort A) or were randomized 2:1 to receive blinded pemivibart or placebo (cohort B) 90 days apart. Safety was a primary endpoint for both cohorts. The primary immunobridging endpoint for cohort A has previously been reported. Composite incidence of reverse transcription-polymerase chain reaction-confirmed symptomatic COVID-19, COVID-19 hospitalization, and all-cause mortality was an exploratory endpoint.

RESULTS: In September-November 2023, 306 participants received pemivibart (cohort A); 317 received pemivibart and 162 placebo (cohort B). The most common study drug-related adverse events were infusion-related reactions (cohort A: 11/306 [3.6%]; cohort B: 7/317 [2.2%, pemivibart] and 0/162 [placebo]). Four of 623 (0.6%) participants who received pemivibart experienced anaphylactic reactions (2 serious). In cohort A, the composite COVID-19 incidence through month 6 was 11/298 (3.7%; 2 deaths). In cohort B, 6/317 (1.9%; no deaths) and 19/160 (11.9%; no deaths) pemivibart and placebo participants, respectively, met the endpoint through month 6 (84.1% standardized relative risk reduction [RRR; 95% CI, 60.9-93.5; nominal P<.0001]), and 15/317 (4.7%; 1 death) and 29/160 (18.1%; no deaths) pemivibart and placebo participants, respectively, met the endpoint through month 12 (73.9% standardized RRR [95% CI, 52.8-85.6; nominal P<.0001]). Twelve month protection was conferred with no additional dosing.

CONCLUSIONS: Pemivibart provided prophylactic efficacy against COVID-19 and was well-tolerated by most participants. Anaphylaxis was an important safety risk.

CLINICAL TRIALS REGISTRATION: NCT06039449.

PMID:40410927 | DOI:10.1093/cid/ciaf265

Sci Rep. 2025 May 23;15(1):17993. doi: 10.1038/s41598-025-02333-z.

ABSTRACT

Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast the results of seven machine learning algorithms and three deep learning models with regard to state-of-the-art parameters. In the paper, optimized model is developed that combined eager random forest and sluggish k star techniques. State-of-the-art parameters have been evaluated and compared for three scenarios. In first scenario with original features, in the second scenario using feature selection and resampling technique with the percentage split method, and in the third scenario using feature selection and resampling technique with 10-fold cross-validation. The principal component analysis is performed for feature selection. An optimized ensembled model performs well in comparison to other models in all three scenarios. It achieved an accuracy of 77% in the first scenario, 89% in the second scenario, and 93% in the third scenario. The proposed model shows the performance increase in accuracy by 8% as compared to the top performer Kstar machine learning model and 21% as compared to deep learning model AIPs-DeepEnC-GA which is remarkable. Also there is significant improvement in other important evaluation parameters in comparison to top performing models. Further concept of W-saw score and L-saw is presented for all the scenarios. An optimized ensembled model using feature selection and resampling technique with tenfold cross-validation performs best among all machine learning models in all the scenarios.

PMID:40410277 | DOI:10.1038/s41598-025-02333-z

IEEE J Biomed Health Inform. 2025 May 23;PP. doi: 10.1109/JBHI.2025.3573158. Online ahead of print.

ABSTRACT

Predicting drug-disease associations is a crucial step in drug repositioning, especially with computational methods that quickly locate potential drug-disease pairs. Heterogenous network is a common tool for introducing multiple type relation information about drugs and diseases. However, the diversity of relations is ignored in most of existing methods, which makes them difficult to explore type semantic information with structure properties. Therefore, we propose a relation-centric GNN framework to encode critical association patterns. Firstly, we utilize a relation-centric graph, line graph, to represent the context of a drug-disease pair identified as the center node. The prediction problem is modeled to learn the embedding vector of the center node. Secondly, a multi-grained line graph neural network (MGLGNN) is designed to excavate fine-grained features that encapsulate local graph structures. We theoretically define a handful of typical nodes that can be regarded as high-order abstractions of relations in each type. Then, MGLGNN distills the local information and passes it to typical nodes from a global perspective. With learned multi-grained features, the center node automatically captures heterogenous relation semantics and structure patterns. Thirdly, a hierarchical contrastive learning (HCL) mechanism is proposed to ensure the quality of multi-grained features in an unsupervised way. Extensive experiments show the great potential of our model in mining drug-disease associations.

PMID:40408216 | DOI:10.1109/JBHI.2025.3573158

Phys Rev Lett. 2025 May 2;134(17):176503. doi: 10.1103/PhysRevLett.134.176503.

ABSTRACT

Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall effect is a prominent example of strong correlation, with Landau level mixing being one of the most challenging aspects to address using traditional computational methods. Deep learning real-space neural network wave function methods have emerged as promising architectures to describe electron correlations in molecules and materials, but their power has not been fully tested for exotic quantum states. In this work, we employ real-space neural network wave function techniques to investigate fractional quantum Hall systems. On both 1/3 and 2/5 filling systems, we achieve energies consistently lower than exact diagonalization results which only consider the lowest Landau level. We also demonstrate that the real-space neural network wave function can naturally capture the extent of Landau level mixing up to a very high level, overcoming the limitations of traditional methods. Our work underscores the potential of neural networks for future studies of strongly correlated systems and opens new avenues for exploring the rich physics of the fractional quantum Hall effect.

PMID:40408749 | DOI:10.1103/PhysRevLett.134.176503

Phys Rev Lett. 2025 May 2;134(17):177301. doi: 10.1103/PhysRevLett.134.177301.

ABSTRACT

Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. Leveraging recent analytical progress in the proportional regime of deep learning theory (i.e., the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio α=P/N finite), in this Letter we develop a novel single-instance Franz-Parisi formalism that yields an effective theory for TL in fully connected neural networks. Unlike the (lazy-training) infinite-width limit, where TL is ineffective, we demonstrate that in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization.

PMID:40408730 | DOI:10.1103/PhysRevLett.134.177301

PLoS One. 2025 May 23;20(5):e0321008. doi: 10.1371/journal.pone.0321008. eCollection 2025.

ABSTRACT

Accurate monthly runoff forecasting is vital for water management, flood control, hydropower, and irrigation. In glacierized catchments affected by climate change, runoff is influenced by complex hydrological processes, making precise forecasting even more challenging. To address this, the study focuses on the Lotschental catchment in Switzerland, conducting a comprehensive comparison between deep learning and ensemble-based models. Given the significant autocorrelation in runoff time series data, which may hinder the evaluation of prediction models, a novel statistical method is employed to assess the effectiveness of forecasting models in detecting turning points in the runoff data. The performance of Extreme Gradient Boosting (XGBoost) was compared with long short-term memory (LSTM) and random forest (RF) models for one-month-ahead runoff forecasting. The study used 20 years of runoff data (2002-2021), with 70% (2002-2015) dedicated for training and calibration, and the remaining data (2016-2021) for testing. The findings for the testing phase results show that the XGBoost model achieves the best accuracy, with R² of 0.904, RMSE of 1.554 m³/sec, an NSE of 0.797, and Willmott index (d) of 0.972, outperforming both the LSTM and RF models. The study also found that the XGBoost model estimated turning points more accurately, obtaining forecasting improvements of up to 22% to 34% compared to LSTM and RF models. Overall, the study's findings are essential for global water resource management, providing insights that can inform sustainable practices to support societies impacted by climate change.

PMID:40408639 | DOI:10.1371/journal.pone.0321008

PLoS Comput Biol. 2025 May 23;21(5):e1013071. doi: 10.1371/journal.pcbi.1013071. eCollection 2025 May.

ABSTRACT

Deep learning-based methods for identifying and tracking cells within microscopy images have revolutionized the speed and throughput of data analysis. These methods for analyzing biological and medical data have capitalized on advances from the broader computer vision field. However, cell tracking can present unique challenges, with frequent cell division events and the need to track many objects with similar visual appearances complicating analysis. Existing architectures developed for cell tracking based on convolutional neural networks (CNNs) have tended to fall short in managing the spatial and global contextual dependencies that are crucial for tracking cells. To overcome these limitations, we introduce Cell-TRACTR (Transformer with Attention for Cell Tracking and Recognition), a novel deep learning model that uses a transformer-based architecture. Cell-TRACTR operates in an end-to-end manner, simultaneously segmenting and tracking cells without the need for post-processing. Alongside this model, we introduce the Cell-HOTA metric, an extension of the Higher Order Tracking Accuracy (HOTA) metric that we adapted to assess cell division. Cell-HOTA differs from standard cell tracking metrics by offering a balanced and easily interpretable assessment of detection, association, and division accuracy. We test our Cell-TRACTR model on datasets of bacteria growing within a defined microfluidic geometry and mammalian cells growing freely in two dimensions. Our results demonstrate that Cell-TRACTR exhibits strong performance in tracking and division accuracy compared to state-of-the-art algorithms, while also meeting traditional benchmarks in detection accuracy. This work establishes a new framework for employing transformer-based models in cell segmentation and tracking.

PMID:40408631 | DOI:10.1371/journal.pcbi.1013071

PLoS One. 2025 May 23;20(5):e0324347. doi: 10.1371/journal.pone.0324347. eCollection 2025.

ABSTRACT

The agricultural sector faces critical challenges, including significant crop losses due to undetected plant diseases, inefficient monitoring systems, and delays in disease management, all of which threaten food security worldwide. Traditional approaches to disease detection are often labor-intensive, time-consuming, and prone to errors, making early intervention difficult. This paper proposes an intelligent framework for automated crop health monitoring and early disease detection to overcome these limitations. The system leverages deep learning, cloud computing, embedded devices, and the Internet of Things (IoT) to provide real-time insights into plant health over large agricultural areas. The primary goal is to enhance early detection accuracy and recommend effective disease management strategies, including crop rotation and targeted treatment. Additionally, environmental parameters such as temperature, humidity, and water levels are continuously monitored to aid in informed decision-making. The proposed framework incorporates Convolutional Neural Network (CNN), MobileNet-1, MobileNet-2, Residual Network (ResNet-50), and ResNet-50 with InceptionV3 to ensure precise disease identification and improved agricultural productivity.

PMID:40408612 | DOI:10.1371/journal.pone.0324347

Sci Adv. 2025 May 23;11(21):eadu2151. doi: 10.1126/sciadv.adu2151. Epub 2025 May 23.

ABSTRACT

Non-small cell lung cancer (NSCLC) constitutes over 80% of lung cancer cases and remains a leading cause of cancer-related mortality worldwide. Despite the advent of immune checkpoint inhibitors, their efficacy is limited to 27 to 45% of patients. Identifying likely treatment responders is essential for optimizing healthcare and improving quality of life. We generated multiplex immunofluorescence (mIF) images, histopathology, and RNA sequencing data from human NSCLC tissues. Through the analysis of mIF images, we characterized the spatial organization of 1.5 million cells based on the expression levels for 33 biomarkers. To enable large-scale characterization of tumor microenvironments, we developed NucSegAI, a deep learning model for automated nuclear segmentation and cellular classification in histology images. With this model, we analyzed the morphological, textural, and topological phenotypes of 45.6 million cells across 119 whole-slide images. Through unsupervised phenotype discovery, we identified specific lymphocyte phenotypes predictive of immunotherapy response. Our findings can improve patient stratification and guide selection of effective therapeutic regimens.

PMID:40408481 | DOI:10.1126/sciadv.adu2151

Genome Biol Evol. 2025 May 23:evaf101. doi: 10.1093/gbe/evaf101. Online ahead of print.

ABSTRACT

Plant DNA methylation changes occur hundreds to thousands of times faster than DNA mutations and can be transmitted transgenerationally, making them useful for studying population-scale patterns in clonal or selfing species. However, a state-of-the-art approach to use them for inferring population genetic processes and demographic histories is lacking. To address this, we compare evolutionary signatures extracted from CG methylomes and genomes in Arabidopsis thaliana and Brachypodium distachyon. While methylation variants (SMPs) are less effective than single nucleotide polymorphisms (SNPs) for identifying population differentiation in A. thaliana, they can classify phenotypically divergent B. distachyon subgroups that are otherwise genetically indistinguishable. The site frequency spectra generated using methylation sites from varied genomic locations and evolutionary conservation exhibit an excess of rare alleles. Nucleotide diversity estimates were three orders of magnitude higher for methylation variants than for SNPs in both species, driven by the higher epimutation rate. Correlations between SNPs and SMPs in nucleotide diversity and allele frequencies at gene exons are weak or absent in A. thaliana, possibly because the two sources of variation reflect evolutionary forces acting at different timescales. Linkage disequilibrium quickly decays within 100 bp for methylation variants in both plant species. Finally, we have developed a novel deep learning-based approach that infers demographic histories using methylation variation data alone. We identified recent population expansions in A. thaliana and B. distachyon using methylation variants that were not identified when using SNPs. Our study demonstrates the unique evolutionary insights methylomes provide that SNPs alone cannot reveal.

PMID:40408446 | DOI:10.1093/gbe/evaf101

PLoS One. 2025 May 23;20(5):e0318372. doi: 10.1371/journal.pone.0318372. eCollection 2025.

ABSTRACT

In the realm of game playing, deep reinforcement learning predominantly relies on visual input to map states to actions. The visual data extracted from the game environment serves as the primary foundation for state representation in reinforcement learning agents. However, humans leverage additional sensory inputs, such as audio cues, which play a pivotal role in perception and decision-making. Therefore, incorporating raw audio along with visual information shows potential for offering valuable insights to reinforcement learning agents. This study advocates for the integration of raw audio samples as complementary information to visual data in state representation. By using raw audio with visual cues, our objective is to enrich the decision-making process of the agent at each stage. Experimental evaluation were conducted employing Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) algorithms within ViZDoom and Unity reinforcement learning environments. The results of our experiments reveal that augmenting visual information with raw audio samples yields superior rewards and expedites the learning rate compared to relying solely on visual data. Additionally, the findings suggest that considering both visual and audio features enhances the agent's behavior, a trend observed across Unity and ViZDoom environments. This study underscores the potential advantages of incorporating multisensory information, particularly raw audio, into the state representation of reinforcement learning agents. Such insights contribute to advancing our understanding of how agents perceive and engage with their environments, ultimately enhancing performance in complex gaming scenarios.

PMID:40408327 | DOI:10.1371/journal.pone.0318372

PLoS One. 2025 May 23;20(5):e0321179. doi: 10.1371/journal.pone.0321179. eCollection 2025.

ABSTRACT

The rapid proliferation of the Power Internet of Things (PIoT) has given rise to severe network security threats, with eavesdropping attacks emerging as a paramount concern. Traditional eavesdropping detection methods struggle to adapt to complex and dynamic attack patterns, necessitating the exploration of more intelligent and efficient anomaly localization approaches. This paper proposes an innovative method for eavesdropping node localization based on Kolmogorov-Arnold Networks (KANs). Leveraging the powerful ability of KANs to approximate arbitrary nonlinear functions, this method constructs an end-to-end mapping from heterogeneous node features to eavesdropping locations through flexible combinations of spline functions. To address the challenges of real-world power grid environments, this paper designs optimization strategies such as adaptive grid refinement and hierarchical sparsity regularization, further enhancing the model's robustness and interpretability. Extensive simulations and experiments on real power grid data demonstrate that the proposed method significantly outperforms traditional machine learning and mainstream deep learning approaches in terms of localization accuracy, generalization ability, and computational efficiency. This paper provides new perspectives and tools for intelligent power grid information security in IoT environments, holding significant innovative value in both theory and practice.

PMID:40408323 | DOI:10.1371/journal.pone.0321179

PLoS One. 2025 May 23;20(5):e0321856. doi: 10.1371/journal.pone.0321856. eCollection 2025.

ABSTRACT

The growing reliance on video conferencing software brings significant benefits but also introduces challenges, particularly in managing audio quality. In multi-participant settings, ambient noise and interruptions can hinder speaker recognition and disrupt the flow of conversation. This work proposes an audio-visual source separation pipeline designed specifically for video conferencing and telepresence robots applications. The framework aims to isolate and enhance the speech of individual participants in noisy environments while enabling control over the volume of specific individuals captured in the video frame. The proposed pipeline comprises key components: a deep learning-based feature extractor for audio and video, an audio-guided visual attention mechanism, a module for background noise suppression and human voice separation, and Deep Multi-Resolution Network (DMRN) modules. For human voice separation, the DPRNN-TasNet, a robust deep neural network framework, is employed. Experimental results demonstrate that the methodology effectively isolates and amplifies individual participants' speech, achieving a test accuracy of 71.88 % on both the AVE and Music 21 datasets.

PMID:40408322 | DOI:10.1371/journal.pone.0321856

PLoS One. 2025 May 23;20(5):e0323708. doi: 10.1371/journal.pone.0323708. eCollection 2025.

ABSTRACT

INTRODUCTION: The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), presents a significant global public health threat. Concurrently, hepatitis B virus (HBV) remains a significant public health challenge. While previous studies have indicated an association between COVID-19 and chronic hepatitis B, the common underlying pathogenesis of these diseases remains incompletely understood.

METHODS: To investigate the shared molecular mechanisms between chronic HBV infection and COVID-19, a comprehensive investigation was conducted using bioinformatics and systems biology. Specifically, we utilized RNA-seq datasets (GSE196822 and GSE83148) to identify differentially expressed genes (DEGs) associated with both SARS-CoV-2 and HBV infection. Subsequently, these common DEGs were utilized to identify shared pathways, hub genes, transcriptional regulatory networks, and potential drugs. The differential expression of hub genes in both COVID-19 and HBV was verified using the GSE171110 and GSE94660 datasets, respectively.

RESULTS: From the 106 shared DEGs identified, immune-related pathways were found to play a role in the development and progression of chronic hepatitis B and COVID-19. Protein-protein interaction (PPI) network analysis revealed 8 hub genes: CDK1, E2F7, E2F8, TYMS, KIF20A, CENPE, TPX2, HMMR, CD8A, GZMA. In the validation set, the expression of hub genes was statistically significant in both the COVID-19 group and the HBV group compared with the healthy control group. Transcriptional regulatory network analysis identified 155 microRNAs (miRNAs) and 43 transcription factors (TFs) as potential regulatory signals. Notably, we identified potential therapeutic drugs for HBV chronic infection and COVID-19, including progesterone, estradiol, dasatinib, aspirin, etoposide, irinotecan hydrochloride, phorbol 12-myristate 13-acetate, lucanthone, calcitriol.

CONCLUSION: This research elucidates potential molecular targets, signaling pathways, and promising small molecule compounds that could aid in the treatment of chronic HBV infection and COVID-19.

PMID:40408617 | DOI:10.1371/journal.pone.0323708

Sci Adv. 2025 May 23;11(21):eadv2930. doi: 10.1126/sciadv.adv2930. Epub 2025 May 23.

ABSTRACT

Tumor hypoxia leads to radioresistance and markedly worse clinical outcomes for pediatric malignant rhabdoid tumors (MRTs). Our transcriptomics and bioenergetic profiling data reveal that mitochondrial oxidative phosphorylation is a metabolic vulnerability of MRT and can be exploited to overcome consumptive hypoxia by repurposing an FDA-approved antimalarial drug, atovaquone (AVO). We then establish the utility of oxygen-enhanced-multispectral optoacoustic tomography, a label-free, ionizing radiation-free imaging modality, to visualize and quantify spatiotemporal changes in tumor hypoxia in response to AVO. We show a potent but transient increase in tumor oxygenation upon AVO treatment that results in complete elimination of tumors in all tested mice when combined with 10-gray radiotherapy, a dose several times lower than the current clinic standard. Last, we use translational mathematical modeling for systematic evaluation of dosing regimens, administration timing, and therapeutic synergy in a virtual patient cohort. Together, our work establishes a framework for safe and pediatric patient-friendly image-guided metabolic radiosensitization of rhabdoid tumors.

PMID:40408469 | DOI:10.1126/sciadv.adv2930

Proc Natl Acad Sci U S A. 2025 May 27;122(21):e2411930122. doi: 10.1073/pnas.2411930122. Epub 2025 May 23.

ABSTRACT

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intracluster heterogeneity. We propose to move beyond coarse analyses of predefined subgroups by using contextualized learning, a multitask learning paradigm that uses multiview contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, and Neighborhood Selection) and estimate context-specific GRNs for 7,997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized.

PMID:40408406 | DOI:10.1073/pnas.2411930122

Proc Natl Acad Sci U S A. 2025 May 27;122(21):e2503857122. doi: 10.1073/pnas.2503857122. Epub 2025 May 23.

ABSTRACT

A major focus of human genetics is to map severe disease mutations. Increasingly, that goal is understood as requiring huge numbers of people to be sequenced from every broadly defined genetic ancestry group, so as not to miss "ancestry-specific variants." Here, we consider whether this focus is warranted. We start from first principles considerations, based on models of mutation-drift-selection balance, which suggest that since severe disease mutations tend to be strongly deleterious, and thus evolutionarily young, they will be kept at relatively constant frequency through recurrent mutation. Therefore, highly pathogenic alleles should be shared identically by descent within extended families, not broad ancestry groups, and sequencing more people should yield similar numbers regardless of ancestry. We test the model predictions using gnomAD genetic ancestry groupings and show that they provide a good fit to the classes of variants most likely to be highly pathogenic, notably sets of loss of function alleles at strongly constrained genes. These findings clarify that strongly deleterious alleles will be found at comparable rates in people of all ancestries, and the information they provide about human biology is shared across ancestries.

PMID:40408403 | DOI:10.1073/pnas.2503857122

Integr Comp Biol. 2025 May 23:icaf052. doi: 10.1093/icb/icaf052. Online ahead of print.

ABSTRACT

Environmental pressures and temperatures around the planet are not constant, both geographically and temporally. On land, changing climates push temperatures to new highs, and in the Arctic and deepest parts of the ocean, temperatures can be below 0°C without freezing. Additionally, these temperatures can fluctuate seasonally. Pressures also have a similar extreme from land to the depth of the sea. Organisms have found ways to adapt to these extreme conditions, and sometimes, two seemingly different pressures that derive from the environment share similar physiological and biochemical problems and therefore have evolved similar adaptations to those problems. Animals that live in cold conditions, like those seen in the Arctic, face the same problems as those in the deep ocean, such as denaturing proteins, changes in membrane structure, and disruption of biological matrices such as the extracellular matrix. Given the similar problems that impact both deep-sea-adapted animals and cold-adapted animals, they have evolved similar processes to adapt to these environmental conditions. This review proposes that cold and hydrostatic pressure exert similar biological challenges. Therefore, animals have evolved related mechanisms to adapt to these conditions. Thus, the information we have learned from studying cold-adapted species could be used to understand the poorly understood mechanisms responsible for adaptation to pressure.

PMID:40408292 | DOI:10.1093/icb/icaf052

Cell Rep. 2025 May 22;44(6):115734. doi: 10.1016/j.celrep.2025.115734. Online ahead of print.

ABSTRACT

Doublecortin-like kinase 1 (Dclk1) expression identifies cells that are rare in normal pancreas but occur with an increased frequency in pancreatic neoplasia. The identity of these cells has been a matter of debate. We employed Dclk1 reporter mouse models and single-cell RNA sequencing (scRNA-seq) to define Dclk1-expressing cells. In normal pancreas, Dclk1 identifies subsets of ductal, islet, and acinar cells. In pancreatic neoplasia, Dclk1 identifies several cell populations, among which acinar-to-ductal metaplasia (ADM)-like cells and tuft-like cells are predominant. These two populations play opposing roles, with Dclk1+ ADM-like cells sustaining and Dclk1+ tuft-like cells restraining tumor progression. The generation of Dclk1+ tuft-like cells requires the transcription factor SPIB and is sustained by a paracrine loop involving type 2 innate lymphoid cells (ILC2s) and cancer-associated fibroblasts (CAFs) that provide interleukin (IL)-13 and IL-33, respectively. Dclk1+ tuft-like cells release angiotensinogen to restrain tumor progression. Overall, our study defines pancreatic Dclk1+ cells and unveils a protective tuft cell-ILC2 axis against pancreatic neoplasia.

PMID:40408246 | DOI:10.1016/j.celrep.2025.115734

Med Sci (Basel). 2025 Apr 23;13(2):47. doi: 10.3390/medsci13020047.

ABSTRACT

Cancer is the second leading cause of mortality worldwide. Despite the available treatment options, a majority of cancer patients develop drug resistance, indicating the need for alternative approaches. Repurposed drugs, such as antiglycolytic and anti-microbial agents, have gained substantial attention as potential alternative strategies against different disease pathophysiologies, including lung cancer. To that end, multiple studies have suggested that the antiglycolytic dichloroacetate (DCA) and the antibiotic salinomycin (SAL) possess promising anticarcinogenic activity, attributed to their abilities to target the key metabolic enzymes, ion transport, and oncogenic signaling pathways involved in regulating cancer cell behavior, including cell survival and proliferation. We used the following searches and selection criteria. (1) Biosis and PubMed were used with the search terms dichloroacetate; salinomycin; dichloroacetate as an anticancer agent; salinomycin as an anticancer agent; dichloroacetate side effects; salinomycin side effects; salinomycin combination therapy; dichloroacetate combination therapy; and dichloroacetate or salinomycin in combination with other agents, including chemotherapy and tyrosine kinase inhibitors. (2) The exclusion criteria included not being related to the mechanisms of DCA and SAL or not focusing on their anticancer properties. (3) All the literature was sourced from peer-reviewed journals within a timeframe of 1989 to 2024. Importantly, experimental studies have demonstrated that both DCA and SAL exert promising anticarcinogenic properties, as well as having synergistic effects in combination with other therapeutic agents, against multiple cancer models. The goal of this review is to highlight the mechanistic workings and efficacy of DCA and SAL as monotherapies, and their combination with other therapeutic agents in various cancer models, with a major emphasis on non-small-cell lung cancer (NSCLC) treatment.

PMID:40407542 | DOI:10.3390/medsci13020047