BMC Pulm Med. 2025 May 2;25(1):212. doi: 10.1186/s12890-025-03621-0.

ABSTRACT

Cystic fibrosis (CF) is a genetic disorder characterized by progressive lung disease and extra-pulmonary manifestations with notable sex disparities in disease outcomes. In this review we summarize the underlying mechanisms driving this sex disparity, with a particular focus on the role of sex hormones on CF lung disease pathophysiology. We explore how the introduction of highly effective modulator therapies (HEMT) may impact sex differences in outcomes and assess whether they have the potential to close the sex gap. While treatment with HEMT has led to better outcomes in the CF population as a whole, females with CF continue to experience worse pulmonary morbidity than males. There is a need for continued research in this area, particularly into the influence and therapeutic potential of sex hormones.

PMID:40316939 | DOI:10.1186/s12890-025-03621-0

BMC Infect Dis. 2025 May 2;25(1):647. doi: 10.1186/s12879-025-10916-4.

ABSTRACT

INTRODUCTION: Pneumonia is the leading cause of child morbidity and mortality and accounts for 5.6 million under-five child deaths. Pneumonia has a significant impact on the quality of life, the country's economy, and the survival of children. Therefore, this study aimed to develop data-driven predictive model using machine learning algorithms to predict pneumonia and stratify the determinant factors among children aged 6-23 months in Ethiopia.

METHODS: A total of 2035 samples of children were used from the 2016 Ethiopian Demographic and Health Survey dataset. Jupyter Notebook from Anaconda Navigators was used for data management and analysis. Important libraries such as Pandas, Seaborn, and Numpy were imported from Python. The data was pre-processed into a training and testing dataset with a 4:1 ratio, and tenfold cross-validation was used to reduce bias and enhance the models' performance. Six machine learning algorithms were used for model building and comparison, and confusion matrix elements were used to evaluate the performance of each algorithm. Principal component analysis and heatmap function were used for correlation detection between features. Feature importance score was used to identify and stratify the most important predictors of pneumonia.

RESULTS: From 2035 total samples, 16.6%, 20.1%, and 24.2% of children had short rapid breath, fever, and cough respectively. The overall magnitude of pneumonia among children aged 6-23 months was 31.3% based on the 2016 EDHS report. A random forest algorithm is the relatively best performance model to predict pneumonia and stratify its determinates with 91.3% accuracy. The health facility visits, child sex, initiation of breastfeeding, birth interval, birth weight, husbands' education, women's age, and region, are the top eight important predictors of pneumonia among children with important scores of more than 5% to 20% respectively.

CONCLUSIONS: Random forest is the best model to predict pneumonia and stratify its determinant factors. The implications of this study are profound for advanced research methodology, tailored to promote effective health interventions such as lifestyle modification and behavioral intervention, based on individuals' unique features, specifically for stakeholders to take proactive childcare interventions. The study would serve as pioneering evidence for future research, and researchers are recommended to use deep learning algorithms to enhance prediction accuracy.

PMID:40316929 | DOI:10.1186/s12879-025-10916-4

Mol Divers. 2025 May 2. doi: 10.1007/s11030-025-11206-6. Online ahead of print.

ABSTRACT

Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and older adults. According to the 2024 Human Pneumonia Etiology Research for Child Health (PERCH) study, hMPV is the second leading common cause of pneumonia in children under five in Asia and Africa. The virus encodes nine proteins, including the essential Fusion (F) and G glycoproteins, which facilitate entry to the host cells. Currently, there are no approved vaccines or antiviral treatments for hMPV; supportive care is the primary way it is managed. Hence, this study focuses on the F protein as a therapeutic target to find a repurposable drug to fight hMPV. Refolding of the F protein and its binding to heparan sulfate enable hMPV infection. Heparin sulfate is important for hMPV binding, and we have found that cangrelor and AVN 944 can prevent the fusion of membranes. We developed a deep learning-based pharmacophore to identify potential drugs targeting hMPV, from which we could narrowed a list of 2400 FDA-approved drugs and 255 antiviral drugs to 792 and 72 drugs, respectively. We then conducted quantitative validation using the ROC curve. Further virtual screening of the drugs was performed, leading us to select the one with the highest docking score. The validation of the deep learning prediction in virtual screening Pearson correlation was done. Further, the MD simulation of these drugs confirmed that the protein-drug complex stability remained in dynamic condition. Further, the stability of protein-drug complexes than unbound protein was confirmed by Free Energy Landscape and Dynamic Cross Correlation Matrices. Further in vitro and in vivo experiments need to determine the efficacy of the identified candidates.

PMID:40316857 | DOI:10.1007/s11030-025-11206-6

Sci Rep. 2025 May 2;15(1):15432. doi: 10.1038/s41598-025-99676-4.

ABSTRACT

Secure medical data sharing and access control play a prominent role. However, it is still unclear how to provide a security architecture that can guarantee the privacy and safety of sensitive medical data. Existing methods are application-specific and fail to take into account the complex security needs of healthcare applications. Moreover, the healthcare sector needs dynamic permission enforcement, extensible context-aware access control, flexible, and on-demand authentication. Therefore, this research proposes an access control mechanism and an effective attack detection model. The proposed authenticate access control mechanism (PA2C) safeguards data integrity as well as the security and dependability of EHR data sharing are improved by the use of smart contracts, encryption, and secure key management. On the other hand, the proposed intelligent voyage optimization algorithm-based Radial basis neural network (IntVO-RBNN) effectively detects the attacks in the network. Specifically, the Intelligent Voyage Optimization algorithm effectively tunes the model hyperparameters and the deployment of hybrid features contributes to the proposed model to detect attack patterns effectively. The comparative results showed that the suggested access control strategy performed better than the current methods in terms of minimal responsiveness of 100.18 s and less information loss of 4.49% for 100 blocks. Likewise, the proposed IntVO-RBNN attack detection model performs better with 95.26% recall, 97.84% precision, and 94.02% accuracy.

PMID:40316724 | DOI:10.1038/s41598-025-99676-4

Sci Rep. 2025 May 2;15(1):15333. doi: 10.1038/s41598-025-99701-6.

ABSTRACT

Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example, to determine whether thrombi are present in the LAA. To guide the imaging process, technologists first perform a localizer scan, which is a preliminary image used to identify the region of interest. However, the lack of well-defined landmarks makes accurate delimitation of the LAA in localizers difficult and often requires whole-heart scans, increasing radiation exposure and cancer risk. This study aims to automate LAA delimitation in CT localizers using deep learning. Four commonly used deep networks (VariFocalNet, Cascade-R-CNN, Task-aligned One-stage Object Detection Network, YOLO v11) were trained to predict the LAA boundaries on a cohort of 1253 localizers, collected retrospectively from a single center. The best-performing network in terms of delimitation accuracy was then evaluated on an internal test cohort of 368 patients, and on an external test cohort of 309 patients. The VariFocalNet performed best, achieving LAA delimitations with high accuracy (97.8% and 96.8%; Dice coefficients: 90.4% and 90.0%) and near-perfect clinical utility (99.8% and 99.3%). Compared to whole-heart scanning, the network-based delimitation reduced the radiation exposure by more than 50% (5.33 ± 6.42 mSv vs. 11.35 ± 8.17 mSv in the internal cohort, 4.39 ± 4.23 mSv vs. 10.09 ± 8.0 mSv in the external cohort). This study demonstrates that a deep learning network can accurately delimit the LAA in the localizer, leading to more accurate CT scans of the LAA, thereby significantly reducing radiation exposure to the patient compared to whole-heart scanning.

PMID:40316718 | DOI:10.1038/s41598-025-99701-6

Sci Rep. 2025 May 2;15(1):15428. doi: 10.1038/s41598-025-99436-4.

ABSTRACT

The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy.

PMID:40316651 | DOI:10.1038/s41598-025-99436-4

Sci Rep. 2025 May 2;15(1):15345. doi: 10.1038/s41598-025-98389-y.

ABSTRACT

Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. However, a large gap is observed between initial accuracies and those observed in clinical practice. In this work, we reproduced and assessed the accuracy of a large number of models, including deep learning networks, for detection of seizures from EEGs. Benchmarking included three different datasets for training and initial testing, and a manually annotated EEG from a local patient for further testing. Random forest and a convolutional neural network achieved the best results on public data, but a large reduction of accuracy was observed testing with the local data, especially for the neural network. We expect that the retrained models and the data available in this work will contribute to the integration of machine learning techniques as tools to improve the accuracy of diagnosis in clinical settings.

PMID:40316648 | DOI:10.1038/s41598-025-98389-y

Sci Rep. 2025 May 2;15(1):15387. doi: 10.1038/s41598-025-98264-w.

ABSTRACT

Mental disorders represent a critical global health challenge that affects millions around the world and significantly disrupts daily life. Early and accurate detection is paramount for timely intervention, which can lead to improved treatment outcomes. Electroencephalography (EEG) provides the non-invasive means for observing brain activity, making it a useful tool for detecting potential mental disorders. Recently, deep learning techniques have gained prominence for their ability to analyze complex datasets, such as electroencephalography recordings. In this study, we introduce a novel deep-learning architecture for the classification of mental disorders such as post-traumatic stress disorder, depression, or anxiety, using electroencephalography data. Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. Before feeding the model as low-level features, the input is pre-processed using a common spatial pattern filter, a signal space projection filter, and a wavelet denoising filter. Then the EEG signals are transformed using continuous wavelet transform to obtain a time-frequency representation. The convolutional layers tokenize the input signals transformed by our pre-processing pipeline, while the Transformer encoder effectively captures long-range temporal dependencies across sequences. This architecture is specifically tailored to process EEG data that has been preprocessed using continuous wavelet transform, a technique that provides a time-frequency representation, thereby enhancing the extraction of relevant features for classification. We evaluated the performance of our proposed model on three datasets: the EEG Psychiatric Dataset, the MODMA dataset, and the EEG and Psychological Assessment dataset. Our model achieved classification accuracies of 87.40% on the EEG and Psychological Assessment dataset, 89.84% on the MODMA dataset, and 92.28% on the EEG Psychiatric dataset. Our approach outperforms every concurrent approaches on the datasets we used, without showing any sign of over-fitting. These results underscore the potential of our proposed architecture in delivering accurate and reliable mental disorder detection through EEG analysis, paving the way for advancements in early diagnosis and treatment strategies.

PMID:40316629 | DOI:10.1038/s41598-025-98264-w

Sci Rep. 2025 May 2;15(1):15404. doi: 10.1038/s41598-025-94239-z.

ABSTRACT

This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR) prediction models. The prediction is ensured for a period ranging from a few hours to several days of the year. These models are derived from four machine learning methods, namely the Feed-forward Back Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method, Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combines Convolutional Neural Networks and Long Short-Term Memory networks. This combination results in the CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Normalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by each model above and the actual output, pre-recorded in the experimental trial. The experimental results consistently show the power of the CNN-LSTM model compared to the remaining models in terms of accuracy and reliability. This is due to its lower error rate and higher detection coefficient (R2 = 0.99925).

PMID:40316622 | DOI:10.1038/s41598-025-94239-z

Sci Rep. 2025 May 2;15(1):15411. doi: 10.1038/s41598-025-96588-1.

ABSTRACT

This study explores the motivations behind integrating TinyML-based voice assistants into daily life, focusing on enhancing their user interface (UI) and functionality to improve user experience. This research discusses real-world applications like smart home automation, visually impaired assistive technologies, and healthcare monitoring. This review acknowledges various problems and helps us understand why TinyML exerts such significant implications in numerous domains. Researchers derive solutions from this study on how voice assistants integrated with TinyML can effectively analyze and adjust to user behaviour patterns in real-world scenarios, thereby enabling the delivery of dynamic and responsive content to enhance user engagement. The article also focused on limitations while implementing TinyML. Researchers will understand the detailed issues that are unavailable in most papers. This work explores features that can be embedded in voice assistants, like smart home automation, smart watches, smart glasses for visually impaired people, etc., using TinyML. A comparative review of current methods identifies areas of research gaps such as deployment difficulties, noise interference, and model efficiency on low-resource devices. From this study, researchers can directly identify the research gap with minimal effort, which may motivate them to focus more on solving the open problems due to optimize the problem identification time.

PMID:40316605 | DOI:10.1038/s41598-025-96588-1

Sci Rep. 2025 May 2;15(1):15412. doi: 10.1038/s41598-025-00331-9.

ABSTRACT

Smart weed-crop discrimination is crucial for modern precision weed management. In this study, we aimed to develop a robust system for site-specific weed control in saffron fields by utilizing color images and a deep learning approach to distinguish saffron from four common weeds: flixweed, hoary cress, mouse barley, and wild garlic. A total of 504 images were taken in natural and unstructured field settings. Eight state-of-the-art deep learning networks - VGG19, ResNet152, Xception, InceptionResNetV2, EfficientNetB0, EfficientNetB1, EfficientNetV2B0, and EfficientNetV2B1 were evaluated as potential base networks. These networks underwent pre-training on ImageNet using transfer learning, followed by fine-tuning and improvement with additional layers to optimize performance on our dataset. The improved EfficientNetB0 model stood out as the top performer among the eight models, achieving an accuracy rate of 94.06% and a loss value of 0.513 on the test dataset. This proposed model excelled in accurately classifying plant categories, obtaining f1-scores ranging from 82 to 100%. We scrutinized fifteen scenarios of weed presence in saffron fields, focusing on various weed types, to propose efficient management tactics using the model. These discoveries lay the groundwork for precise saffron weed management strategies that reduce herbicide use, environmental impact, and boost yield and quality.

PMID:40316572 | DOI:10.1038/s41598-025-00331-9

Sci Rep. 2025 May 2;15(1):15453. doi: 10.1038/s41598-025-99857-1.

ABSTRACT

The contribution of forced oscillation technique (FOT), also called oscillometry, in diagnosis and follow-up of progressive pulmonary fibrosis (PPF) is not yet established. The aims of this monocentric retrospective study were to compare the FOT profile between patients suffering from PPF and stable non-idiopathic pulmonary fibrosis (IPF) interstitial lung diseases (ILDs), to look for a correlation between oscillometry and conventional function tests currently used for PPF follow-up and functional definition (forced vital capacity (FVC) and diffusing lung capacity (DLCO)) and correlation with ILD severity according to FVC. Compared to non-IPF stable ILDs (n = 96), PPF patients (n = 45) showed lower median resistance at 5Hz (Xrs5) values (during inspiratory phase: 0.31 versus -0.39 cmH2O/(L/sec), p = 0.019595). Xrs5 also showed moderate correlation with FVC and DLCO. Finally, among all ILDs (n = 160), Xrs5 showed correlation with disease severity according to FVC. These results suggest that, in conjunction with conventional pulmonary function tests, FOT could be an interesting tool to predict progressive course of fibrosing non-IPF ILDs. Its exact contribution to PPF diagnosis and follow-up needs to be determined by a prospective approach.

PMID:40316670 | DOI:10.1038/s41598-025-99857-1

Med Image Anal. 2025 Apr 26;103:103604. doi: 10.1016/j.media.2025.103604. Online ahead of print.

ABSTRACT

Idiopathic Pulmonary Fibrosis (IPF) is a progressive lung disease that continuously scars and thickens lung tissue, leading to respiratory difficulties. Timely assessment of IPF progression is essential for developing treatment plans and improving patient survival rates. However, current clinical standards require multiple (usually two) CT scans at certain intervals to assess disease progression. This presents a dilemma: the disease progression is identified only after the disease has already progressed. To address this issue, a feasible solution is to generate the follow-up CT image from the patient's initial CT image to achieve early prediction of IPF. To this end, we propose a lung structure and function information-guided residual diffusion model. The key components of our model include (1) using a 2.5D generation strategy to reduce computational cost of generating 3D images with the diffusion model; (2) designing structural attention to mitigate negative impact of spatial misalignment between the two CT images on generation performance; (3) employing residual diffusion to accelerate model training and inference while focusing more on differences between the two CT images (i.e., the lesion areas); and (4) developing a CLIP-based text extraction module to extract lung function test information and further using such extracted information to guide the generation. Extensive experiments demonstrate that our method can effectively predict IPF progression and achieve superior generation performance compared to state-of-the-art methods.

PMID:40315576 | DOI:10.1016/j.media.2025.103604

Malar J. 2025 May 2;24(1):139. doi: 10.1186/s12936-025-05376-9.

ABSTRACT

BACKGROUND: Understanding the molecular mechanisms of the malarial parasites in hosts is crucial for developing effective treatments. Epitranscriptomic research on pathogens has unveiled the significance of RNA methylation in gene regulation and pathogenesis. This is the first report investigating methylation signatures and alternative splicing events using Nanopore Direct RNA Sequencing to single-base resolution in Plasmodium falciparum and Plasmodium vivax clinical isolates with hepatic dysfunction complications.

METHODS: Direct RNA Sequencing using Nanopore from clinical isolates of P. falciparum and P. vivax showing hepatic dysfunction manifestation was performed. Subsequently, transcriptome reconstruction using FLAIR and transcript classification using SQANTI3, followed by methylation detection using CHEUI and m6Anet to identify N6-methyladenosine (m6A) and 5-methylcytosine (m5C) methylation signatures, was done. The alternative splicing events from both the datasets were documented.

RESULTS: The reference genome of Plasmodium reports > 5000 genes out of which ~ 50% was identified as expressed in the two sequenced isolates, including novel isoforms and intergenic transcripts, highlighting extensive transcriptome diversity. The distinct RNA methylation profiles of m6A and m5C from the expressed transcripts were observed in sense, Natural Antisense Transcripts (NATs) and intergenic categories hinting at species-specific regulatory mechanisms. Dual modification events were observed in a significant number of transcripts in both the parasites. Modified transcripts originating from apicoplast and mitochondrial genomes have also been detected. These modifications are unevenly present in the annotated regions of the mRNA, potentially influencing mRNA export and translation. Several splicing events were observed, with alternative 3' and 5' end splicing predominating in the datasets suggesting differences in translational kinetics and possible protein characteristics in these disease conditions.

CONCLUSION: The data shows the presence of modified sense, NATs and alternatively spliced transcripts. These phenomena together suggest the presence of multiple regulatory layers which decides the post-translational proteome of the parasites in particular disease conditions. Studies like these will help to decipher the post-translational environments of malaria parasites in vivo and elucidate their inherent proteome plasticity, thus allowing the conceptualization of novel strategies for interventions.

PMID:40316999 | DOI:10.1186/s12936-025-05376-9

Nat Biotechnol. 2025 May 2. doi: 10.1038/s41587-025-02659-z. Online ahead of print.

ABSTRACT

Despite progress in mapping protein-protein interactions, their tissue specificity is understudied. Here, given that protein coabundance is predictive of functional association, we compiled and analyzed protein abundance data of 7,811 proteomic samples from 11 human tissues to produce an atlas of tissue-specific protein associations. We find that this method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while cell-type-specific cellular structures, such as synaptic components, are found to represent a substantial driver of differences between tissues. Over 25% of associations are tissue specific, of which <7% are because of differences in gene expression. We validate protein associations for the brain through cofractionation experiments in synaptosomes, curation of brain-derived pulldown data and AlphaFold2 modeling. We also construct a network of brain interactions for schizophrenia-related genes, indicating that our approach can functionally prioritize candidate disease genes in loci linked to brain disorders.

PMID:40316700 | DOI:10.1038/s41587-025-02659-z

J Mol Biol. 2025 Apr 30:169181. doi: 10.1016/j.jmb.2025.169181. Online ahead of print.

ABSTRACT

Understanding of complex biological systems remains a significant challenge due to their high dimensionality, nonlinearity, and context-specific behavior. Artificial intelligence (AI) and mechanistic modeling are becoming essential tools for studying such complex systems. Mechanistic modeling can facilitate the construction of simulatable models that are interpretable but often struggle with scalability and parameters estimation. AI can integrate multi-omics data to create predictive models, but it lacks interpretability. The gap between these two modeling methods limits our ability to develop comprehensive and predictive models for biomedical applications. This article reviews the most recent advancements in the integration of AI and mechanistic modeling to fill this gap. Recently, with omics availability, AI has led to new discoveries in mechanistic computational modeling. The mechanistic models can also help in getting insight into the mechanism for prediction made by AI models. This integration is helpful in modeling complex systems, estimating the parameters that are hard to capture in experiments, and creating surrogate models to reduce computational costs because of expensive mechanistic model simulations. This article focuses on advancements in mechanistic computational models and AI models and their integration for scientific discoveries in biology, pharmacology, drug discovery and diseases. The mechanistic models with AI integration can facilitate biological discoveries to advance our understanding of disease mechanisms, drug development, and personalized medicine. The article also highlights the role of AI and mechanistic model integration in the development of more advanced models in the biomedical domain, such as medical digital twins and virtual patients for pharmacological discoveries.

PMID:40316010 | DOI:10.1016/j.jmb.2025.169181

Cell Rep Methods. 2025 Apr 25:101033. doi: 10.1016/j.crmeth.2025.101033. Online ahead of print.

ABSTRACT

High-quality human genome assemblies derived from lymphoblastoid cell lines (LCLs) provide reference genomes and pangenomes for genomics studies. However, LCLs pose technical challenges for profiling immunoglobulin (IG) genes, as their IG loci contain a mixture of germline and somatically recombined haplotypes, making genotyping and assembly difficult with widely used frameworks. To address this, we introduce IGLoo, a software tool that analyzes sequence data and assemblies derived from LCLs, characterizing somatic V(D)J recombination events and identifying breakpoints and missing IG genes in the assemblies. Furthermore, IGLoo implements a reassembly framework to improve germline assembly quality by integrating information on somatic events and population structural variations in IG loci. Applying IGLoo to the assemblies from the Human Pangenome Reference Consortium, we gained valuable insights into the mechanisms, gene usage, and patterns of V(D)J recombination and the causes of assembly artifacts in the IG heavy-chain (IGH) locus, and we improved the representation of IGH assemblies.

PMID:40315852 | DOI:10.1016/j.crmeth.2025.101033

Cell Stem Cell. 2025 Apr 28:S1934-5909(25)00141-9. doi: 10.1016/j.stem.2025.04.006. Online ahead of print.

ABSTRACT

The repertoire of neurons and their progenitors depends on their location along the antero-posterior and dorso-ventral axes of the neural tube. To model these axes, we designed the Dual Orthogonal-Morphogen Assisted Patterning System (Duo-MAPS) diffusion device to expose spheres of induced pluripotent stem cells (iPSCs) to concomitant orthogonal gradients of a posteriorizing and a ventralizing morphogen, activating WNT and SHH signaling, respectively. Comparison with single-cell transcriptomes from the fetal human brain revealed that Duo-MAPS-patterned organoids generated an extensive diversity of neuronal lineages from the forebrain, midbrain, and hindbrain. WNT and SHH crosstalk translated into early patterns of gene expression programs associated with the generation of specific brain lineages with distinct functional networks. Human iPSC lines showed substantial interindividual and line-to-line variations in their response to morphogens, highlighting that genetic and epigenetic variations may influence regional specification. Morphogen gradients promise to be a key approach to model the brain in its entirety.

PMID:40315847 | DOI:10.1016/j.stem.2025.04.006

Cell Host Microbe. 2025 Apr 25:S1931-3128(25)00143-X. doi: 10.1016/j.chom.2025.04.010. Online ahead of print.

ABSTRACT

Adult human facial skin microbiomes are remarkably similar at the species level, dominated by Cutibacterium acnes and Staphylococcus epidermidis, yet each person harbors a unique community of strains. Understanding how person-specific communities assemble is critical for designing microbiome-based therapies. Here, using 4,055 isolate genomes and 356 metagenomes, we reconstruct on-person evolutionary history to reveal on- and between-person strain dynamics. We find that multiple cells are typically involved in transmission, indicating ample opportunity for migration. Despite this accessibility, family members share only some of their strains. S. epidermidis communities are dynamic, with each strain persisting for an average of only 2 years. C. acnes strains are more stable and have a higher colonization rate during the transition to an adult facial skin microbiome, suggesting this window could facilitate engraftment of therapeutic strains. These previously undetectable dynamics may influence the design of microbiome therapeutics and motivate the study of their effects on hosts.

PMID:40315837 | DOI:10.1016/j.chom.2025.04.010

Brief Bioinform. 2025 May 1;26(3):bbaf163. doi: 10.1093/bib/bbaf163.

ABSTRACT

Host-directed antivirals offer a promising strategy for addressing the challenge of viral resistance. Virus-host interactions often trigger stage-specific metabolic reprogramming in the host, and the causal links between these interactions and virus-induced metabolic changes provide valuable insights for identifying host targets. In this study, we present a workflow for repurposing host-directed antivirals using virus-induced protein networks. These networks capture the dynamic progression of viral infection by integrating host proteins directly interacting with the virus and enzymes associated with significantly altered metabolic fluxes, identified through dual-species genome-scale metabolic models. This approach reveals numerous hub nodes as potential host targets. As a case study, 50 approved drugs with potential anti-influenza virus A (IVA) activity were identified through eight stage-specific IVA-induced protein networks, each comprising 699-899 hub nodes. Lisinopril, saxagliptin, and gliclazide were further validated for anti-IVA efficacy in vitro through assays measuring the inhibition of cytopathic effects and viral titers in A549 cells infected with IVA PR8. This workflow paves the way for the rapid repurposing of host-directed antivirals.

PMID:40315435 | DOI:10.1093/bib/bbaf163