bioRxiv [Preprint]. 2025 Mar 13:2025.03.11.642711. doi: 10.1101/2025.03.11.642711.

ABSTRACT

Drug delivery platforms, complex lipid nanoparticles (LNPs) and extracellular vesicles (EVs) have both faced a number of key challenges ranging from organ specificity to loading capacity and stability. A key challenge in EV biology as well as LNP design remains vesicle to cell interaction and the creation of a polar permeability pathway necessary for cargo exchange. Membrane to membrane recognition and intercalation are tantamount to delivery and integral to function of both EVs and LNPs, both complex and single component. We reasoned that the overlapping advantages of both nanoparticles centered on compositional lipids. EVs are encapsulations using biological membrane lipids and expressed proteins and have a larger carrier capacity. LNPs are composed of synthetic lipids differing in charge and amount mimicking those present in biological membranes and include a synthetic lipid of choice that carries a charge, designed to enhance biological membrane disruption and subsequent cargo off-loading. Our goal was to design hybrid EVs (HEVs) that combined both elements. We manufactured positively charged liposomes (Lip) carrying mRNA coding for fluorescent proteins to load isolated EVs in order to create a combinatorial delivery platform. Using knowledge from LNP-based mRNA vaccine delivery, we have formulated and characterized HEVs. Future therapeutic strategies could involve isolating EVs from patients, hybridizing them with synthetic lipids loaded with desired payloads, and reintroducing them to the patient. This approach is particularly relevant for enhancing the function of pulmonary innate immunity in diseases like cystic fibrosis, chronic granulomatous disease, and pulmonary fibrosis. By conducting both in-vitro and in-vivo assays, we demonstrate that HEVs exhibit comparable transfection efficacy to LNPs composed of complex synthetic lipids, while significantly reducing cytotoxicity. This highlights their potential as safer and more efficient delivery vehicles.

PMID:40161690 | PMC:PMC11952422 | DOI:10.1101/2025.03.11.642711

Brief Bioinform. 2025 Mar 4;26(2):bbaf129. doi: 10.1093/bib/bbaf129.

ABSTRACT

Kolmogorov-Arnold networks (KANs) emerged as a promising alternative for multilayer perceptrons (MLPs) in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures in the domains of computer vision and natural language processing. Integrating KANs into deep learning models for genomic tasks has not been explored. Here, we tested linear KANs (LKANs) and convolutional KANs (CKANs) as a replacement for MLP in baseline deep learning architectures for classification and generation of genomic sequences. We used three genomic benchmark datasets: Genomic Benchmarks, Genome Understanding Evaluation, and Flipon Benchmark. We demonstrated that LKANs outperformed both baseline and CKANs on almost all datasets. CKANs can achieve comparable results but struggle with scaling over large number of parameters. Ablation analysis demonstrated that the number of KAN layers correlates with the model performance. Overall, linear KANs show promising results in improving the performance of deep learning models with relatively small number of parameters. Unleashing KAN potential in different state-of-the-art deep learning architectures currently used in genomics requires further research.

PMID:40163820 | DOI:10.1093/bib/bbaf129

PLoS One. 2025 Mar 31;20(3):e0318796. doi: 10.1371/journal.pone.0318796. eCollection 2025.

ABSTRACT

In most eukaryotes, mitochondrial organelles contain their own genome, usually circular, which is the remnant of the genome of the ancestral bacterial endosymbiont that gave rise to modern mitochondria. Mitochondrial genomes are dramatically reduced in their gene content due to the process of endosymbiotic gene transfer to the nucleus; as a result most mitochondrial proteins are encoded in the nucleus and imported into mitochondria. This includes the components of the dedicated mitochondrial transcription and replication systems and regulatory factors, which are entirely distinct from the information processing systems in the nucleus. However, since the 1990s several nuclear transcription factors have been reported to act in mitochondria, and previously we identified 8 human and 3 mouse transcription factors (TFs) with strong localized enrichment over the mitochondrial genome using ChIP-seq (Chromatin Immunoprecipitation) datasets from the second phase of the ENCODE (Encyclopedia of DNA Elements) Project Consortium. Here, we analyze the greatly expanded in the intervening decade ENCODE compendium of TF ChIP-seq datasets (a total of 6,153 ChIP experiments for 942 proteins, of which 763 are sequence-specific TFs) combined with interpretative deep learning models of TF occupancy to create a comprehensive compendium of nuclear TFs that show evidence of association with the mitochondrial genome. We find some evidence for chrM occupancy for 50 nuclear TFs and two other proteins, with bZIP TFs emerging as most likely to be playing a role in mitochondria. However, we also observe that in cases where the same TF has been assayed with multiple antibodies and ChIP protocols, evidence for its chrM occupancy is not always reproducible. In the light of these findings, we discuss the evidential criteria for establishing chrM occupancy and reevaluate the overall compendium of putative mitochondrial-acting nuclear TFs.

PMID:40163815 | DOI:10.1371/journal.pone.0318796

J Speech Lang Hear Res. 2025 Mar 31:1-19. doi: 10.1044/2024_JSLHR-24-00347. Online ahead of print.

ABSTRACT

PURPOSE: Phonetic forced alignment has a multitude of applications in automated analysis of speech, particularly in studying nonstandard speech such as children's speech. Manual alignment is tedious but serves as the gold standard for clinical-grade alignment. Current tools do not support direct training on manual alignments. Thus, a trainable speaker adaptive phonetic forced alignment system, Wav2TextGrid, was developed for children's speech. The source code for the method is publicly available along with a graphical user interface at https://github.com/pkadambi/Wav2TextGrid.

METHOD: We propose a trainable, speaker-adaptive, neural forced aligner developed using a corpus of 42 neurotypical children from 3 to 6 years of age. Evaluation on both child speech and on the TIMIT corpus was performed to demonstrate aligner performance across age and dialectal variations.

RESULTS: The trainable alignment tool markedly improved accuracy over baseline for several alignment quality metrics, for all phoneme categories. Accuracy for plosives and affricates in children's speech improved more than 40% over baseline. Performance matched existing methods using approximately 13 min of labeled data, while approximately 45-60 min of labeled alignments yielded significant improvement.

CONCLUSION: The Wav2TextGrid tool allows alternate alignment workflows where the forced alignments, via training, are directly tailored to match clinical-grade, manually provided alignments.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28593971.

PMID:40163771 | DOI:10.1044/2024_JSLHR-24-00347

Oral Dis. 2025 Mar 31. doi: 10.1111/odi.15330. Online ahead of print.

ABSTRACT

OBJECTIVE: To directly compare the diagnostic accuracy of deep learning models with human experts and other diagnostic methods used for the clinical detection of oral cancer.

METHODS: Comparative diagnostic studies involving patients with photographic images of oral mucosal lesions (cancer or non-cancer) were included. Only studies using deep learning methods were eligible. Medline, EMBASE, Scopus, Google Scholar, and ClinicalTrials.gov were searched until September 2024. QUADAS-C assessed the risk of bias. A Bayesian meta-analysis compared diagnostic test accuracy.

RESULTS: Eight studies were included, none of which had a low risk of bias. Three studies compared deep learning versus human experts. The difference in sensitivity favored deep learning by 0.024 (95% CI: -0.093, 0.206), while the difference in specificity favored human experts by -0.041 (95% CI: -0.218, 0.038). Two studies compared deep learning versus postgraduate medical students. The differences in sensitivity and specificity favored deep learning by 0.108 (95% CI: -0.038, 0.324) and by 0.010 (95% CI: -0.119, 0.111), respectively. Both comparisons provided low-level evidence.

CONCLUSIONS: Deep learning models showed comparable sensitivity and specificity to human experts. These models outperformed postgraduate medical students in terms of sensitivity. Prospective clinical trials are needed to evaluate the real-world performance of deep learning models.

PMID:40163741 | DOI:10.1111/odi.15330

Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2416660122. doi: 10.1073/pnas.2416660122. Epub 2025 Mar 31.

ABSTRACT

Skeletal muscles grow substantially during childhood. However, quantitative information about the size of typically developing children's muscles is sparse. Here, the objective was to construct muscle-specific reference curves for lower leg muscle volumes in children aged 5 to 15 y. Volumes of 10 lower leg muscles were measured from magnetic resonance images of 208 typically developing children and 78 ambulant children with cerebral palsy. Deep learning was used to automatically segment the images. Reference curves for typical childhood muscle volumes were constructed with quantile regression. The median total leg muscle volume of a 15-y-old child is nearly five times that of a 5-y-old child. Between the ages of 5 and 15, boys typically have larger muscles than girls, both in absolute terms (medians are greater by 5 to 20%) and per unit of body weight (1 to 13%). Muscle volumes vary widely between children of a particular age: the range of volumes for the central 80% of the distribution (i.e., between the 10th and 90th centiles) is more than 40% of the median volume. Reference curves for individual muscle volumes have a similar shape to reference curves for total lower leg muscle volume. Confidence bands about the centile curves were wide, especially at the youngest and oldest ages. Nonetheless, the reference curves can be used with confidence to identify small-for-age muscles (centile < 10). We show that 56% of children with cerebral palsy in our cohort had total lower leg muscle volumes that were small-for-age and that 80% had at least one lower leg muscle that was small-for-age.

PMID:40163724 | DOI:10.1073/pnas.2416660122

Inflamm Bowel Dis. 2025 Mar 31:izaf050. doi: 10.1093/ibd/izaf050. Online ahead of print.

ABSTRACT

BACKGROUND: Endoscopic scoring indices for ulcerative colitis and Crohn's disease are subject to inter-endoscopist variability. There is increasing interest in the development of deep learning models to standardize endoscopic assessment of intestinal diseases. Here, we summarize and critically appraise the literature on artificial intelligence-assisted endoscopic characterization of inflammatory bowel disease severity.

METHODS: A systematic search of Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and IEEE Xplore was performed to identify reports of AI systems used for endoscopic severity classification of IBD. Selected studies were critically appraised for methodological and reporting quality using APPRAISE-AI.

RESULTS: Thirty-one studies published between 2019 and 2024 were included. Of 31 studies, 28 studies examined endoscopic classification of ulcerative colitis and 3 examined Crohn's disease. Researchers sought to accomplish a wide range of classification tasks, including binary and multilevel classification, based on still images or full-length colonoscopy videos. Overall scores for study quality ranged from 41 (moderate quality) to 64 (high quality) out of 100, with 28 out of 31 studies within the moderate quality range. The highest-scoring domains were clinical relevance and reporting quality, while the lowest-scoring domains were robustness of results and reproducibility.

CONCLUSIONS: Multiple AI models have demonstrated the potential for clinical translation for ulcerative colitis. Research concerning the endoscopic severity assessment of Crohn's disease is limited and should be further explored. More rigorous external validation of AI models and increased transparency of data and codes are needed to improve the quality of AI studies.

PMID:40163659 | DOI:10.1093/ibd/izaf050

JMIR Rehabil Assist Technol. 2025 Feb 18. doi: 10.2196/69457. Online ahead of print.

ABSTRACT

BACKGROUND: Effective communication is essential for human interaction, yet infants can only express their needs through various types of suggestive cries. Traditional approaches of interpreting infant cries are often subjective, inconsistent, and slow leaving gaps in timely, precise caregiving responses. A precise interpretation of infant cries can potentially provide valuable insights into the infant's health, needs, and well-being, enabling prompt medical or caregiving actions.

OBJECTIVE: This study seeks to systematically review the advancements in methods, coverage, deployment schemes, and applications of infant cry classification over the last 24 years. The review focuses on the different infant cry classification techniques, feature extraction methods, and the practical applications. Furthermore, we aimed to identify recent trends and directions in the field of infant cry signal processing to address both academic and practical needs.

METHODS: A systematic literature review was conducted by using nine electronic databases: Cochrane Database of Systematic Reviews, JSTOR, Web of Science Core Collection, Scopus, PubMed, ACM, MEDLINE, IEEE Xplore, and Google Scholar. A total of 5904 search results were initially retrieved, with 126 studies meeting the eligibility criteria after screening by two independent reviewers. The methodological quality of the studies was assessed using the Cochrane risk-of-bias tool version 2 (RoB2), with 92% (n=116) of the studies indicating a low risk of bias and 8% (n=10) of the studies showing some concerns regarding bias. The overall quality assessment was performed using the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines. The data analysis was conducted using R version 3.64.

RESULTS: Notable advancements in infant cry classification methods were realized, particularly from 2019 onwards employing machine learning, deep learning, and hybrid approaches. Common audio features included Mel-frequency cepstral coefficients (MFCCs), spectrograms, pitch, duration, intensity, formants, zero-crossing rate and chroma. Deployment methods included mobile applications and web-based platforms for real-time analysis with 90% (n=113) of the remaining models remained undeployed to real world applications. Denoising techniques and federated learning were limitedly employed to enhance model robustness and ensure data confidentiality from 5% (n=6) of the studies. Some of the practical applications spanned healthcare monitoring, diagnostics, and caregiver support.

CONCLUSIONS: The evolution of infant cry classification methods has progressed from traditional classical statistical methods to machine learning models but with minimal considerations of data privacy, confidentiality, and ultimate deployment to the practical use. Further research is thus proposed to develop standardized foundational audio multimodal approaches, incorporating a broader range of audio features and ensuring data confidentiality through methods such as federated learning. Furthermore, a preliminary layer is proposed for denoising the cry signal before the feature extractions stage. These improvements will enhance the accuracy, generalizability, and practical applicability of infant cry classification models in diverse healthcare settings.

PMID:40163619 | DOI:10.2196/69457

Nano Lett. 2025 Mar 31. doi: 10.1021/acs.nanolett.5c00787. Online ahead of print.

ABSTRACT

Machine learning (ML) with in-situ diagnostics offers a transformative approach to accelerate, understand, and control thin film synthesis by uncovering relationships between synthesis conditions and material properties. In this study, we demonstrate the application of deep learning to predict the stoichiometry of Sr2xTi2(1-x)O3 thin films using reflection high-energy electron diffraction images acquired during pulsed laser deposition. A gated convolutional neural network trained for regression of the Sr atomic fraction achieved accurate predictions with a small dataset of 31 samples. Explainable AI techniques revealed a previously unknown correlation between diffraction streak features and cation stoichiometry in Sr2xTi2(1-x)O3 thin films. Our results demonstrate how ML can be used to transform a ubiquitous in-situ diagnostic tool, that is usually limited to qualitative assessments, into a quantitative surrogate measurement of continuously valued thin film properties. Such methods are critically needed to enable real-time control, autonomous workflows, and accelerate traditional synthesis approaches.

PMID:40163590 | DOI:10.1021/acs.nanolett.5c00787

PLoS Comput Biol. 2025 Mar 31;21(3):e1012905. doi: 10.1371/journal.pcbi.1012905. Online ahead of print.

ABSTRACT

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

PMID:40163555 | DOI:10.1371/journal.pcbi.1012905

PLoS One. 2025 Mar 31;20(3):e0317726. doi: 10.1371/journal.pone.0317726. eCollection 2025.

ABSTRACT

Student dropout is a significant social issue with extensive implications for individuals and society, including reduced employability and economic downturns, which, in turn, drastically influence social sustainable development. Identifying students at high risk of dropping out is a major challenge for sustainable education. While existing machine learning and deep learning models can effectively predict dropout risks, they often rely on real student data, raising ethical concerns and the risk of information leakage. Additionally, the poor interpretability of these models complicates their use in educational management, as it is difficult to justify identifying a student as high-risk based on an opaque model. To address these two issues, we introduced for the first time a modified Preprocessed Kernel Inducing Points data distillation technique (PP-KIPDD), specializing in distilling tabular structured dataset, and innovatively employed the PP-KIPDD to reconstruct new samples that serve as qualified training sets simulating student information distributions, thereby preventing student privacy information leakage, which showed better performance and efficiency compared to traditional data synthesis techniques such as the Conditional Generative Adversarial Networks. Furthermore, we empower the classifiers credibility by enhancing model interpretability utilized SHAP (SHapley Additive exPlanations) values and elucidated the significance of selected features from an educational management perspective. With well-explained features from both quantitative and qualitative aspects, our approach enhances the feasibility and reasonableness of dropout predictions using machine learning techniques. We believe our approach represents a novel end-to-end framework of artificial intelligence application in the field of sustainable education management from the view of decision-makers, as it addresses privacy leakage protection and enhances model credibility for practical management implementations.

PMID:40163446 | DOI:10.1371/journal.pone.0317726

PLoS Comput Biol. 2025 Mar 31;21(3):e1012927. doi: 10.1371/journal.pcbi.1012927. Online ahead of print.

ABSTRACT

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

PMID:40163550 | DOI:10.1371/journal.pcbi.1012927

J Vis Exp. 2025 Mar 14;(217). doi: 10.3791/67925.

ABSTRACT

Pure bacterial cultures are essential for the study of microbial culturomics. Traditional methods based on solid plates, well plates, and micro-reactors are hindered by cumbersome procedures and low throughput, impeding the rapid progress of microbial culturomics research. To address these challenges, we had successfully developed the Single-cell Microliter-droplet Culture Omics System (MISS cell), an automated high-throughput platform that utilizes droplet microfluidic technology for microbial monoclonal isolation, cultivation, and screening. This system can generate a large number of single-cell droplets and cultivate, screen, and collect monoclonal colonies in a short time, facilitating an integrated process from microbial isolation to picking. In this protocol, we demonstrated its application using the isolation and cultivation of human gut microbiota as an example and compared the microbial isolation efficiency, monoclonal culture performance, and screening throughput using the solid-plate culture method. The experimental workflow was simple, and reagent consumption was very low. Compared to solid-plate culture methods, the MISS cell could cultivate a greater diversity of gut microbiota species, offering significant potential and value for microbial culturomics research.

PMID:40163395 | DOI:10.3791/67925

Adv Sci (Weinh). 2025 Mar 31:e2414918. doi: 10.1002/advs.202414918. Online ahead of print.

ABSTRACT

The early detection of high-risk human papillomavirus (HR-HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However, existing PCR-based screening methods suffer from inadequate accessibility, which dampens the enthusiasm for screening among grassroots populations, especially in resource-limited areas, and contributes to the persistently high mortality rate of cervical cancer. Here, a portable system is proposed for multiplexed nucleic acid detection, termed R-CHIP, that integrates Recombinase polymerase amplification (RPA), CRISPR detection, Hand-driven microfluidics, and an artificial Intelligence Platform. The system can go from sample pre-processing to results readout in less than an hour with simple manual operation. Optimized for sensitivity of 10-17 M for HPV-16 and 10-18 M for HPV-18, R-CHIP has an accuracy of over 95% in 300 tests on clinical samples. In addition, a smartphone microimaging system combined with the ResNet-18 deep learning model is used to improve the readout efficiency and convenience of the detection system, with initial prediction accuracies of 96.0% and 98.0% for HPV-16 and HPV-18, respectively. R-CHIP, as a user-friendly and intelligent detection platform, has great potential for community-level HR-HPV screening in resource-constrained settings, and contributes to the prevention and early diagnosis of other diseases.

PMID:40163382 | DOI:10.1002/advs.202414918

Plant J. 2025 Apr;122(1):e70122. doi: 10.1111/tpj.70122.

ABSTRACT

We identified a mutant of hexaploid wheat (Triticum aestivum) with impaired responses to gravity. The mutant, named Twisted Sister1 (TS1), had agravitropic roots that were often twisted along with altered shoot phenotypes. Roots of TS1 were insensitive to externally applied auxin, with the genetics and physiology suggestive of a mutated AUX/IAA transcription factor gene. Hexaploid wheat possesses over 80 AUX/IAA genes, and sequence information did not identify an obvious candidate. Bulked segregant analysis of an F2 population mapped the mutation to chromosome 5A, and subsequent mapping located the mutation to a 41 Mbp region. RNA-seq identified the TraesCS5A03G0149800 gene encoding a TaAUX/IAA protein to be mutated in the highly conserved domain II motif. We confirmed TraesCS5A03G0149800 as underlying the mutant phenotype by generating transgenic Arabidopsis thaliana. Analysis of RNA-seq data suggested broad similarities between Arabidopsis and wheat for the role of AUX/IAA genes in gravity responses, although there were marked differences. Here we show that the sequenced wheat genome, along with previous knowledge of the physiology of gravity responses from other plant species, gene mapping, RNA-seq, and expression in Arabidopsis have enabled the cloning of a key wheat gene that defines plant architecture.

PMID:40162979 | DOI:10.1111/tpj.70122

Int J Immunopathol Pharmacol. 2025 Jan-Dec;39:3946320251324401. doi: 10.1177/03946320251324401. Epub 2025 Mar 31.

ABSTRACT

OBJECTIVE: This study aimed to investigate the relationship between fibroblast growth factor 19 (FGF19) and the prognosis and immune infiltration of colorectal cancer (CRC) and identify the related genes and pathways influencing the onset and progression of CRC.

INTRODUCTION: The potential of FGF19 to guide the prognosis of CRC and inform immunotherapeutic strategies warrants further investigation.

METHODS: We performed Quantitative Real-Time PCR to assess the expression of FGF19 and conducted a bioinformatics analysis to evaluate the impact of FGF19 expression on the clinical prognosis of CRC. We also analyzed the association between FGF19 expression and immune cell infiltration in CRC, and explored the related genes and pathways through which FGF19 influences CRC development.

RESULTS: CRC patients with higher FGF19 expression exhibited a poorer prognosis. In terms of the Receiver Operating Characteristic (ROC), FGF19 achieved an area under the curve (AUC) of 0.904. FGF19 expression correlated with the N stage, M stage, and pathological stage in patients with CRC. Functional enrichment analysis revealed significant enrichment of FGF19 in pathways associated with tumor development. ssGSEA and Spearman correlation analysis demonstrated that FGF19 expression was linked to tumor immune cells. We discovered that FGF19 is closely related to neutrophil extracellular traps (NETs), which play a significant role in the immune microenvironment.

CONCLUSION: FGF19 is a key gene associated with immunity and prognosis in CRC patients. Our findings suggest that FGF19 may influence CRC progression by promoting NETs expression, which leads to suppression of immune cells.

PMID:40162957 | DOI:10.1177/03946320251324401

Biomacromolecules. 2025 Mar 31. doi: 10.1021/acs.biomac.4c01798. Online ahead of print.

ABSTRACT

Biomedical device-related bacterial infections are a leading cause of mortality, and traditional antibiotics contribute to resistance. Various surface modification strategies have been explored, but effective clinical solutions remain limited. This study introduces a novel antibacterial nanocoating with copper nanoparticles (CuNPs) that triggers localized nitric oxide (NO) release. The multilayered nanocoating is created using branched polyethylenimine (BPEI) and poly(acrylic acid) (PAA) via a Layer-by-Layer assembly method. CuNP-decorated nanocoatings are formed by reducing copper ions coordinated with amine/carboxylic acid groups. In a physiological environment, CuNPs oxidize to Cu(I), promoting NO release from endogenous NO donors. The nanocoating's thickness is adjustable to regulate amount of CuNPs and NO flux. The optimal thickness for effective NO release against Staphylococcus aureus and Pseudomonas aeruginosa is identified, preventing microbial adhesion and biofilm formation. Importantly, the coating remains cytocompatible due to minimal CuNPs, physiological NO levels, and stable coating properties under physiological conditions.

PMID:40162566 | DOI:10.1021/acs.biomac.4c01798

Netw Neurosci. 2025 Mar 5;9(1):237-258. doi: 10.1162/netn_a_00433. eCollection 2025.

ABSTRACT

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

PMID:40161996 | PMC:PMC11949581 | DOI:10.1162/netn_a_00433

Netw Neurosci. 2025 Mar 5;9(1):159-180. doi: 10.1162/netn_a_00424. eCollection 2025.

ABSTRACT

Our brains operate as a complex network of interconnected neurons. To gain a deeper understanding of this network architecture, it is essential to extract simple rules from its intricate structure. This study aimed to compress and simplify the architecture, with a particular focus on interpreting patterns of functional connectivity in 2.5 hr of electrical activity from a vast number of neurons in acutely sliced mouse brains. Here, we combined two distinct methods together: automatic compression and network analysis. Firstly, for automatic compression, we trained an artificial neural network named NNE (neural network embedding). This allowed us to reduce the connectivity to features, be represented only by 13% of the original neuron count. Secondly, to decipher the topology, we concentrated on the variability among the compressed features and compared them with 15 distinct network metrics. Specifically, we introduced new metrics that had not previously existed, termed as indirect-adjacent degree and neighbor hub ratio. Our results conclusively demonstrated that these new metrics could better explain approximately 40%-45% of the features. This finding highlighted the critical role of NNE in facilitating the development of innovative metrics, because some of the features extracted by NNE were not captured by the currently existed network metrics.

PMID:40161994 | PMC:PMC11949542 | DOI:10.1162/netn_a_00424

Front Immunol. 2025 Mar 14;16:1536003. doi: 10.3389/fimmu.2025.1536003. eCollection 2025.

ABSTRACT

The T cell receptor (TCR) is crucial for immune responses and represents a pivotal therapeutic target for CAR T cell therapies. However, which enhancer elements drive the constitutive expression of the TCRα chain in mature, peripheral T cells has not been well defined. Earlier work has suggested that enhancer alpha is inactive in mature peripheral T cells and that an alternative enhancer element in the 5' J region was driving TRA expression, while more recent findings indicated the opposite. Here, we applied a pooled CRISPR screen to probe a large genomic region proximal to the human TRA gene for the presence of regulatory elements. Interestingly, no sgRNA targeting the 5' J region was identified that influenced TRA expression. In contrast, several sgRNAs targeting enhancer alpha element Tα2, were identified that compromised the expression of the TCRα chain in Jurkat E6.1, as well as in a subset of human primary T cells. Our results provide new insights into the regulation of TRA in human peripheral T cells, advancing our understanding of how constitutive TRA expression is driven and regulated.

PMID:40160815 | PMC:PMC11949936 | DOI:10.3389/fimmu.2025.1536003