Nat Biotechnol. 2025 May 7. doi: 10.1038/s41587-025-02655-3. Online ahead of print.

ABSTRACT

Naturally existing enzymes have been adapted for a variety of molecular technologies, with enhancements or modifications to the enzymes introduced to improve the desired function; however, it is difficult to engineer variants with enhanced activity while maintaining specificity. Here we engineer the compact Obligate Mobile Element Guided Activity (OMEGA) RNA-guided endonuclease IscB and its guiding RNA (ωRNA) by combining ortholog screening, structure-guided protein domain design and RNA engineering, and deep learning-based structure prediction to generate an improved variant, NovaIscB. We show that the compact NovaIscB achieves up to 40% indel activity (~100-fold improvement over wild-type OgeuIscB) on the human genome with improved specificity relative to existing IscBs. We further show that NovaIscB can be fused with a methyltransferase to create a programmable transcriptional repressor, OMEGAoff, that is compact enough to be packaged in a single adeno-associated virus vector for persistent in vivo gene repression. This study highlights the power of combining natural diversity with protein engineering to design enhanced enzymes for molecular biology applications.

PMID:40335752 | DOI:10.1038/s41587-025-02655-3

Plant Cell Rep. 2025 May 8;44(6):115. doi: 10.1007/s00299-025-03498-7.

ABSTRACT

Using deep learning-based image restoration, we achieved high-resolution 4D imaging with minimal photodamage, revealing distinct localization and suggesting Lifeact-RFP-labeled actin microfilaments play a role in initiating cell plate formation. Phragmoplasts are plant-specific intracellular structures composed of microtubules, actin microfilaments (AFs), membranes, and associated proteins. Importantly, they are involved in the formation and the expansion of cell plates that partition daughter cells during cell division. While previous studies have revealed the important role of cytoskeletal dynamics in the proper functioning of the phragmoplast, the localization and the role of AFs in the initial phase of cell plate formation remain controversial. Here, we used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage, enabling us to investigate the dynamics of AFs during the initial phase of cell plate formation in transgenic tobacco BY-2 cells labeled with Lifeact-RFP or RFP-ABD2 (actin-binding domain 2). This computational approach overcame the limitation of conventional imaging, namely laser-induced photobleaching and phototoxicity. The restored images indicated that RFP-ABD2-labeled AFs were predominantly localized near the daughter nucleus, whereas Lifeact-RFP-labeled AFs were found not only near the daughter nucleus but also around the initial cell plate. These findings, validated by imaging with a long exposure time, highlight distinct localization patterns between the two AF probes and suggest that Lifeact-RFP-labeled AFs play a role in initiating cell plate formation.

PMID:40335746 | DOI:10.1007/s00299-025-03498-7

Nature. 2025 May 7. doi: 10.1038/s41586-025-08985-1. Online ahead of print.

ABSTRACT

The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.

PMID:40335689 | DOI:10.1038/s41586-025-08985-1

Nature. 2025 May 7. doi: 10.1038/s41586-025-08973-5. Online ahead of print.

ABSTRACT

Despite recent advances in understanding disease biology, treatment of group 3/4 medulloblastoma remains a therapeutic challenge in paediatric neuro-oncology1. Bulk-omics approaches have identified considerable intertumoural heterogeneity in group 3/4 medulloblastoma, including the presence of clear single-gene oncogenic drivers in only a subset of cases, whereas in most cases, large-scale copy number aberrations prevail2,3. However, intratumoural heterogeneity, the role of oncogene aberrations, and broad copy number variation in tumour evolution and treatment resistance remain poorly understood. To dissect this interplay, we used single-cell technologies (single-nucleus RNA sequencing (snRNA-seq), single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC-seq) and spatial transcriptomics) on a cohort of group 3/4 medulloblastoma with known alterations in the oncogenes MYC, MYCN and PRDM6. We show that large-scale chromosomal aberrations are early tumour-initiating events, whereas the single-gene oncogenic events arise late and are typically subclonal, but MYC can become clonal upon disease progression to drive further tumour development and therapy resistance. Spatial transcriptomics shows that the subclones are mostly interspersed across tumour tissue, but clear segregation is also present. Using a population genetics model, we estimate medulloblastoma initiation in the cerebellar unipolar brush cell lineage starting from the first gestational trimester. Our findings demonstrate how single-cell technologies can be applied for early detection and diagnosis of this fatal disease.

PMID:40335697 | DOI:10.1038/s41586-025-08973-5

Nature. 2025 May 7. doi: 10.1038/s41586-025-08971-7. Online ahead of print.

ABSTRACT

The eukaryotic genome is packed into nucleosomes of 147 base pairs around a histone core and is organized into euchromatin and heterochromatin, corresponding to the A and B compartments, respectively1,2. Here we investigated whether individual nucleosomes contain sufficient information for 3D genomic organization into compartments, for example, in their biophysical properties. We purified native mononucleosomes to high monodispersity and used physiological concentrations of polyamines to determine their condensability. The chromosomal regions known to partition into A compartments have low condensability and those for B compartments have high condensability. Chromatin polymer simulations using condensability as the only input, without any trans factors, reproduced the A/B compartments. Condensability is also strongly anticorrelated with gene expression, particularly near the promoters and in a cell type-dependent manner. Therefore, mononucleosomes have biophysical properties associated with genes being on or off. Comparisons with genetic and epigenetic features indicate that nucleosome condensability is an emergent property, providing a natural axis on which to project the high-dimensional cellular chromatin state. Analysis using various condensing agents or histone modifications and mutations indicates that the genome organization principle encoded into nucleosomes is mostly electrostatic in nature. Polyamine depletion in mouse T cells, resulting from either knocking out or inhibiting ornithine decarboxylase, results in hyperpolarized condensability, indicating that when cells cannot rely on polyamines to translate the biophysical properties of nucleosomes to 3D genome organization, they accentuate condensability contrast, which may explain the dysfunction observed with polyamine deficiency3-5.

PMID:40335690 | DOI:10.1038/s41586-025-08971-7

Sci Rep. 2025 May 7;15(1):15886. doi: 10.1038/s41598-025-00817-6.

ABSTRACT

Isoform switching in cancer is a prevalent phenomenon with significant implications for immunotherapy, as actionable neoantigens derived from these cancer-specific events would be applicable to broad categories of patients, reducing the necessity for personalized treatments. By integrating five large-scale transcriptomic datasets comprising over 19,500 samples across 29 cancer and 54 normal tissue types, we identified cancer-associated isoform switching events common to multiple cancer types, several of which involve genes with established mechanistic roles in oncogenesis. The presence of neoantigen-containing peptides derived from these transcripts was confirmed in broad cancer and normal tissue proteome datasets and the binding affinity of predicted neoantigens to the human leukocyte antigen (HLA) complex via molecular dynamics simulations. The study presents strong evidence that isoform switching in cancer is a significant source of actionable neoantigens that have the capability to trigger an immune response. These findings suggest that isoform switching events could potentially be leveraged for broad immunotherapeutic strategies across various cancer types.

PMID:40335513 | DOI:10.1038/s41598-025-00817-6

Nat Commun. 2025 May 7;16(1):4260. doi: 10.1038/s41467-025-59525-4.

ABSTRACT

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N ~ 408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N ~ 40,466), where the genetically most variable individuals had increased conventional PGS accuracy (by ~19%) relative to the genetically least variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

PMID:40335489 | DOI:10.1038/s41467-025-59525-4

Trends Biotechnol. 2025 May 6:S0167-7799(25)00134-9. doi: 10.1016/j.tibtech.2025.04.007. Online ahead of print.

ABSTRACT

Given the crucial role of water-soluble vitamins in the human body and the rising demand for natural sources, their biosynthesis has gained the attention of researchers. This review offers a comprehensive look at recent progress in water-soluble vitamin biosynthesis, emphasizing synthetic biotechnology for green biomanufacturing. Specifically, it encompasses the optimization of biological components, pathways, and systems, as well as energy metabolism regulation, stress-tolerance enhancement, high-throughput screening, and the upscaling of production processes. It also envisages intelligent biomanufacturing platforms, highlighting the role of systems biology and artificial intelligence (AI), and proposes future research directions, such as integrating AI-driven metabolic models, enzyme engineering, and cell-free systems, to address limitations in the efficiency, toxicity, and scalability of water-soluble vitamin production.

PMID:40335344 | DOI:10.1016/j.tibtech.2025.04.007

Int J Biol Macromol. 2025 May 5:143949. doi: 10.1016/j.ijbiomac.2025.143949. Online ahead of print.

ABSTRACT

Porous starch (PS) has been utilized as an oral protective carrier to enhance the oxidative stability of liposoluble nutrients. However, PS releases more glucose during digestion, thereby increasing the risk of chronic diseases. Chlorogenic acid (CA) has excellent antioxidant properties and enhances the starch digestion resistance. To simultaneously enhance the oxidative protection and anti-digestibility, PS was blended with CA. Morphological analysis revealed that PSs with pores absorbed liposoluble substances. Surface area, total pore volume, and oxidative stability analyses demonstrated that rice starch (RS) enzymatically hydrolyzed for 12 h (PS12) loaded more substances and exerted a better protective effect in cooperation with CA. Simulated digestion confirmed that PS12-CA1 had the best anti-digestibility among PS12-CAs and a similar digestibility as RS. Additionally, CA treatment resulted in more anti-digestive V-type crystals in PSs, which resisted digestion. This study showed that the combination of PS and CA simultaneously enhanced oxidative protection and reduced the digestibility of PS. Thus, CA treatment makes PS a better oral nutrient delivery.

PMID:40334898 | DOI:10.1016/j.ijbiomac.2025.143949

Bioresour Technol. 2025 May 5:132620. doi: 10.1016/j.biortech.2025.132620. Online ahead of print.

ABSTRACT

Fermented food remains poorly understood, largely due to the lack of knowledge about microbes in food fermentation. Here, this study constructed Moutai Fermented Grain Catalog (MTFGC), a representative liquor fermented by one of the most complex fermentations. MTFGC comprised 8,379,551 non-redundant genes and 5,159 metagenome-assembled genomes, with 20% species and 20% genes being novel. Additionally, 25,625 biosynthetic gene clusters (BGCs) and 28 BGC-enriched species were identified. Moreover, the microbial community assembly was deterministic, with significant species and gene changes in early fermentation stages, while stabilizing in later stages. Further BGC-knockout experiments verified Bacillus licheniformis, a BGC-enriched species, employed its BGCs for synthesizing the aroma-related lipopeptide lichenysin. This study has established the largest genetic resource for fermented food, uncovering its uniqueness and high metabolic potential. These findings facilitate the transition potential from traditional fermentation to precision-driven synthetic biology in food systems.

PMID:40334798 | DOI:10.1016/j.biortech.2025.132620

J Lipid Res. 2025 May 5:100820. doi: 10.1016/j.jlr.2025.100820. Online ahead of print.

ABSTRACT

BACKGROUND: Elevated levels of lipoprotein (a) [Lp(a)], an apolipoprotein B particle, are causally linked to atherosclerotic cardiovascular disease (ASCVD). Lp(a) is thought to promote ASCVD through multiple mechanisms, including its effects on cholesterol transport, inflammation, and thrombosis.

OBJECTIVE: Define the mechanisms that integrate Lp(a)-mediated cholesterol accumulation, inflammation, and thrombosis.

METHODS: In this study, we employed systems biology approaches, including proteomics, transcriptomics, and mass cytometry, to define the immune cellular and molecular phenotypes in ASCVD subjects with high and low Lp(a) levels and the molecular mechanisms through which Lp(a) mediates monocyte-driven inflammation and thrombosis.

RESULTS: In 64 stable ASCVD subjects (41 with high Lp(a) [median Lp(a) 228.7 nmol/L] and 23 with low Lp(a) [median Lp(a) 17.8 nmol/L]), we found that circulating markers of inflammation (CCL28, IL-17D) and vascular dysfunction (tissue factor [TF]; 6.4 vs 5.7 normalized protein expression (NPX); p=0.01) were elevated in subjects with high Lp(a) levels compared with those with low Lp(a) levels. Although total monocyte and hsCRP levels were similar between the groups, CD14+ monocytes from ASCVD subjects with an elevated Lp(a) were primed and expressed more TF at baseline and in response to stress. Mechanistically, we found that Lp(a) itself can activate monocytes through Toll-like receptor 2 (TLR2) and nuclear factor kappa B (NFκB) signaling, driving both the induction of TF and TF activity.

CONCLUSIONS: Overall, these studies are the first to link Lp(a) to monocyte-mediated inflammation and thrombosis. They demonstrate a novel mechanism through TLR2, NFκB, and monocyte TF by which Lp(a) amplifies immunothrombotic risk.

PMID:40334781 | DOI:10.1016/j.jlr.2025.100820

Metab Eng. 2025 May 5:S1096-7176(25)00075-8. doi: 10.1016/j.ymben.2025.04.011. Online ahead of print.

ABSTRACT

Genetically engineered microbes are increasingly utilized to produce a broad range of high-value compounds. However, most studies start with only a very narrow group of genetically tractable type strains that have not been selected for maximum titers or industrial robustness. In this study, we used high-throughput screening and parallel metabolic engineering to identify and optimize Saccharomyces cerevisiae chassis strains for the production of limonene, a monoterpene with applications in flavors, fragrances, and biofuels. We screened 921 genetically and phenotypically distinct S. cerevisiae strains for limonene tolerance and lipid content to identify optimal chassis strains for precision fermentation of limonene. In parallel, we also evaluated 16 different plant limonene synthases. Our results revealed that two of the selected strains showed approximately a 2-fold increase in titers compared to CEN.PK2-1C, the type strain that is often used as a chassis for limonene production, with the same genetic modifications in the mevalonate pathway. Intriguingly, the most effective engineering strategy proved strain-specific. Metabolic profiling revealed that this difference is likely explained by differences in native mevalonate production. Ultimately, by using strain-specific engineering strategies, we achieved 844 mg/L in a new strain, 40% higher than the titer (605 mg/L) achieved by CEN.PK2-1C. Our findings demonstrate the potential of leveraging genetic diversity in S. cerevisiae for monoterpene bioproduction and highlight the necessity for tailoring metabolic engineering strategies to specific strains.

PMID:40334774 | DOI:10.1016/j.ymben.2025.04.011

PLoS One. 2025 May 7;20(5):e0322546. doi: 10.1371/journal.pone.0322546. eCollection 2025.

ABSTRACT

Proteins are fundamental biomolecules composed of one or more chains of amino acids. They are essential for all living organisms, contributing to various biological functions and regulatory processes. Alterations in protein structures and functions are closely linked to diseases, emphasizing the need for in-depth study. A thorough understanding of these associations is crucial for developing targeted and more effective therapeutic strategies.Computational analyses of biomedical data facilitate the identification of specific patterns in proteins associated with diseases, providing novel insights into their biological roles. This study introduces a computational approach designed to detect relevant sequence patterns within proteins. These patterns, characterized by specific amino acid arrangements, can be critical for protein functionality. The proposed methodology was applied to proteins targeted by drugs used in lung cancer treatment, a disease that remains the leading cause of cancer-related mortality worldwide. Given that non-small cell lung cancer represents 85-90% of all lung cancer cases, it was selected as the primary focus of this study.Significant sequence patterns were identified, establishing connections between drug-target proteins and proteins associated with lung cancer. Based on these findings, a novel computational framework was developed to extend this pattern-based analysis to proteins linked to other diseases. By employing this approach, relationships between lung cancer drug-target proteins and proteins associated with four additional cancer types were uncovered. These associations, characterized by shared amino acid sequence features, suggest potential opportunities for drug repurposing. Furthermore, validation through an extensive literature review confirmed biological links between lung cancer drug-target proteins and proteins related to other malignancies, reinforcing the potential of this methodology for identifying new therapeutic applications.

PMID:40334012 | DOI:10.1371/journal.pone.0322546

Molecules. 2025 Apr 13;30(8):1736. doi: 10.3390/molecules30081736.

ABSTRACT

Alzheimer's disease (AD) affects an increasing number of people as the human population ages. The main pathological feature of AD, amyloid plaques, consists of the key protein amyloid-β and other co-deposited proteins. These co-deposited proteins and their protein interactors could hold some additional functional insights into AD pathophysiology. For this work, proteins found on amyloid plaques were collected from the AmyCo database. A protein-protein and protein-drug interaction network was constructed with data from the IntAct and DrugBank databases, respectively. In total, there were 12 proteins co-deposited on amyloid plaques that reportedly interact with 513 other proteins and are targets of 72 drugs. These drugs were shown to be almost entirely distinct from the panel of drugs currently approved by the FDA for AD and their corresponding protein targets. In conclusion, this work demonstrates the potential for drug repurposing of drugs that target proteins found in amyloid plaques.

PMID:40333680 | DOI:10.3390/molecules30081736

Talanta. 2025 May 5;294:128278. doi: 10.1016/j.talanta.2025.128278. Online ahead of print.

ABSTRACT

The early diagnosis of diseases largely relies on the monitoring and accurate detection of biomarkers within biological systems. The quantification of chloride ions (Cl-) and glutathione (GSH) can effectively assess the progression of diseases such as cystic fibrosis and cancer, as well as the alterations in the body's internal environment. However, developing reliable sensing platforms with high sensitivity and selectivity poses significant challenges. Based on the dual-functional silver-based metal-organic frameworks (Ag MOF), an electrochemical/electrochemiluminescent (EC/ECL) dual-channel nanoplatform was developed for the detection of Cl- and GSH, aided by graphitic carbon nitride (g-C3N4). In the EC mode, the interaction between Ag MOF and Cl- leads to the formation of silver chloride (AgCl), which is characterized by an increased peak current of AgCl solid-state electrochemistry as Cl- concentration rises. The further introduction of GSH generates a non-electroactive complex through competition with Cl-, resulting in a decrease in the peak current of AgCl. In the ECL mode, the quenching of ECL signals from g-C3N4 by Ag MOF is alleviated by Cl-, due to the etching of the Ag-MOF. The ECL recovery effect is further enhanced with the addition of GSH. For Cl-, both EC and ECL responses exhibit good linear relationships with concentrations ranging from 0.5 to 10 mM, with detection limit (LOD) of 0.4 mM and 0.1 mM, respectively. For GSH, EC and ECL also show good linear relationships in range of 0.01-100 μM, with LOD of 9.8 nM and 1.02 nM. The unique properties of Ag MOF, acting both as an electrochemical sensing component that generates sensitive current outputs for Cl- and GSH, and as a quencher for the ECL of g-C3N4, facilitate the sequential detection of Cl- and GSH, providing mutual validation that significantly enhances accuracy and reliability. The specific interactions of Ag MOF with these analytes offer the innovative platform good selectivity, demonstrating significant potential for advancements in biological analysis and disease diagnosis.

PMID:40334507 | DOI:10.1016/j.talanta.2025.128278

PLoS One. 2025 May 7;20(5):e0311711. doi: 10.1371/journal.pone.0311711. eCollection 2025.

ABSTRACT

Arsenic is associated with lung disease and experimental models suggest that arsenic-induced degradation of the chloride channel CFTR (cystic fibrosis transmembrane conductance regulator) is a mechanism of arsenic toxicity. We examined associations between arsenic exposure, sweat chloride concentration (measure of CFTR function), and pulmonary function among 269 adults in Bangladesh. Participants with sweat chloride ≥ 60 mmol/L had higher arsenic exposures than those with sweat chloride < 60 mmol/L (water: median 77.5 µg/L versus 34.0 µg/L, p = 0.025; toenails: median 4.8 µg/g versus 3.7 µg/g, p = 0.024). In linear regression models, a one-unit µg/g increment in toenail arsenic was associated with a 0.59 mmol/L higher sweat chloride concentration, p < 0.001. Among the entire study population, after adjusting for covariates including age, sex, smoking, education, and height, toenail arsenic concentration was associated with increased odds of airway obstruction (OR: 1.97, 95%: 1.06, 3.67, p = 0.03); however, sweat chloride concentration did not mediate this association. Our findings suggest that sweat chloride concentration may serve as novel biomarker for arsenic exposure, warranting further investigation in diverse populations, and that arsenic likely acts on the lung through mechanisms other than inducing CFTR dysfunction. Alternative mechanisms by which environmental arsenic exposure may lead to obstructive lung disease, such as arsenic-induced direct lung injury and/or increase lung proteinase activity, require additional exploration in future work.

PMID:40333927 | DOI:10.1371/journal.pone.0311711

Pathogens. 2025 Apr 15;14(4):387. doi: 10.3390/pathogens14040387.

ABSTRACT

This study analyzed eleven isolates of colistin-resistant Pseudomonas aeruginosa, originating from Portugal and Taiwan, which are associated with various pathologies. The results revealed significant genetic diversity among the isolates, with each exhibiting a distinct genetic profile. A prevalence of sequence type ST235 was observed, characterizing it as a high-risk clone, and serotyping indicated a predominance of type O11, associated with chronic respiratory infections in cystic fibrosis (CF) patients. The phylogenetic analysis demonstrated genetic diversity among the isolates, with distinct clades and complex evolutionary relationships. Additionally, transposable elements such as Tn3 and IS6 were identified in all isolates, highlighting their importance in the mobility of antibiotic resistance genes. An analysis of antimicrobial resistance profiles revealed pan-drug resistance in all isolates, with a high prevalence of genes conferring resistance to β-lactams and aminoglycosides. Furthermore, additional analyses revealed mutations in regulatory networks and specific loci previously implicated in colistin resistance, such as pmrA, cprS, phoO, and others, suggesting a possible contribution to the observed resistant phenotype. This study has a strong impact because it not only reveals the genetic diversity and resistance mechanisms in P. aeruginosa but also identifies mutations in regulatory genes associated with colistin resistance.

PMID:40333140 | DOI:10.3390/pathogens14040387

Int J Pediatr Otorhinolaryngol. 2025 May 4;194:112369. doi: 10.1016/j.ijporl.2025.112369. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial Intelligence (AI) and machine learning (ML) have transformative potential in enhancing diagnostics, treatment planning, and patient management. However, their application in pediatric otolaryngology remains limited as the unique physiological and developmental characteristics of children require tailored AI applications, highlighting a gap in knowledge.

PURPOSE: To provide a narrative review of current literature on the application of AI in pediatric otolaryngology, highlighting knowledge gaps, associated challenges and future directions.

RESULTS: ML models have demonstrated efficacy in diagnosing conditions such as otitis media, adenoid hypertrophy, and pediatric obstructive sleep apnea through deep learning-based image analysis and predictive modeling. AI systems also show potential in surgical settings such as landmark identification during otologic surgery and prediction of middle ear effusion during tympanostomy tube placement. Telemedicine solutions and large language models have shown potential to improve accessibility to care and patient education. The principal challenges include flawed generalization of adult training data and the relative lack of pediatric data.

CONCLUSIONS: AI holds significant promise in pediatric otolaryngology. However, its widespread clinical integration requires addressing algorithmic bias, enhancing model interpretability, and ensuring robust validation across pediatric population. Future research should prioritize federated learning, developmental trajectory modeling, and psychosocial integration to create patient-centered solutions.

PMID:40334638 | DOI:10.1016/j.ijporl.2025.112369

Artif Intell Med. 2025 Apr 29;166:103116. doi: 10.1016/j.artmed.2025.103116. Online ahead of print.

ABSTRACT

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques - including Deep Learning (DL) models - offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and DL models to EEG data for MS. It explores the methodologies used, with a focus on DL architectures such as Convolutional Neural Networks (CNNs) and hybrid models, and highlights recent advancements in ML techniques and EEG technologies that have significantly improved MS diagnosis and monitoring. The review addresses the challenges and potential biases in using ML-based EEG analysis for MS. Strategies to mitigate these challenges, including advanced preprocessing techniques, diverse training datasets, cross-validation methods, and explainable Artificial Intelligence (AI), are discussed. Finally, the paper outlines potential future applications and trends in ML for MS management. This review underscores the transformative potential of ML-enhanced EEG analysis in improving MS management, providing insights into future research directions to overcome existing limitations and further improve clinical practice.

PMID:40334524 | DOI:10.1016/j.artmed.2025.103116

Food Chem. 2025 Apr 28;486:144311. doi: 10.1016/j.foodchem.2025.144311. Online ahead of print.

ABSTRACT

Resistant starch (RS) is a vital dietary component with notable health benefits, but tradition quantification methods are labor-intensive, costly, and unsuitable for large-scale applications. This study introduced an innovative data-driven framework integrating Near-Infrared (NIR) spectroscopy with Convolutional Neural Networks (CNN) and data augmentation to achieve rapid, cost-effective RS prediction. Achieving exceptional accuracy (Rp2 = 0.992), the CNN model outperformed traditional methods like Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR). To overcome the "black-box" limitation of deep learning, SHapley Additive exPlanations (SHAP) were innovatively employed, pinpointing critical wavelengths (2000-2500 nm), significantly narrowing the spectral range while providing meaningful insights into the contribution of specific wavelengths to RS prediction. This optimized spectral enhanced data acquisition efficiency, reduces analytical costs, and simplifies operational complexity, establishing a practical and scalable solution for deploying NIR spectroscopy in food quality assessment and production-line applications.

PMID:40334489 | DOI:10.1016/j.foodchem.2025.144311