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PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Clinical and endocrine correlates of genetic etiologies in severe hypospadias: Study from 34 patients.

OBJECTIVE: Hypospadias is a prevalent congenital anomaly (0.3%-1.0%); however, severe hypospadias (defined as proximal cases with the meatus at the penoscrotal junction, scrotum, or perineum) is a rare and clinically challenging entity with a multifactorial etiology. This study aimed to characterize the interrelationships among the clinical, endocrine, and genetic profiles in children with severe hypospadias. MATERIALS AND METHODS: We conducted a comprehensive analysis of 34 male patients with severe hypospadias. Preoperative hormone levels were measured using two methods: chemiluminescent immunoassay for luteinizing hormone and follicle-stimulating hormone, and liquid chromatography-tandem mass spectrometry for testosterone (T), dihydrotestosterone (DHT), dehydroepiandrosterone (DHEA), 17α-hydroxyprogesterone (17α-OHP), and other steroids. Genetic analysis was conducted via whole exome sequencing. RESULTS: The diagnostic yield of clinically relevant genetic variants (including pathogenic and likely pathogenic, and variants of uncertain significance) in our cohort was 41.2% (14/34) of patients. Patients carrying these variants exhibited a more complex phenotypic profile compared to non-carriers, including a significantly higher rate of patients with ≥3 associated malformations and a greater prevalence of cryptorchidism. Furthermore, the group with clinically relevant variants showed selective elevations in adrenal-derived precursors, specifically 17α-OHP and DHEA. Correlation analysis revealed significant positive associations of both 17α-OHP levels and the T/DHT ratio with the number of associated malformations. CONCLUSION: This study reveals significant genetic heterogeneity in patients with severe hypospadias. Those carrying genetic variants was associated with more severe clinical phenotypes, while certain endocrine variations, including the elevation of adrenal-derived hormones, were also observed in this cohort.

Humans↗

PubMed2026

Normoalbuminuric and albuminuric diabetic kidney disease exhibit divergent renal proteomic characteristics: implications for management.

BACKGROUND: The pathogenesis of diabetic kidney disease (DKD) is complex. Normoalbuminuric diabetic kidney disease (NADKD) is a special subtype of DKD that often progresses insidiously without detectable albuminuria, posing diagnostic and therapeutic challenges. Its pathogenesis remains unclear. Proteomic analysis of renal tissues may offer insights into its pathogenesis and identify biomarkers. METHODS: Clinicopathological data from 295 biopsy-proven DKD patients were collected and classified into normoalbuminuric (UACR < 30 mg/g, n = 25), microalbuminuric (UACR 30-300 mg/g, n = 26), and macroalbuminuric (UACR > 300 mg/g, n = 244) groups. Laser microdissection combined with mass spectrometry (LMD/MS) was used to analyze glomerular and proximal tubule proteomics in 5 patients per DKD subgroup and 5 control subjects. Associations with clinical features were examined. RESULTS: Glomerular proteomic analysis revealed that oxidative stress and metabolic pathways (UQCRC1) were upregulated in NADKD group, whereas the complement and coagulation cascades (C3, C5, C6, C9, CFH, CFHR1) were significantly upregulated in the microalbuminuric and macroalbuminuric DKD groups. The proximal tubule proteomics analysis showed that oxidative phosphorylation-related proteins (SDHA, CYCS, UQCRQ) were upregulated in NADKD, and collagen I related proteins (COL1A1, COL1A2) were significantly upregulated. CONCLUSION: Oxidative stress and mitochondrial dysfunction are involved in the progression of NADKD, lesions predominantly located in the tubulointerstitium. The complement pathway participates in the pathogenesis and progression of albuminuric DKD (ADKD). These divergent molecular profiles suggest that NADKD and ADKD may reflect different pathophysiological mechanisms and have important implications for therapeutic strategies in diabetes management.

Humans↗

PubMed2026

An Integrated Proteomics and Genomics Approach to Identify Essential Protein Kinases During Human Trophoblast Development.

In the developing human placenta, three subtypes of trophoblast cells, cytotrophoblasts (CTBs), extravillous trophoblasts (EVTs), and syncytiotrophoblasts (STBs), mediate critical functions essential for a successful pregnancy. CTBs constitute the stem/progenitor compartment and differentiate into STBs and EVTs within the floating and anchoring villi, respectively. STBs establish the maternal-fetal exchange interface and secrete human chorionic gonadotropin (hCG), a hormone vital for the maintenance of early pregnancy. EVTs anchor the maternal endometrium and invade the uterine tissue to remodel maternal cells, supporting implantation and progression of pregnancy. In this study, we used human trophoblast stem cells (hTSCs) as a model system and performed quantitative, label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) to profile the proteome and phosphoproteome in TSC stem state (analogous to undifferentiated CTBs) and following their differentiation to STBs and EVTs. Through a multiomics approach, we integrated our proteomics data with global gene expression profiles to correlate cell-type specific gene and protein expression during human trophoblast development. We also identified global phosphoproteome and analyzed kinases that are specifically active in hTSC stem state, as well as in differentiated STBs and EVTs. We experimentally validated specific kinases, such as BUB1B, PAK6, PKYMT1, and TNIK, that are essential for maintaining the hTSC stem-state. Additionally, atypical protein kinase C isoforms PKCζ are essential for STB development, whereas PTK2B, SRC, TRIO, and LYN are important for EVT development. Our findings highlight key kinases uniquely required for specific stages of trophoblast development during human placentation and suggest that pharmacological inhibition of these kinases could negatively impact the placentation process during pregnancy.

Humans↗

PubMed2026

Ecological Restoration of the Soil-Like Function in the Bauxite Residue: Natural Microbiomes Mediated Molecular Transformation of Dissolved Organic Matter.

Soilization of bauxite residues offers a scalable route for long-term carbon management and ecological restoration. However, the microbial processes that transform exogenous organic inputs into stable soil-like carbon pools remain poorly resolved. Here, we combined cross-ecosystem meta-analysis, machine-learning prediction, native synthetic community (SynCom) construction, 13C-labeled straw microcosms, field validation, Fourier transform ion cyclotron resonance mass spectrometry, and genome-resolved metagenomics to unravel microbiome-mediated carbon transformation at the dissolved organic matter (DOM) molecular scale. Our meta-analysis revealed that alkaline industrial wastes retained soil-like DOM signatures but were enriched in microbial humic- and protein-like components, indicating active yet incomplete carbon processing. Guided by these patterns, native SynCom inoculation increased 13C incorporation into total organic carbon (TOC) and dissolved organic carbon (DOC), enlarged biodegradable and adsorbable DOC fractions, and shifted DOM from recalcitrant aromatic pools toward oxygenated carbohydrate-, tannin-, and phenolic-like molecular classes. Genome-resolved analyses linked this transformation to complementary polymer degradation and nutrient-cycling functions across fungal and bacterial guilds, including enriched carbohydrate-active enzymes in straw-carbon-utilizing metagenome-assembled genomes. Null model and thermodynamic analyses further showed that microbial communities were constrained by homogeneous selection, whereas DOM molecules were diversified through variable selection and redox-dependent transformation. Field-scale validation confirmed that SynCom promoted TOC and DOC accumulation and humic-like, high-density DOM fractions under alkaline conditions. Together, these findings establish a mechanistic framework in which functional microbiomes couple plant carbon depolymerization, DOM molecular diversification, and mineral-interactive carbon stabilization, providing a microbiome-guided strategy for carbon sequestration and soilization in the bauxite residue.

Soil↗

PubMed2026

In situ product monitoring in heterogeneous reaction of gaseous trimethylamine on Fe2O3/Fe(NO3)3: Effect of environmental factor and particle property.

Gas-particle reactions represent an important atmospheric heterogeneous transformation process for organic amines (OAs). Environmental factors and particle properties may impact the gas-particle reaction products. Although the products from gas-particle reactions can be monitored by various in situ techniques, related data remain scarce. Here, the interfacial and gaseous products from the reaction of trimethylamine on Fe2O3/Fe(NO3)3 particles under light irradiation with mixed NO2, O2, SO2 and H2O were monitored using in-situ diffuse reflectance Fourier transform infrared spectroscopy and proton transfer reaction time-of-flight mass spectrometry. Dark reaction of gaseous trimethylamine on Fe2O3/Fe(NO3)3 generated two interfacial products types: N-containing ones (CH3NCH2, CH3NO2, (CH3)2NCHO, and CH3N(OH)CHO) and N-free ones (alcohols, aldehydes and acids), both accumulating with reaction progression. Light irradiation and O2 oxidation enhanced formation of these products, while NO2 promoted the production of CH3NO2 and (CH3)2NCHO. H2O and SO2 occupied the active sites of particles to inhibit the formation of all products. Compared to Fe(NO3)3, Fe2O3 showed absolute dominance in contribution to the formation of products. Considering the smaller particle size of Fe2O3 and excess Fe(NO3)3, the physical mixing of them reduced the generation of interfacial products. Furthermore, gaseous products of CH3OH, HCHO, CH3CHO, HCOOH and CH3COOH detection clarified the N-free interfacial products. The presence of Fe(NO3)3 inhibited the formation of HCOOH and favored the formation of CH3CHO in the gas phase. By combining product information with thermodynamic calculations, the heterogeneous reaction pathways of trimethylamine were tentatively proposed. These findings provide a guiding significance for the migration of OAs in real atmospheric environment.

Methylamines↗

PubMed2026

Plasma proteome profiling identifies XPNPEP3 as a novel biomarker associated with metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus.

OBJECTIVE: To identify plasma protein differences between type 2 diabetes mellitus (T2DM) patients with and without metabolic dysfunction-associated steatotic liver disease (MASLD), and to evaluate the diagnostic potential of X-prolyl aminopeptidase 3 (XPNPEP3) for identifying MASLD in T2DM patients. METHODS: Twenty T2DM inpatients were categorized into groups with and without MASLD and their plasma samples were analyzed using data-independent acquisition mass spectrometry, followed by bioinformatics analysis to identify differentially expressed proteins. The cohort was then expanded to 84 patients, and plasma XPNPEP3 levels were validated by enzyme-linked immunosorbent assay. Correlation between XPNPEP3 and clinical indicators were evaluated, and diagnostic performance was determined via receiver operating characteristic (ROC) analysis. Immunohistochemistry was employed to compare hepatic XPNPEP3 expression between the two groups. RESULTS: Proteomic analysis identified 176 differentially expressed proteins, with XPNPEP3 exhibiting the most significant down-regulation by fold change. In the validation cohort, plasma XPNPEP3 was significantly lower in T2DM+MASLD versus T2DM alone. XPNPEP3 levels were negatively correlated with diabetes duration, liver function markers, and triglyceride levels, and was identified as an independent factor inversely associated with MASLD in T2DM.ROC analysis demonstrated strong diagnostic performance for XPNPEP3, further enhanced when combined with BMI and diabetes duration.  Immunohistochemistry confirmed reduced hepatic XPNPEP3 expression in T2DM+MASLD patients. CONCLUSIONS: Lower plasma XPNPEP3 is independently associated with MASLD in T2DM patients and demonstrates strong diagnostic potential, positioning XPNPEP3 as a promising biomarker for diagnosing MASLD in T2DM patients and a novel target for non-invasive diagnostic tool development.

Humans↗

PubMed2026

Comparative analysis of lipopolysaccharide lipid A structure and its biosynthetic genes in the plant-associated bacteria Brucella cytisi and Brucella lupini.

The genus Brucella comprises important human and animal pathogens, as well as numerous environmental and symbiotic species. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, plays a crucial role in bacterial physiology and host interactions. In this study, the structures of lipid A, the hydrophobic anchor of lipopolysaccharide, isolated from two plant-associated strains, Brucella cytisi ESC1ᵀ and Brucella lupini LUP21ᵀ, were presented. Lipid A preparations were structurally characterized using chemical methods, MALDI-TOF mass spectrometry, and nuclear magnetic resonance spectroscopy. The obtained results indicated that both lipid A molecules have almost identical structures. Their sugar backbones consist exclusively of 2,3-diamino-2,3-dideoxy-d-glucose (d-GlcpN3N). Phosphate residues were connected to distal and proximal GlcpN3N in approximately half of the lipid A molecules. Fatty acid analysis revealed the presence of C14:0 (3-OH), C16:0 (3-OH), and traces of C18:0 (3-OH). All of these were primary fatty substituents of the sugar backbone and were amide-linked residues. Lactobacillic acid C19:0cyc and 27-hydroxyoctacosanoic acid (C28:0 (27-OH)) were found as ester-linked secondary acyl residues. In turn, C28:0 (27-OH) was partly esterified by a 3-hydroxybutyroyl residue. Two unsubstituted 3-hydroxyfatty acids were linked exclusively to the proximal d-GlcpN3N residue. It was pointed out that sequences of putative genes encoding enzymes required for lipid A biosynthesis and genes encoding specific enzymes involved in structural modifications of lipid A occurring in the genomes of both bacterial species are almost identical. The high sequence similarity of these proteins reflects the observed similarities in the lipid A structures in both investigated Brucella species.

Brucella↗

PubMed2026

An overview of the use of proteomics and peptidomics to characterize alternative protein foods.

The global protein transition is accelerating the development of alternative protein foods, mainly derived from plants, insects, algae, fungi, and cellular agriculture. Ensuring the authenticity, safety, and nutritional adequacy of these emerging protein matrices requires molecular-level characterization beyond traditional compositional analyses. Proteomics and peptidomics have emerged as transformative analytical platforms capable of decoding the molecular signatures that define protein origin, structural integrity, digestibility, functionality, and health potential. The review comprehensively examines the application of proteomics, and peptidomics for profiling alternative protein foods. Further, the source authentication strategies based on species-specific protein and peptide biomarkers, detection of adulteration in complex matrices, and allergenicity assessment is discussed. Special attention is also given to nutritional proteomics with protein digestibility, gastrointestinal peptide release, and identification of bioactive sequences. SIGNIFICANCE: The importance of this review is that proteomics and peptidomics are becoming central in the management of the fast-growing environment of alternative protein foods, such as plant-based, insect, algal, fungal, and cultured meat products. It provides an explanation of the application of mass spectrometry-based processes to decode molecular signatures defining the origin of proteins, their structural integrity, digestibility, allergenicity, and bioactive properties, and thus directly contribute to safety, nutritional analysis, and authenticity of the product. Presentation of the article includes the integration of the knowledge of traditional muscle foods with alternative systems of proteins, where validated protein and peptide biomarkers are used in authentication, fraud detection, and allergy risk assessment in a wide variety of matrices. It also indicates the role of nutritional proteomics and peptidomics in informing the formulation strategy to promote digestibility and release of health-promoting peptides. In general, this review will guide scientists, the food industry, and regulatory bodies to use modern proteomic technologies in quality assurance, and decision-making, for the advancementof sustainable protein-based foods.

Proteomics↗

PubMed2026

Identification and formation pathways of oxidation products of chlorinated paraffins during ozonation in municipal wastewater.

Chlorinated paraffins (CPs) cannot be efficiently removed by conventional water treatment processes and are continually discharged into the aqueous environment. Ozonation can effectively remove lipophilic and persistent pollutants. However, the degradation behaviors of short-chain CPs (SCCPs), medium-chain CPs (MCCPs), and long-chain CPs (LCCPs) in wastewater during the ozonation process remained unknown. In this study, ozonation treatment achieved removal efficiencies of 61 % for SCCPs, 66 % for MCCPs, and 51 % for LCCPs from wastewater within 30 min. Approximately 147 oxidative products of SCCPs, MCCPs, and LCCPs were non-targeted identified through Ph4PCl-enhanced ionization with ultra-high performance liquid chromatography-Orbitrap mass spectrometry. These oxidation products were structurally classified into three categories: carbon chain breakage (53 products), HCl-elimination (27 products), and hydroxylation (67 products). Twenty-three di-hydroxylated CPs were newly identified among the products. Hydroxylation was the predominant pathway for SCCPs, producing di-hydroxylated SCCPs ((OH)₂-SCCPs) with a higher generation rate constant (KG = 22.28 × 10⁻² min⁻¹) compared to other products. MCCPs and LCCPs mainly underwent carbon chain breakage and hydroxylation, generating shorter carbon chain congeners, (OH)2-SCCPs, and di-hydroxylated MCCPs ((OH)2-MCCPs). The KG values of (OH)2-SCCPs (10.56 × 10-2 min-1) and (OH)2-MCCPs (12.05 × 10-2 min-1) generated from the MCCPs were the highest, and the KG values of MCCPs (6.49 × 10-2 min-1), SCCPs (6.27 × 10-2 min-1), and (OH)2-SCCPs (4.74 × 10-2 min-1) generated from the LCCPs were higher than those of other products. These results comprehensively clarify the oxidation efficiencies and pathways of CPs during ozonation. Future studies must explore the potential risks associated with the oxidation products.

Water Pollutants, Chemical↗

PubMed2026

Simultaneous determination of imiquimod and terbinafine in skin permeation studies: Validation of a liquid chromatography method with fluorescence detection.

Chromoblastomycosis is a chronic, neglected subcutaneous mycosis posing significant therapeutic challenges. A topical strategy combining terbinafine (TBF), an antifungal, with imiquimod (IMQ), a TLR-7/8 agonist immunomodulator, has emerged a promising alternative. However, no validated analytical method is currently available to simultaneously quantify both drugs in skin, which is crucial for novel formulation development. This study reports the development and validation of a simple HPLC method with fluorescence detection (excitation 236 nm, emission 340 nm) for the simultaneous determination of TBF and IMQ extracted from porcine skin. Separation was achieved on a C8 reversed-phase column (125 × 4.0 mm, 5 μm) using a mobile phase of methanol and water (60,40, v/v), both containing 0.1% formic acid at a flow rate of 0.8 mL/min. The method showed excellent linearity (r > 0.999) over 0.01-1.0 μg/mL for IMQ and 0.1-2.0 μg/mL for TBF. Intra- and inter-day precision demonstrated coefficients of variation below 5%, and recovery rates from skin (79-105%) confirmed accuracy. Limits of detection were 0.001 μg/mL for IMQ and 0.004 μg/mL for TBF, with quantification limits of 0.02 μg/mL and 0.16 μg/mL, respectively. This selective, sensitive, and reproducible method represents a valuable analytical tool for supporting the development and quality control of topical formulations for chromoblastomycosis and other fungal skin diseases.

Animals↗

PubMed2026

The Interrelationship between Cadmium, Smoking, and Migraine in the ELSA-Brasil Study.

This study explored the relationship between serum cadmium (Cd), smoking exposure, and migraine in the ELSA-Brasil cohort (2008-2010). The analysis included 2,750 participants whose serum Cd levels were measured using inductively coupled plasma mass spectrometry. Smoking exposure was assessed using self-report data of active smoking and second-hand smoke. Migraine, including definite and probable migraine, was diagnosed according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Logistic regression was used to estimate the odds of migraine across serum Cd quintiles, using the third quintile as the reference category, while linear regression examined the relationship between smoking exposure and Cd levels. Polynomial contrasts tested linear trends. Participants had a mean (SD) age of 53.2 (9.0) years and 53.1% were women. Migraine prevalence was 29.1% (including probable migraine). Individuals with migraine had slightly higher median serum Cd levels than controls [0.050 µg/L (IQR 0.035-0.077) vs. 0.048 µg/L (0.035-0.066); p = 0.021], although smoking exposure scores did not differ between groups. Smoking exposure showed a strong positive association with serum Cd concentrations (p-trend < 0.001). Participants in the highest Cd quintile had greater odds of migraine [aOR 1.51 (95% CI 1.09-2.08), p = 0.011] after adjustment for smoking exposure and sociodemographic and clinical confounders. Sex-stratified analysis yielded even stronger associations among males [aOR 1.84 (95% CI 1.07-3.16), p = 0.027]. However, in the sensitivity analysis including definite migraine cases only, this association was no longer significant [aOR: 1.14 (0.71, 1.83), p = 0.579]. Main findings suggest that Cd exposure from smoking may contribute to migraine occurrence, particularly in males. However, other sources of Cd that could influence migraine should not be disregarded, and future investigation in this field is warranted.

Cadmium↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗

PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗

PubMed2026

Pediococcus acidilactici WSP-AZM23: Genomic Insights, In Vitro Probiotic Potential, and Computational Analysis for Biotherapeutic Applications.

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Anti-inflammatory↗

PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗