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PubMed2026

Towards microplastic bioremediation: Fungal degradation of pristine and pretreated high-density polyethylene and polystyrene.

Microplastic (MP) contamination has become a significant ecological issue because of its enduring existence in the ecosystem and its possible negative impacts. Therefore, using degrading strategies to eliminate these stubborn polymers has been a subject of scientific research. However, the currently used degradation methods are relatively inefficient. Given the pervasiveness of High-Density Polyethylene (HDPE) and Polystyrene (PS) and their resistance to biodegradability, disposal strategies are critical and must be addressed. This manuscript examines the biodegradation of pristine and UV-treated HDPE and PS MPs by Aspergillus flavus species in minimal growth media over 70 days. The maximum weight loss observed at 70 days for pristine HDPE and PS in sole carbon source (SCS) media was (29.33 ± 0.28) % and (17.67 ± 0.35) %, respectively. Whereas, for UV-treated HDPE and PS MPs, the % weight reduction was (33 ± 0.21) % and (25 ± 0.19) %, respectively. UV-treated MPs exhibited greater weight reduction, as UV induced oxygenated functional groups enhance polymer susceptibility to enzymes, thereby promoting biodegradation. HDPE MPs typically show a higher proportion of particles in the lower size range compared to PS MPs. This assertion was based on the weight loss, particle size distribution, and SEM analysis. Furthermore, chemical changes were evaluated using Fourier transform Infrared Spectroscopy (FTIR) analysis, which also displayed chemical oxidation occurring during biodegradation. Liquid Chromatography-Mass Spectrometry (LC-MS) results indicate that UV pretreatment enhances biodegradability by promoting chain scission. These findings further suggest that this fungus's natural and ubiquitous occurrence in terrestrial and marine environments may actively contribute to MP biodegradation while requiring few nutrients.

Microplastics↗

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

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

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

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

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

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

A novel peptide encoded by circTLL1 drives osimertinib resistance in lung cancer by modulating the NT5C2/Ras/PI3K axis.

BACKGROUND: Acquired resistance to osimertinib, a third-generation EGFR tyrosine kinase inhibitor, remains a major clinical challenge in the treatment of non-small cell lung cancer (NSCLC). Although circular RNAs (circRNAs) have been increasingly implicated in drug resistance, most studies have focused on their canonical role as microRNA sponges, while their capacity to encode functional micropeptides remains largely unexplored. This study aimed to identify novel circRNAs involved in osimertinib resistance and to characterize their regulatory functions at the protein level. METHODS: Osimertinib-resistant (OR) NSCLC cell lines were established and validated. High-throughput RNA sequencing was performed to compare the circRNA expression profiles between parental and OR cells. The function of the candidate circRNA was assessed through a series of in vitro and in vivo experiments, including cell viability assays, apoptosis analysis, and xenograft mouse models. Mechanistic investigations involved mass spectrometry, co-immunoprecipitation and western blotting to explore its protein-coding potential and downstream signaling pathways. RESULTS: We identified a novel circRNA, termed circTLL1, that was stably and significantly upregulated in OR-NSCLC cells. Functionally, overexpression of circTLL1 promoted osimertinib resistance, whereas its knockdown restored drug sensitivity both in vitro and in vivo. Mechanistically, we discovered that circTLL1 harbors an open reading frame (ORF) that is translated into a novel 90-amino-acid protein, which we designated circTLL1-90aa. Further investigation revealed that circTLL1-90aa directly interacts with and promotes the degradation of 5'-nucleotidase, cytosolic II (NT5C2), thereby uncoupling nucleotide metabolism from its normal regulatory constraints. The consequent downregulation of NT5C2 leads to elevated GTP levels and leading to the sustained activation of the downstream Ras/PI3K/AKT signaling pathway. CONCLUSION: Our findings unveil a previously unrecognized circRNA/micropeptide/metabolism cascade underlying osimertinib resistance. The identification of the circTLL1-90aa/NT5C2/Ras/PI3K axis not only expands the functional repertoire of the non-coding genome but also provides new insights into the complexity of drug resistance. Given its selective upregulation in resistant cells, circTLL1-90aa holds promise both as a predictive biomarker for treatment stratification and as an actionable therapeutic target, offering a novel strategy to overcome osimertinib resistance in NSCLC patients.

Pyrimidines↗

PubMed2026

Emerging hantavirus risks in mass gatherings: epidemiology, diagnostic challenges, and outbreak preparedness.

Hantaviruses are emerging rodent borne zoonotic pathogens of increasing global public health concern because of their high mortality, expanding ecological distribution, and potential for international dissemination. Although traditionally associated with sporadic rural outbreaks, recent ecological disruption, climate variability, urbanization, and increased global mobility have heightened concerns regarding hantavirus risks in mass gathering settings. This review critically examines the epidemiology, transmission uncertainty, diagnostic and surveillance challenges, and preparedness strategies related to hantavirus infections in the context of mass gatherings, including religious events, refugee settlements, cruise tourism, sporting events, and temporary accommodations. Particular emphasis is placed on the 2026 multinational cruise ship associated outbreak linked to the MV Hondius, which highlighted vulnerabilities related to delayed diagnosis, international passenger dispersal, and uncertainties surrounding possible human to human transmission of Andes virus. Current evidence indicates that hantavirus transmission occurs primarily through inhalation of aerosolized rodent excreta; however, controversies regarding limited interpersonal transmission, environmental persistence, and asymptomatic infections continue to complicate risk assessment and outbreak preparedness. Diagnostic limitations, underreporting, insufficient environmental surveillance, and lack of mass gathering specific preparedness frameworks remain major public health challenges, especially in resource limited settings. Strengthening proactive preparedness through integrated One Health approaches, ecological surveillance, genomic monitoring, AI driven epidemic intelligence, and coordinated international response systems is essential for mitigating future risks. The review emphasizes the urgent need for multidisciplinary research and evidence based policy development to improve global preparedness against emerging hantavirus associated threats in increasingly interconnected mass gathering environments.

Humans↗

PubMed2026

Risk factors for loss of skeletal muscle mass in patients with chronic kidney disease on a low-protein diet.

OBJECTIVES: A low-protein diet (LPD) is recommended for patients with chronic kidney disease (CKD) to prevent a further decline in renal function. However, its impact on muscle mass in these patients remains unclear. This study investigated the risk factors for loss of muscle mass in patients with CKD on an LPD. METHODS: Eighty-four patients with predialysis CKD (59 men, mean age 61.9 ± 11.5 y) who participated in a multicenter randomized controlled trial initiated in 2014 were retrospectively reviewed. We collected data on baseline blood and urine tests, body composition, and dietary records at the start and end of the observation period. We evaluated muscle mass using the skeletal muscle index (SMI) and analyzed risk factors for a decrease in SMI during the 24-wk observation period, using logistic regression analysis. Variables with an association (P < 0.1) in univariate analysis, as well as age, sex, use of low-protein rice, and changes in protein intake, were subjected to multivariate analysis. RESULTS: SMI decreased in 50 patients (59.5%) during the observation period. Multivariate analysis identified significant associations of the SMI with serum albumin at baseline (odds ratio 0.11, 95% confidence interval 0.02-0.52, P = 0.004) and changes in energy intake while on the LPD (odds ratio 3.39, 95% confidence interval 1.00-11.43, P = 0.049). CONCLUSIONS: Risk factors for reduced SMI in patients with CKD on an LPD were malnutrition when initiating the LPD and reduced energy intake during its implementation. Clinicians should optimize nutritional status before initiation of an LPD and ensure adequate energy intake throughout treatment.

Humans↗

PubMed2026

Associations Between Paternal Pre-Conceptional Body Mass Index and Lifestyle Factors and Offspring Weight Development.

BACKGROUND: Evidence suggests that pre-conceptional paternal factors, including BMI and diet, may influence offspring development. OBJECTIVES: We examined the associations between paternal BMI, dietary protein intake, glycemic index (GI), smoking and alcohol consumption and offspring development during the first 5 years of life. METHODS: This secondary analysis of an RCT included 162 father-child pairs from pregnancies among women with pre-pregnancy overweight or obesity. Paternal characteristics were reported at gestational week 15, reflecting the preceding 3 months. Offspring anthropometry was measured at birth, 6 and 18 months, 3 and 5 years. Associations were examined using linear mixed models and linear regression models. RESULTS: No consistent associations were found between paternal characteristics and offspring outcomes from birth to 3 years. At age 5, higher paternal BMI was associated with higher offspring BMI z-score (β = 0.07 (CI: 0.03; 0.10)), fat mass index (β = 0.07 kg/m2 (CI: 0.02; 0.12)) and fat-free mass index (β = 0.05 kg/m2 (CI: 0.01; 0.08)). Lower paternal protein intake was associated with higher offspring BMI z-score (β = 0.54 (CI: 0.07; 1.01)) and fat-free mass index (β = 0.60 kg/m2 (CI: 0.13; 1.07)), while moderately higher protein intake was associated with higher waist-to-height ratio (β = 0.03 (CI: 3.00 × 10-3; 0.05)). Higher paternal GI was associated with lower offspring BMI z-score (β = -0.04 (CI: -0.08; -2.14-10-3)) at age 5. Smoking and alcohol were not associated with offspring outcomes. CONCLUSION: Paternal BMI was associated with offspring outcomes at age 5 years, while findings for paternal dietary factors were less consistent.

Humans↗