SEARCH · Search PubMed

Results for “Liquid chromatography-tandem mass spectrometry”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

204 records · Page 5Linked to original sources

PubMed2026

Associations between multiple essential trace metal concentrations and risk of hyperuricemia: insights from a central Chinese population.

Previous studies have indicated that levels of individual essential trace metals are related to hyperuricemia (HUA), but evidence on their combined effects is limited. To address this gap, the associations of individual and joint levels of 12 essential trace metals (manganese, selenium, nickel, chromium, cobalt, tin, iron, molybdenum, zinc, strontium, vanadium, and copper) with the risk of HUA were investigated in 2,021 adults recruited from Hunan Province, China. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to determine urinary metal concentrations. Logistic regression, Bayesian kernel machine regression (BKMR), and quantile g calculation (Qgcomp) were applied to evaluate the associations of single and mixture metal concentrations with HUA. Of the participants, 516 (25.53%) were diagnosed with HUA. Inverse associations were found between vanadium, chromium, manganese, iron, cobalt, selenium, strontium, and molybdenum levels and HUA, with ORs ranging from 0.63 to 0.91. Conversely, a positive association was observed between zinc concentration and HUA [OR (95% CI): 1.17 (1.01, 1.37)]. Both BKMR and Qgcomp models showed a negative overall effect of essential trace metals on HUA risk, with strontium (- 43.6%) and vanadium (- 27.8%) being the main contributors. In addition, formal interaction tests revealed significant effect modification by age for tin and by BMI for zinc. In conclusion, the levels of essential trace metals were linked to a decreased risk of HUA, and these associations were modified by age and BMI only for specific metals.

Humans↗

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

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

Prognostic Value of Circulating Tumor DNA-Based Minimal Residual Disease for Recurrence-Free Survival in Resectable Gastric Cancer: A Systematic Review and Meta-Analysis with Serial Monitoring Analysis.

BACKGROUND: Circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) is an emerging biomarker, but its utility in resectable gastric cancer remains incompletely characterized. METHODS: We conducted a systematic review and meta-analysis of eight studies (520 patients) to evaluate the prognostic value of ctDNA-based MRD for recurrence-free survival (RFS) and overall survival (OS) in resectable gastric cancer. RESULTS: In localized resectable gastric cancer (Stage I-III), the setting in which postoperative ctDNA most coherently represents true molecular residual disease after curative-intent surgery, postoperative ctDNA positivity was associated with diminished recurrence-free survival (RFS: HR 12.26, 95% CI 3.30-45.52) and overall survival (OS: HR 8.57, 95% CI 3.06-23.98). The test for subgroup differences between localized and mixed-stage cohorts was not statistically significant (P = 0.57), and the numerically higher HR in the localized subgroup should therefore not be interpreted as evidence of a quantitatively stronger prognostic effect. Postoperative ctDNA detection demonstrated substantially stronger prognostic value (overall RFS: HR 10.00, 95% CI 4.53-22.10) compared to preoperative assessment (HR 2.17, 95% CI 1.10-4.28). Both tumor-informed and tumor-agnostic strategies effectively stratified high-risk patients. However, these effect sizes should be interpreted cautiously given the small number of studies and substantial heterogeneity (I2 = 65-72%). Results from mixed-stage cohorts including Stage IV disease are supportive but should not be considered equivalent to localized-disease findings, as ctDNA in metastatic disease reflects persistent systemic burden rather than minimal residual disease in the postoperative sense. CONCLUSIONS: Postoperative ctDNA-based MRD shows a consistent adverse prognostic association in resectable gastric cancer, with localized disease (Stage I-III) representing the most biologically and clinically coherent setting for interpretation. However, the large pooled hazard ratios (HR 10.00-12.26) should be interpreted as a directionally consistent signal rather than precise quantitative estimates, given the small number of studies, wide confidence intervals, and substantial heterogeneity (I2 = 65-73%). This heterogeneity is largely driven by substantial variation in postoperative sampling timing (4 days to 16 weeks) and ctDNA assay characteristics (platform, sensitivity, coverage, variant filtering, and positivity thresholds), which require standardization in future studies. While ctDNA is prognostically valuable, its clinical utility remains unestablished. Prospective randomized trials are needed to determine whether ctDNA-guided strategies improve patient outcomes before routine clinical implementation can be recommended.

Humans↗

PubMed2026

Diagnostic utility of high-risk HPV polymerase chain reaction-based testing in head and neck FNA specimens with indeterminate cytomorphology.

BACKGROUND: Fine-needle aspiration (FNA) is critical in the initial diagnosis of many high-risk human papillomavirus (HR-HPV)-associated, metastatic oropharyngeal squamous cell carcinomas. Updated guidelines recommend HR-HPV-specific polymerase chain reaction (PCR) analysis over p16 immunohistochemistry on FNA specimens because p16 performs poorly on cytology material. PCR-based assays on liquid cytology material have demonstrated excellent analytic performance; however, the diagnostic utility of a positive HR-HPV PCR result in specimens with indeterminate cytomorphology remains uncharacterized. METHODS: The authors retrospectively identified 279 head and neck FNA specimens that had paired HR-HPV PCR testing on residual liquid cytology material over a 5-year period. The positive predictive value for histopathologically confirmed squamous cell carcinoma on surgical follow-up was calculated within each cytologic interpretive category. RESULTS: The HR-HPV PCR results were positive in 50.2% of specimens, negative in 40.9%, and indeterminate in 9.0%. The HR-HPV positivity rate ranged from 0% in specimens categorized as negative for malignancy to 57.3% in cytologically positive specimens, with 19.0%, 41.2%, and 50.0% positivity in the atypical, suspicious, and nondiagnostic categories, respectively. Among cytologically indeterminate specimens with positive HR-HPV PCR results (n = 14), the positive predictive value was 100% (95% confidence interval, 78.5%-100.0%). Blinded slide review additionally identified 15 cytologically positive specimens in which the definitive malignant interpretation depended substantially on HR-HPV positivity; all 15 were confirmed as squamous cell carcinoma. CONCLUSIONS: A positive HR-HPV PCR result on liquid cytology material carries a positive predictive value of 100% for malignancy in cytologically indeterminate head and neck FNA specimens. These findings support integrating HR-HPV PCR analysis into routine cytologic interpretation with the potential to upgrade some indeterminate specimens to malignant when HR-HPV is detected, expediting definitive treatment and sparing patients additional, invasive sampling.

Humans↗

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

Time-varying hazard rates reveal patterns of progression in HR+/HER2- metastatic breast cancer: Towards risk-adapted monitoring.

BACKGROUND: optimal imaging intervals for patients with hormone receptor-positive/HER2-negative metastatic breast cancer (MBC) remains undefined. Aim of this study was to analyze the temporal patterns of disease progression to identify high risk subgroups that may benefit from intensified monitoring. METHODS: we analyzed 149 hormone receptor-positive/HER2-negative MBC patients prospectively enrolled in the MAGNETIC.1 trial (NCT05814224) and treated with first line endocrine therapy. Hazard rates (HR) for disease progression were determined according to clinico-pathological and liquid biopsy features. RESULTS: in the overall population, two distinct progression-risk peaks emerged at 2-3 months (32.9/1000 person-months) and at 24 months (28.0/1000). Higher risk of progression was observed in lobular carcinoma (61.1) [HR 61.12 per 1000 person month (pm)], progesterone receptor-negative status (HR 39.07), fulvestrant-based treatment (HR 46.88), liver metastases (HR 59.00), and presence of ≥ 3 metastatic sites (HR 40.10). CONCLUSIONS: Hazard distribution in hormone receptor-positive/HER2-negative MBC is biphasic and modulated by readily available clinical variables. High-risk subgroups may benefit from intensified radiologic and liquid-biopsy surveillance during the first three months and around two years after treatment start.

Breast cancer↗

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↗