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PubMed2026

An RPA-assisted homogeneous electrochemical DNA sensor for on-site eDNA detection toward early warning of crown-of-thorns starfish outbreaks.

Crown-of-thorns starfish (COTS) outbreaks seriously threaten coral reef ecosystems, while conventional monitoring approaches are time-consuming and often lack sufficient sensitivity for early warning. Existing electrochemical DNA sensors usually require complex electrode-surface immobilization procedures, which can lead to uneven probe distribution, significant steric hindrance, and poor stability. Meanwhile, the low concentration of environmental DNA (eDNA) in marine environments further complicates detection. To overcome these challenges, this study developed a homogeneous electrochemical DNA sensor assisted by recombinase polymerase amplification (RPA) for COTS eDNA detection. Target DNA was first amplified by RPA, and the amplification products were then hybridized in solution with capture probe (CP)-modified magnetic beads (MB) and biotin-labeled signal probe (SP) to form sandwich-structured MB complexes. These complexes were subsequently magnetically enriched and immobilized on the electrode surface for electrochemical signal readout. Under optimized conditions, the sensor displayed a linear response to COTS genomic DNA from 3.77 fg/μL to 1 ng/μL, with an LOD of 2.02 fg/μL and an LOQ of 3.77 fg/μL. The sensor was applied to Xisha Islands samples, and the results agreed with droplet digital PCR (ddPCR) (P > 0.05), demonstrating its potential for sensitive and reliable on-site COTS eDNA detection.

Animals↗
PubMed2026

Influenza A virus in Swiss pig herds with respiratory disease: Seasonality and age dependence.

Influenza A virus (IAV) is an important respiratory pathogen in pigs and poses a zoonotic risk to humans in close contact. While IAV epidemiology has been extensively studied in large-scale production systems, data from Switzerland - characterized by small herds and limited live pig imports - remain scarce. This exploratory nationwide cross-sectional study aimed to assess the association between herd-level IAV detection and reported respiratory disease in pig herds, and to explore associations with husbandry-, animal-, and human health-related factors. Between November 2023 and April 2025, 25 Swiss pig herds with caretaker-suspected respiratory symptoms were investigated. In each herd, five nasal swabs were collected and analyzed by quantitative PCR. Herd managers completed an interview, and clinical examinations were performed. Overall, 56 % (95 % CI: 37,1 - 73,3) of herds tested positive for IAV, comparable to reports from other European countries. The estimated intra-herd detection rate was 49,6 % (95 % CI: 31,2 - 68,0). Respiratory disease outbreaks associated with IAV detection showed indications of seasonal variation, with no positive herds identified during summer. Across age groups, pigs aged 11-14 weeks had a higher likelihood of IAV detection, with 15,79-fold increased odds (95 % CI: 1,50 - 860,4), although with considerable uncertainty. The interpretation is limited by the small sample size, heterogeneous data, and reliance on single time-point qPCR detection. The results suggest that IAV detection in clinically apparent respiratory outbreaks may follow seasonal patterns in Swiss pig herds. Weaners and newly introduced fattening pigs may play a role in such respiratory outbreaks and could represent relevant targets for IAV surveillance in Switzerland. Continued monitoring and the implemen tation of appropriate control measures remain important given the virus's zoonotic potential and impact on pig health.

Animals↗
PubMed2026

Emergence of a Novel, Phenotypically Difficult-to-Detect Vancomycin-Resistant Enterococcus faecium Clone (ST117/CT7799).

A significant increase of vancomycin-resistant Enterococcus faecium (VREfm) infections was observed in South-Eastern Austria since 2024. The prolonged outbreak is caused by a novel vanB-VREfm clone (ST117/CT7799, "VREfmstyr"). This study characterizes the atypical difficult-to-detect resistance phenotype and assesses the genomic relatedness of the isolates. Patient and outbreak characteristics were investigated including whole genome sequencing of the isolates. Sensitivity of broth microdilution (BMD), gradient tests (GT), disk diffusion (DD), and automated susceptibility testing (VITEK2) was compared. The performance of commercial screening media was evaluated. From sporadic detections in early 2024 case numbers began to rise during the year. In 30/31 (97%) of all cases, intra-hospital transmission was considered likely and an association with invasive procedures was identified in most cases. Core genome multilocus sequence typing revealed only six allelic differences between VREfmstyr isolates collected in a 12-month period, all belonging to the E. faecium ST117/CT7799 lineage. BMD detected vancomycin resistance (MIC > 4 mg/L) in no more than 16/31 (52%) of isolates after 24 h incubation, while GT and DD misclassified all isolates. Only prolonged incubation improved the performance of these assays. VITEK2 analysis, however, correctly classified all 31 isolates. Of four commercially available VRE-screening agars, only one was capable of detecting VREfmstyr after 24 h incubation. The emergence and clonal dissemination of VREfm ST117/CT7799 reveals a serious diagnostic gap as commonly used diagnostic algorithms fail to reliably detect this resistance phenotype. Our findings should help to further evaluate the true geographical distribution and clinical significance of this novel VREfm clone.

Enterococcus faecium↗
PubMed2026

Evaluation of three Aspergillus antibody assays for screening of chronic pulmonary aspergillosis: prospective diagnostic accuracy study.

OBJECTIVES: Chronic pulmonary aspergillosis (CPA) is a frequent complication of pulmonary tuberculosis (PTB), particularly in high-burden settings where access to reliable serological diagnostics remains limited. We evaluated the diagnostic performance of two immunochromatographic technology (ICT) lateral flow assays (LFAs) and an ELISA for CPA screening among patients with active or previously treated PTB. METHODS: In this two-year prospective multicentre diagnostic evaluation, serum from adults with prior or active PTB was tested using the Era Biology Aspergillus IgG ICT LFA, LDBio Aspergillus IgG/IgM ICT LFA, and Bordier Aspergillus fumigatus IgG ELISA. CPA diagnosis was established using a consensus composite reference standard incorporating clinical, immunological, radiological, and microbiological criteria. The Bordier ELISA was used as part of the immunological component of the consensus CPA diagnosis, with a cutoff optical density of ≥1.0. Diagnostic accuracy, agreement statistics, receiver operating characteristic analysis, and latent class analysis (LCA) were performed. RESULTS: Among 340 participants, 24 (7.06%) had CPA. Proportion of participants with positive antibody tests among all tested individuals were 6.76% for LDBio ICT LFA, 20.0% for Era Biology ICT LFA, and 11.47% for Bordier ELISA. Against consensus CPA diagnosis, Bordier ELISA showed 87.50% sensitivity and 94.30% specificity, LDBio ICT LFA 58.33% sensitivity and 97.15% specificity, and Era Biology LFA 66.67% sensitivity and 83.54% specificity. LCA estimated CPA prevalence at 7.72%. LCA-derived sensitivities and specificities were 86.58% and 99.92% for LDBio ICT LFA, 83.39% and 85.31% for Era Biology LFA, and 79.10% and 94.19% for Bordier ELISA. CONCLUSIONS: The Bordier ELISA showed high sensitivity and specificity, while the LDBio ICT LFA demonstrated very high specificity with strong LCA-derived performance. These findings support the use of ELISA for laboratory diagnosis and ICT as a point-of-care screening tool for CPA in resource-limited settings. Era Biology Aspergillus IgG LFA demonstrated moderate sensitivity and acceptable diagnostic performance, indicating its potential utility as a supplementary screening assay for CPA in settings where rapid, point-of-care testing is required.

Humans↗
PubMed2026

How Do Climatic Factors Directly Influence the Incidence and Risk of Meningococcal Meningitis Across the African Meningitis Belt? A Narrative Literature Review.

Globally, the highest incidence of meningococcal meningitis occurs within the African meningitis belt, spanning 26 countries across sub-Saharan Africa. Meningococcal meningitis incidence is highly seasonal in this region specifically, with outbreaks mostly occurring during the dry season, characterized by low rainfall and atmospheric humidity, high temperature, and increased dust and wind speed. The strong seasonality of meningococcal outbreaks coincides with seasonal variation in climatic factors. This multicollinearity can make it difficult to identify environmental drivers of disease and the mechanisms by which they operate. This review aims to collate existing evidence to better clarify the mechanisms by which climatic variables influence meningococcal meningitis incidence. We examined the impact of dust, wind speed, temperature, rainfall, and land cover on meningococcal meningitis outbreaks. Within the literature, atmospheric dust and wind speed had the strongest statistical association with meningococcal outbreaks and demonstrated greater predictive probability than other climatic variables. However, several climatic factors have demonstrable influences on one another, reflected in the seasonality of meningococcal meningitis. Atmospheric dust can reduce precipitation levels in part through its radiative properties. Decreased rainfall and increasing temperatures can dry out soil, increasing its availability to be uplifted as dust. Alongside, this lower atmospheric humidity increases evaporative demand, leading to faster soil moisture loss and enhanced surface drying. We argue that rainfall, temperature, and land cover variability may act as part of a broader climatic mechanism, increasing atmospheric dust. This increases the incidence and risk of meningococcal meningitis.

Africa↗
PubMed2026

Estimated prevalence and distribution of causes of immune-mediated hypertrophic pachymeningitis in São Paulo, Brazil.

BACKGROUND: The prevalence of hypertrophic pachymeningitis (HP) remains poorly defined worldwide. In Japan, the estimated prevalence is 0.949 cases per 100,000 inhabitants, with most cases related to ANCA-associated vasculitis or IgG4-related disease. In contrast, epidemiological data from other regions, including Latin America, are scarce. METHODS: This study was conducted at a tertiary referral center in the city of São Paulo, Brazil. The prevalence of HP was estimated by comparison with the known prevalence of multiple sclerosis (MS), based on the number of patients with each condition followed at our institution. The etiological distribution of HP was also analyzed. In addition, HP prevalence was compared with that of tuberculous meningoencephalitis, a compulsory notifiable disease and a well-established cause of chronic meningitis in Brazil, using data from the national surveillance system (SINAN). RESULTS: We identified 55 patients with HP: 36 (65%) idiopathic, 10 (18%) IgG4-related disease, 4 (7%) ANCA-associated pachymeningitis, 4 (7%) probable or definite neurosarcoidosis, and 1 (2%) secondary to rheumatoid arthritis. Among 968 registered MS patients and assuming an MS prevalence of 15 per 100,000 inhabitants in São Paulo, the estimated HP prevalence was 0.85 per 100,000 inhabitants. This estimate was comparable to the prevalence of tuberculous meningoencephalitis in the same region. CONCLUSION: The estimated prevalence of HP in São Paulo (0.85 per 100,000) is consistent with reports from other regions and is comparable to that of tuberculous meningeal disease, although with a distinct etiological profile. IgG4-related disease was the most frequent systemic association, differing from previous reports. ANCA-associated HP appears to be less frequent than in other geographic regions, possibly reflecting differences in disease phenotype. Key Points • The estimated prevalence of HP in São Paulo was 0.85 per 100,000 inhabitants. • IgG4-related disease was the most common systemic disease identified among patients with HP. • ANCA-associated HP was less common than reported in Asian and European cohorts. • The prevalence of HP was comparable to that of tuberculous meningoencephalitis in São Paulo.

Humans↗
PubMed2026

Culture of infectious human norovirus isolated from live contaminated oysters.

Human noroviruses are a major cause of foodborne outbreaks worldwide. Filter-feeding shellfish, such as oysters, can bioaccumulate these viruses in their digestive tissue when grown in sewage-impacted coastal areas and are often implicated in norovirus foodborne outbreaks. Despite the high sensitivity of current molecular assays, these methods for norovirus detection in shellfish fail to distinguish between infectious and non-infectious particles. Assessing norovirus infectivity in shellfish remains a challenge due to the lack of suitable isolation methods that maintain capsid integrity. In this study, a protocol for isolating infectious norovirus from oyster tissues, based on chloroform-butanol elution and polyethylene glycol concentration (CB-PEG), was optimized for the recovery of human norovirus GI and GII. While CB-PEG method recovered various norovirus GI and GII genotypes, it was less efficient at the genomic level than a protocol based on proteinase K elution (adapted from ISO 15216) and showed genotype-dependent viral recovery rates. By optimizing the flocculation step, we improved the method's compatibility with human intestinal enteroid (HIE) cultures. Using this approach, we successfully quantified infectious norovirus GII.3 titers recovered from artificially-contaminated live oysters. Interestingly, infectious virus was better isolated following a freezing step of the digestive tissues, with titers ranging from 13 to 40 TCID50/mL for positive samples. In conclusion, this study established an optimized methodological approach for the relative quantification of infectious norovirus GII.3 in shellfish, paving the way for future research on viral persistence and inactivation strategies in this foodstuff.

Norovirus↗
PubMed2026

From commensal to pathobiont: The emergence of virulence-enhanced Escherichia coli in China's food-animal systems - insights with future implications.

A fundamental shift in Escherichia coli epidemiology is being driven by convergence of virulence determinants and antimicrobial resistance within linked human-animal-environment systems. In China, the rapid growth of food-animal production, extensive antimicrobial use, and complex food networks are accelerating the emergence and dissemination of virulence-enhanced E. coli pathobionts. This review synthesizes recent epidemiological, genomics, and outbreak data to characterize China's evolving landscape of food-animal-associated E. coli. We highlight a significant shift from classical pathotypes to hybrid lineages that simultaneously carry virulence factors and last-resort antibiotic resistance determinants, including mcr-1, tet(X4), and blaNDM. These traits disseminate rapidly via plasmid-mediated horizontal gene transfer, facilitating rapid adaptation and enabling cross-sectoral One Health transmission. National surveillance, foodborne outbreak investigations, and whole-genome sequencing data show that food-animal reservoirs are active evolutionary niches that drive pathogen diversity and fitness, rather than serving merely as contamination sources. Whole-genome sequencing also pinpoints high-risk clones (e.g., ST394) and plasmid-mediated co-selection of virulence and AMR. The emergence of hybrid pathotypes (e.g., STEC/ETEC) and AMR-virulence co-selection challenges traditional classification and limits the effectiveness of conventional surveillance approaches. The 2017 colistin ban reduced mcr-1, yet ongoing resistance and emerging tet(X4) demand integrated surveillance. Collectively, these findings call for reconceptualizing E. coli as a dynamic genomic entity embedded within a unified ecological network. Addressing this threat requires an integrated One Health strategy including genomic surveillance, agricultural antimicrobial stewardship, and coordinated food-environment-clinical monitoring to prevent high-risk clone emergence and global spread.

Animals↗
PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗
PubMed2026

Multi‑omics approaches to decipher the molecular mechanisms of exercise‑mediated bone protection: From mechanistic insights to personalized exercise prescription (Review).

The global burden of bone metabolic disorders necessitates a shift from generic exercise recommendations toward personalized prescription strategies. Exercise confers skeletal protection through mechanotransduction, yet the underlying molecular networks remain incompletely understood. Multi‑omics technologies, including transcriptomics, proteomics, metabolomics and single‑cell spatial approaches, have revolutionized the capacity to decode exercise‑mediated bone adaptation at the systems level. The present review synthesizes current single‑omics landscapes and integrative multi‑omics analyses that elucidate the core regulatory networks, mechanobiological coupling mechanisms and multiorgan crosstalk that are implicated in the bone response to mechanical loading. Translational applications across clinical scenarios such as osteoporosis, osteoarthritis and disuse bone loss are evaluated, and the technical, analytical and translational challenges limiting clinical implementation are addressed. Finally, the present review provides a framework for translating multi‑omics molecular signatures into personalized exercise prescriptions for optimized skeletal health.

Humans↗
PubMed2025

Depth-dependent multi-kingdom microbial interactions and biogeochemical cycling genes in eutrophic shallow lake sediments.

Microorganisms are pivotal to lake ecosystem biogeochemical cycles, yet existing research often focuses on single microbial kingdoms or surface sediments, neglecting multi-kingdom interactions and depth-resolved dynamics. To address these gaps, we used metagenomic sequencing to characterize microbial communities and their functional associations across overlying water and 0-45 cm sediments in four shallow lakes of the middle Yangtze River basin, China. Despite increasing bacterial and fungal diversity with depth, the 0-9 cm surface sediments exhibited the strongest multi-kingdom network connectivity and the greatest microbial stability. Functional genes exhibited clear depth-dependent patterns: nitrogen cycling genes, including those involved in dissimilatory nitrate reduction to ammonium, were most enriched in the upper 0-9 cm of sediment; methane cycling genes were positively correlated with depth; phosphorus cycling genes and some sulfur cycling genes, such as assimilatory sulphate reduction, declined with depth. Sediment microbial assembly was dominated by deterministic processes, in which the vertical distribution of functional genes was primarily dictated by heavy metals and conventional environmental indicators. These findings highlight depth-specific multi-kingdom microbial interactions and their associations with biogeochemical cycling, advancing lacustrine microbial ecology understanding and providing references for lake conservation under environmental change.

Lakes↗
PubMed2026

Unraveling a Diagnostic Enigma: A TECPR2 Case Solved Through Multi-Omic Genomics.

TECPR2 is a key regulator of autophagy, encoded by the TECPR2 gene. Pathogenic variants in this gene have been linked to a rare hereditary sensory and autonomic neuropathy with intellectual disability (HSAN9). We report a teenage female with a syndromic intellectual disability disorder associated with neuromuscular abnormalities. Multi-omics analysis including genomics, transcriptomics, and proteomics, together with muscle biopsy from the affected individual, were used in this clinical case. Through trio exome sequencing we identified two heterozygous variants in the TECPR2 gene, NM_014844.4: c.480G>A; p.(Gln160=) and c.2846C>A; p.(Ala949Glu). Both were classified as variants of uncertain significance due to the lack of supporting evidence for pathogenicity. Subsequent long-read sequencing phased the variants and confirmed they were in trans. Additional functional studies using RNAseq and proteomics analyses verified the pathogenicity of the variants. This case study demonstrated the value of a multi-omics assisted analysis, which complemented the traditional phenotype-first approach in reaching a definitive clinical diagnosis.

Humans↗
PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗
PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗
PubMed2026

Spatially resolved multi-omics analysis of indigenous Bacillus-fortified high-temperature Daqu.

Layer-dependent patterns associated with indigenous Bacillus fortification on high-temperature Daqu remain unclear. Here, six indigenous functional Bacillus strains were combined to fortify Daqu at three inoculation levels (QH4, QH5, QH6), with non-fortified as the control (CK). Upper, middle, and lower shelf-layer samples were profiled by physicochemical measurements, volatilomics, organic acid analysis, untargeted metabolomics, 16S/ITS amplicon sequencing, and metagenomics. PERMANOVA showed significant effects of treatment, spatial layer, and their interaction on physicochemical, volatile, bacterial, and fungal profiles (P = 0.001). Among the three inoculation levels, QH5 showed the most balanced performance: QH5_M exhibited the highest observed mean peak temperature (63.3 °C; +4.5 °C relative to CK_M), and its group-mean temperature remained ≥ 60 °C for seven consecutive days. Multi-omics analyses indicated coordinated, non-linear, and layer-dependent differences associated with indigenous Bacillus fortification, with QH5_M showing the most pronounced combined thermal, pyrazine, substrate, microbial, and predicted functional profile. These findings indicate that moderate indigenous Bacillus fortification was associated with distinct layer-dependent thermal and flavor profiles and coordinated microbial, metabolic, and predicted functional differences.

Bacillus↗
PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

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&#x2009;=&#x2009;11), persistent FGR (n&#x2009;=&#x2009;9), and healthy controls (n&#x2009;=&#x2009;13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n&#x2009;=&#x2009;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&#x2009;<&#x2009;0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular&#xa0;signatures reflecting&#xa0;the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗