Unveiling the Gastric Microbiome: Novel Insights into Early Detection and Pathogenesis of Gastric Cancer

By: Suresh Malchi · Kodiganti Naresh Kumar · Sreekanthyadav Pokala · Sathish Kumar Venkatachalam · Parthasarathy Ranganathan · Nallusamy Duraisamy

14 September 2026 at 18:00

Probiotics Antimicrob Proteins. 2026 Sep 14. doi: 10.1007/s12602-026-11134-3. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a major global health burden and is frequently diagnosed at advanced stages owing to the limited sensitivity, invasiveness, and restricted availability of current screening strategies. Increasing evidence indicates that the gastric microbiome including Helicobacter pylori and diverse non-H. pylori bacteria, fungi, and viruses actively contribute to gastric carcinogenesis by modulating mucosal immunity, chronic inflammation, epithelial barrier integrity, and metabolism. High-throughput sequencing has revealed reproducible dysbiosis signatures in GC, characterized by enrichment of taxa such as Lactobacillus, Streptococcus, and Fusobacterium, and depletion of beneficial commensals, including Bifidobacterium and short-chain fatty acid producing anaerobes, some of which show promise as diagnostic or prognostic biomarkers. This review summarizes current knowledge on bacterial, fungal, and viral inhabitants of gastric tumors, highlighting their mechanistic roles in tumor initiation and progression and their potential as microbial indicators of disease. It further evaluates noninvasive and minimally invasive early detection strategies based on fecal and salivary microbiota profiling, urinary extracellular vesicles, and metabolomic fingerprints, alongside multi-omics integration and machine-learning models that combine microbial and host features to improve risk stratification. Finally, the review discusses therapeutic avenues including microbiota modulation, immunotherapy microbiome interactions, and personalized medicine frameworks that incorporate microbial signatures into clinical decision-making, underscoring the need for standardized, multicenter studies to translate gastric microbiome insights into robust tools for early detection and targeted intervention in GC.

PMID:42734869 | DOI:10.1007/s12602-026-11134-3

Unveiling the Gastric Microbiome: Novel Insights into Early Detection and Pathogenesis of Gastric Cancer

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Suresh Malchi · Kodiganti Naresh Kumar · Sreekanthyadav Pokala · Sathish Kumar Venkatachalam · Parthasarathy Ranganathan · Nallusamy Duraisamy

14 September 2026 at 18:00

Probiotics Antimicrob Proteins. 2026 Sep 14. doi: 10.1007/s12602-026-11134-3. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a major global health burden and is frequently diagnosed at advanced stages owing to the limited sensitivity, invasiveness, and restricted availability of current screening strategies. Increasing evidence indicates that the gastric microbiome including Helicobacter pylori and diverse non-H. pylori bacteria, fungi, and viruses actively contribute to gastric carcinogenesis by modulating mucosal immunity, chronic inflammation, epithelial barrier integrity, and metabolism. High-throughput sequencing has revealed reproducible dysbiosis signatures in GC, characterized by enrichment of taxa such as Lactobacillus, Streptococcus, and Fusobacterium, and depletion of beneficial commensals, including Bifidobacterium and short-chain fatty acid producing anaerobes, some of which show promise as diagnostic or prognostic biomarkers. This review summarizes current knowledge on bacterial, fungal, and viral inhabitants of gastric tumors, highlighting their mechanistic roles in tumor initiation and progression and their potential as microbial indicators of disease. It further evaluates noninvasive and minimally invasive early detection strategies based on fecal and salivary microbiota profiling, urinary extracellular vesicles, and metabolomic fingerprints, alongside multi-omics integration and machine-learning models that combine microbial and host features to improve risk stratification. Finally, the review discusses therapeutic avenues including microbiota modulation, immunotherapy microbiome interactions, and personalized medicine frameworks that incorporate microbial signatures into clinical decision-making, underscoring the need for standardized, multicenter studies to translate gastric microbiome insights into robust tools for early detection and targeted intervention in GC.

PMID:42734869 | DOI:10.1007/s12602-026-11134-3

Initial Insights Into an Institutional Secure Large Language Model for Magnetic Resonance Imaging Examination Requests: Retrospective Study

8 April 2026 at 01:30

Background: Incomplete clinical details on magnetic resonance imaging (MRI) examination requests (MERs) can lead to suboptimal protocol selection. An institutional secure large language model (sLLM) with access to manually retrieved salient data from the electronic medical record (EMR) may improve request completeness and protocol accuracy across multiple MRI subspecialties. Objective: The objective of this study was to compare clinician MERs with sLLM-augmented MERs for information quality and to evaluate the protocoling accuracy of the sLLM versus board-certified radiologists across body, musculoskeletal, and neuroradiology MRI. Methods: This retrospective study included 608 random outpatient MRI examinations performed between September 2023 and July 2024 (body 206, musculoskeletal 203, neuroradiology 199). The cohort comprised 528 patients (mean 51.2 years, SD 19.2; range 4‐93; n=279, 52.8% women, n=249, 47.2% men). MERs without EMR access were excluded. A privately hosted Anthropic Claude 3.5 model (temperature 0) augmented each MER with manually retrieved salient EMR data and, via rule-based parsing, mapped the extracted elements onto predefined institutional criteria to recommend region or coverage and contrast use. Two experienced radiologists established a consensus reference standard. Two board-certified general radiologists (Rad 3 and Rad 4) and the sLLM were compared with this standard. Clinical information quality was graded using the Reason-for-Exam Imaging Reporting and Data System (RI-RADS). Interrater reliability was quantified with Gwet AC1. Paired accuracies were compared with the McNemar test to determine whether there was a statistically significant difference. Results: Interreader agreement for RI-RADS was almost perfect for sLLM-augmented MERs (AC1 0.97, 95% CI 0.94‐0.99) and moderate for clinician MERs (AC1 0.43, 95% CI 0.34‐0.52). Limited or deficient clinical information (RI-RADS C/D) fell to 0% to 0.7% (0/608 to 4/608) with sLLM augmentation vs 4.1% to 20.4% (25/608 to 124/608) for clinician MERs. Overall protocol accuracy was 93.1% (566/608; 95% CI 89.6‐96.6) for the sLLM, 91.4% (556/608; 95% CI 87.6‐95.3) for Rad 3, and 92.1% (560/608; 95% CI 88.4‐95.8) for Rad 4 (sLLM vs Rad 3 =.23 vs Rad 4 =.40). Region or coverage accuracy was similar (sLLM: 579/608, 95.2%; Rad 3: 585/608, 96.2%; Rad 4: 573/608, 94.2%; =.46 and =.36). Contrast decisions were more accurate using the sLLM at 94.4% (574/608; 95% CI 91.3‐97.5) vs Rad 3 at 92.1% (560/608; 95% CI 88.4‐95.8; =.027) and were not significantly different to Rad 4 at 92.9% (565/608; 95% CI 89.4‐96.4; =.16). Subspecialty analyses showed similar patterns, with the sLLM outperforming Rad 4 for musculoskeletal MRI contrast decisions (96.6% vs 91.1%; =.006) and matching readers elsewhere. Manual review indicated that sLLM improvements arose from EMR details not listed on the MER (infection/inflammation, tumor history, prior surgery). No clinically significant hallucinations were identified in a manual review of discordant cases. Conclusions: Across body, musculoskeletal, and neuroradiology MRI, sLLM-augmented examination requests improved clinical context and enhanced contrast selection while demonstrating accuracy comparable to general radiologists for region or coverage. Integrating sLLMs into routine vetting workflows may reduce manual workload in protocol selection for more efficient, standardized protocoling.

OSCAR: Orchestrated Self-verification and Cross-path Refinement

cs.AI, q-bio.NC updates on arXiv.org

By: Yash Shah · Abhijit Chakraborty · Naresh Kumar Devulapally · Vishnu Lokhande · Vivek Gupta

3 April 2026 at 12:00

arXiv:2604.01624v1 Announce Type: new Abstract: Diffusion language models (DLMs) expose their denoising trajectories, offering a natural handle for inference-time control; accordingly, an ideal hallucination mitigation framework should intervene during generation using this model-native signal rather than relying on an externally trained hallucination classifier. Toward this, we formulate commitment uncertainty localization: given a denoising trajectory, identify token positions whose cross-chain entropy exceeds an unsupervised threshold before factually unreliable commitments propagate into self-consistent but incorrect outputs. We introduce a suite of trajectory-level assessments, including a cross-chain divergence-at-hallucination (CDH) metric, for principled comparison of localization methods. We also introduce OSCAR, a training-free inference-time framework operationalizing this formulation. OSCAR runs N parallel denoising chains with randomized reveal orders, computes cross-chain Shannon entropy to detect high-uncertainty positions, and then performs targeted remasking conditioned on retrieved evidence. Ablations confirm that localization and correction contribute complementary gains, robust across N in {4, 8, 16}. On TriviaQA, HotpotQA, RAGTruth, and CommonsenseQA using LLaDA-8B and Dream-7B, OSCAR enhances generation quality by significantly reducing hallucinated content and improving factual accuracy through uncertainty-guided remasking, which also facilitates more effective integration of retrieved evidence. Its native entropy-based uncertainty signal surpasses that of specialized trained detectors, highlighting an inherent capacity of diffusion language models to identify factual uncertainty that is not present in the sequential token commitment structure of autoregressive models. We are releasing the codebase1 to support future research on localization and uncertainty-aware generation in DLMs.

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