Antibiotic Resistance in Helicobacter pylori: Pathogenic Mechanisms and Eradication Barriers

By: Meenakshi Kandpal · Siddharth Singh · Harshita Shrivastava · Himanshi Jindal · Pranit Hemant Bagde · Amit Kumar Dixit · Parvathy G Nair · Ranjit Dey · Deepak Lamba · Sanjay Kumar Yr · Manivannan Elangovan · Rajesh Sharma · Hamendra Singh Parmar · Parimal Kar · Hem Chandra Jha

28 August 2026 at 18:00

Int J Antimicrob Agents. 2026 Aug 28:107981. doi: 10.1016/j.ijantimicag.2026.107981. Online ahead of print.

ABSTRACT

Antibiotic resistance in Helicobacter pylori is an important factor in the ineffectiveness of eradication regimens. The rate of resistance is not constant and varies widely by region and over time. Resistance is mainly due to point mutations in target genes like 23S rRNA (clarithromycin), gyrA/gyrB (fluoroquinolones), rdxA/frxA (metronidazole), and PBP1 (amoxicillin). Moreover, multi-drug resistance is mediated by efflux proteins (e.g., HefA, RND proteins), biofilm formation, and phase-variable epigenetics like DNA methylation, which regulate virulence and stress response. Immune evasion by the bacterium involves Toll-like receptor modulation, cytokine (IL-1β, TNF-α, IL-8) dysregulation, miRNA (e.g., miR-146, miR-155) modification, and persistent epigenetic field defects post-eradication, which may result in carcinogenesis via NF-κB and STAT3 signaling. H. pylori also induces gastric microbiome dysbiosis, with reduced microbial diversity, increased pro-inflammatory species, and extragastric manifestations like iron deficiency anemia, metabolic syndrome, and neurological complications. Microbiome-directed therapies, such as probiotics (Lactobacillus, Bifidobacterium), have been demonstrated to increase eradication success to 78-88%. Machine learning algorithms, including XGBoost and CNNs, accurately predict resistance from genomic sequences with over 90% sensitivity, integrating multi-omics for personalized therapy. Efflux pumps are key in multidrug resistance, while host epigenetics plays a role in bacterial persistence. Approaches include susceptibility testing, bismuth quadruple therapy, and novel adjuncts such as fecal microbiota transplantation. Prompt and personalized eradication is essential in overcoming antimicrobial resistance and preventing oncogenic transformation.

PMID:42665067 | DOI:10.1016/j.ijantimicag.2026.107981

Mitochondrial <span>l</span>-2-hydroxyglutarate is a physiological signalling metabolite

20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10564-x

l-2-Hydroxyglutarate is identified as a legitimate physiological signalling metabolite, and control of its levels is essential for postnatal growth and survival and correct renal development and function.

ContractSkill: Repairable Contract-Based Skills for Multimodal Web Agents

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

By: Zijian Lu · Yiping Zuo · Yupeng Nie · Xin He · Weibei Fan · Lianyong Qi · Shi Jin

1 April 2026 at 12:00

arXiv:2603.20340v2 Announce Type: replace-cross Abstract: Self-generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottleneck is not only skill generation quality, but the fact that web skills remain implicit and therefore cannot be checked or locally repaired. To address this, we present ContractSkill, a framework that converts a draft skill into an executable artifact with explicit procedural structure, enabling deterministic verifica tion, fault localization, and minimal local repair. This turns skill refinement from full rewriting into localized editing of a single skill artifact. Experiments on VisualWebArena show that Contract Skill is effective in realistic web environments, while MiniWoB provides a controlled test of the mechanism behind the gain. Under matched transfer layers, repaired artifacts also remain reusable after removing the source model from the loop, providing evi dence of portability within the same benchmark family rather than full-benchmark generalization. These results suggest that the central challenge is not merely generating skills, but mak ing them explicit, executable, and repairable. Code is available at https://github.com/underfitting-lu/contractskill.git.

A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction

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

By: Xiaojie Li · Zhijie Cai · Nan Qi · Chao Dong · Guangxu Zhu · Haixia Ma · Qihui Wu · Shi Jin

17 February 2026 at 13:00

arXiv:2507.14186v2 Announce Type: replace-cross Abstract: The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-altitude road test data is cost-prohibitive, often yielding only sparse samples per BS. This scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples. To overcome these obstacles, we introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning. The former reduces feature space complexity by leveraging communications expertise, while the latter enhances model generalizability through the integration of propagation models and distinct subnetworks that capture and aggregate the semantic representations of latent features. Experimental evaluation confirms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm. Real-network validations further attest to its reliability, achieving practical prediction accuracy with MAE errors at the 5dB level.

Reading view