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Antibiotic Resistance in Helicobacter pylori: Pathogenic Mechanisms and Eradication Barriers

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

Optimizing Sensor Placement for Flow Reconstruction in Urban Drainage Networks: A Digital Twin-Based Sparse Sensing Approach

arXiv:2511.04556v2 Announce Type: replace Abstract: Urban flooding triggered by intense rainfall is becoming increasingly frequent and widespread. While flood prediction and monitoring in high spatio-temporal resolution are desired, practical constraints in time, budget, and technology hinder its full implementation. How to monitor urban drainage networks and predict flow conditions under constrained resources is a major challenge. To address this, we introduced a data-driven sparse sensing (DSS) approach, demonstrated via a digital-twin of the Woodland catchment in Duluth, Minnesota. Specifically, we coupled EPA-SWMM with singular value decomposition and QR factorization-based sensor selection to optimize monitoring locations for system-level flow reconstruction. An ensemble of SWMM simulations, driven by diverse scenarios, provided the necessary hydraulic data to extract the reduced basis and identify informative sensor locations. Cross-event validation showed that three strategically placed sensors among 77 candidate nodes achieved a mean system-level Nash-Sutcliffe efficiency (NSE) of 0.949 across observed storm events. The QR-selected sensor sets were benchmarked against reference sensor configurations obtained from exhaustive searches and Monte Carlo random-placements. This comparison further showed that flow reconstruction based on QR-selected sensors closely tracked the exhaustive optimum while substantially outperforming random placements. We further evaluated the framework's robustness by introducing multiplicative Gaussian noise and simulating individual sensor failures. While the model is relatively resilient to noise, the impact of sensor dropouts depends heavily on the number of sensors allocated and their specific locations.
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