AIMC Topic: Escherichia coli

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Quantifying complexity in DNA structures with high resolution Atomic Force Microscopy.

Nature communications
DNA topology is essential for regulating cellular processes and maintaining genome stability, yet it is challenging to quantify due to the size and complexity of topologically constrained DNA molecules. By combining high-resolution Atomic Force Micro...

Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.

Nature communications
Protein engineering has recently seen tremendous transformation due to machine learning (ML) tools that predict structure from sequence at unprecedented precision. Predicting catalytic activity, however, remains challenging, restricting our capabilit...

Experimentally profiling dielectric properties of Escherichia coli and Staphylococcus aureus by movement velocity and force.

Scientific reports
The gradual research in integrating artificial intelligence in the Dielectrophoresis system is rapid since the evolution of AI in every aspect of technology since the early 2020s. The benefits of AI integration into DEP systems include improving posi...

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity.

mBio
Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited....

Comparative study on antibacterial activities and removal of iron ions from water using novel modified sand with silver through the hydrothermal technique.

Scientific reports
The hydrothermal-calcination technique was used to modify raw sand with silver (Ag) at different weight percentages: 2%, 5%, and 10% using silver nitrate. The raw and sand-coated Ag nanoparticle samples were analyzed using various techniques, includi...

Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.

Nature communications
The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs us...

Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning.

Scientific reports
Bacterial detection and classification are critical challenges in healthcare, environmental monitoring, and food safety, demanding selective and efficient methods. This study presents a novel, label-free approach for E. coli detection using ultrathin...

Unveiling Berberine analogues as potential inhibitors of Escherichia coli FtsZ through machine learning molecular docking and molecular dynamics approach.

Scientific reports
The bacterial cell division protein FtsZ, a crucial GTPase, plays a vital role in the formation of the contractile Z-ring, which is essential for bacterial cytokinesis. Consequently, inhibiting FtsZ could prevent the formation of proto-filaments and ...

Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria.

Microbiology spectrum
UNLABELLED: Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure effective clinical treatment. We developed a machine learning model based on metagenomic next-generat...