Bacterial Infection

A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach.

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machi...

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep...

Synthesis and antimicrobial activities of chitosan/polypropylene carbonate-based nanoparticles.

Antibiotic resistance is an emerging threat to public health. The development of a new generation of antimicrobial compounds is therefore currently re...

Antibiofilm Activity of α-Amylase from Bacillus subtilis and Prediction of the Optimized Conditions for Biofilm Removal by Response Surface Methodology (RSM) and Artificial Neural Network (ANN).

α-amylase is known to have antibiofilm activity against biofilms of both Gram positive and Gram-negative bacterial strains. Partially purified α-amyla...

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films bri...

AI-based mobile application to fight antibiotic resistance.

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic suscept...

The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy develo...

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute r...

Biohybrid robotics: From the nanoscale to the macroscale.

Biohybrid robotics is a field in which biological entities are combined with artificial materials in order to obtain improved performance or features ...

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis.

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of micr...

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr by the immobilized Pseudomonas alcaliphila NEWG-2.

Heavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and sever...

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.

The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. Thi...

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is ...

iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.

Identification of bacterial type III secreted effectors (T3SEs) has become a popular research topic in the field of bioinformatics due to its crucial ...

Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria.

Present research is focused on the rapid and accurate identification of bacterial species based on artificial neural networks combined with spectral d...

Photodynamic Therapy With Propolis: Antibacterial Effects on and Escherichia coli Analysed by Atomic Force Microscopy.

Photodynamic therapy (PDT) is a process that uses a light source (e.g. laser), oxygen molecules and a photosensitizing agent. PDT aims to act against...

Anastomotic Leak is Increased With Infection After Colectomy: Machine Learning-Augmented Propensity Score Modified Analysis of 46 735 Patients.

BACKGROUND: infection (CDI) is now the most common cause of healthcare-associated infections, with increasing prevalence, severity, and mortality of ...

Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.

The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA-approved and investigational) against SARS-CoV-...

Lightweight deep learning models for detecting COVID-19 from chest X-ray images.

Deep learning methods have already enjoyed an unprecedented success in medical imaging problems. Similar success has been evidenced when it comes to t...

Label-Free Detection of Based on Bacteria-Imprinted Polymer and Turn-on Fluorescence Probes.

The effective identification and quantitative determination of is a major public health concern. Here, an innovative strategy that combines a bacteri...