Biology subjects

Irene, D.

Publications and source records attributed to Irene, D..

2 recordsLinked to original sources

bioRxiv2026

Particle Swarm Optimization with Random Forest Surrogates Modelling for Rational Design of Antimicrobial Fluoride Toothpaste Formulations against Clinically Significant Oral Pathogens

To make effective antimicrobial toothpastes, you need to optimize many parts that work together. Creating new formulations the old-fashioned way takes a lot of time and money. This research formulates and substantiates a methodological framework that combines systematic antimicrobial susceptibility testing with Particle Swarm Optimization (PSO) to enhance toothpaste formulations against clinically significant oral pathogens. Using a D-optimal mixture design, we made 24 different toothpaste formulations by changing the type of fluoride (NaF, MFP, SnF2), the concentration of fluoride (1000-1500 ppm), the concentration of SLS (0.5-2.5%), the type of abrasive (silica, calcium carbonate, dicalcium phosphate), and the concentration of abrasive (10-30%). We used agar well diffusion and minimum inhibitory concentration (MIC) tests to see how well the drugs worked against Streptococcus mutans ATCC 25175, Porphyromonas gingivalis ATCC 33277, and Lactobacillus acidophilus ATCC 4356. A Random Forest surrogate model was trained on 120 experimental data points (24 formulations x 5 concentrations) and validated through 10-fold cross-validation. Multi-objective PSO was used to improve the effectiveness of antimicrobials, the availability of fluoride, and the cost of the formulation. Chosen PSO-predicted formulations underwent experimental validation. The antimicrobial activity changed a lot (p < 0.001) depending on the formulation parameters. The optimized formulation (sodium fluoride 1120 ppm, SLS 2.3%, hydrated silica 18%, pH 7.2) showed 28.4 {+/-} 1.2 mm of inhibition against S. mutans, 26.8 {+/-} 1.4 mm against P. gingivalis, and 24.2 {+/-} 1.1 mm against L. acidophilus. These were improvements of 18.5%, 22.3%, and 19.8%, respectively, over the best commercial comparator. Experimental validation corroborated PSO predictions with a mean absolute error of 5.2%. Multi-objective Optimization found Pareto-optimal formulations that let you choose based on trade-offs between effectiveness, safety, and cost. Combining systematic experimental design with PSO gives a tested framework for making rational toothpaste formulations. This method significantly lowers the amount of work needed for experiments while also allowing for the Optimization of multiple competing formulation goals.

microbiology↗

bioRxiv2024

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the images signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation and L1 denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

bioinformatics↗