AIMC Topic: Methylene Blue
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Biohybrid microrobots with a Spirulina skeleton and MOF skin for efficient organic pollutant adsorption.
Nanoscale
Mar 24, 2025
Wastewater treatment is a key component in maintaining environmental health and sustainable urban life, and the rapid development of micro/nanotechnology has opened up new avenues for more efficient treatment processes. This work developed a novel bi...
Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.
Scientific reports
Mar 1, 2025
This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory septic shock and to identify key factors influencing MB responsiveness using the SHapley Additive exPl...
An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.
Scientific reports
Jan 23, 2025
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inferen...
Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.
Scientific reports
Jul 16, 2024
Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep le...
Soft robot-mediated autonomous adaptation to fibrotic capsule formation for improved drug delivery.
Science robotics
Aug 30, 2023
The foreign body response impedes the function and longevity of implantable drug delivery devices. As a dense fibrotic capsule forms, integration of the device with the host tissue becomes compromised, ultimately resulting in device seclusion and tre...
Ultrasound-enhanced catalytic degradation of simulated dye wastewater using waste printed circuit boards: catalytic performance and artificial neuron network-based simulation.
Environmental monitoring and assessment
Nov 24, 2022
Recent developments of heterogeneous advanced oxidation for refractory organic contaminants and catalysts made of solid waste have attracted much attention. In this work, waste printed circuit board (wPCB) was used for catalytic degradation of simula...
Intelligent modeling and experimental study on methylene blue adsorption by sodium alginate-kaolin beads.
International journal of biological macromolecules
Jul 5, 2021
As tighter regulations on color in discharges to water bodies are more widely implemented worldwide, the demand for reliable inexpensive technologies for dye removal grows. In this study, the removal of the basic dye, methylene blue, by adsorption on...
Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane.
Environmental research
Jan 21, 2020
Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independ...
Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.
Annals of internal medicine
Aug 14, 2018
BACKGROUND: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost.
Robotic blue-dye sentinel lymph node detection for endometrial cancer - Factors predicting successful mapping.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Sep 25, 2015
OBJECTIVE: Sentinel lymph node (SLN) mapping has emerged as a viable option for the treatment of patients with endometrial cancer. We report our initial experience with SLN mapping algorithm, and examine the factors predicting successful SLN mapping.
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