A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration

Automated identification of toxigenic cyanobacterial genera for water quality control purposes

Cyanobacteria are the dominating microorganisms in aquatic environments, posing significant risks to public health due to toxin production in drinking water reservoirs. Traditional water quality assessments for abundance of the toxigenic genera in water samples are both time-consuming and error-prone, highlighting the urgent need for a fast and accurate automated approach. This study addresses this gap by introducing a novel public dataset, TCB-DS (Toxigenic Cyanobacteria Dataset), comprising 2593 microscopic images of 10 toxigenic cyanobacterial genera and subsequently, an automated system to identify these genera which can be divided into two parts. Initially, a feature extractor Convolutional Neural Network (CNN) model was employed, with MobileNet emerging as the optimal choice after comparing it with various other popular architectures such as MobileNetV2, VGG, etc. Secondly, to perform classification algorithms on the extracted features of the first section, multiple approaches were tested and the experimental results indicate that a Fully Connected Neural Network (FCNN) had the optimal performance with weighted accuracy and f1-score of 94.79% and 94.91%, respectively. The highest macro accuracy and f1-score were 90.17% and 87.64% which were acquired using MobileNetV2 as the feature extractor and FCNN as the classifier. These results demonstrate that the proposed approach can be employed as an automated screening tool for identifying toxigenic Cyanobacteria with practical implications for water quality control replacing the traditional estimation given by the lab operator following microscopic observations. The dataset and code of this paper are publicly available at https://github.com/iman2693/CTCB.

Cyanotoxins availability and detection methods in wastewater treatment plants: A review

Research interest in ecological significance, toxicity, and potential applications of cyanobacterial metabolites has grown as a result of the current extensive cyanobacterial blooms in water bodies. Under favourable conditions, specific cyanobacterial species release cyanotoxins, hepatotoxins, dermatoxins, neurotoxins, and cytotoxins, creating a heightened threat to aquatic ecosystems and human health. Wastewater treatment plants (WWTPs) offer one of the best culture media for cyanobacterial development and synthesis of cyanotoxins by providing optimum environmental conditions, including temperature, light intensity, lengthy water residence time, and nutrient-rich habitat. To discover the intricate relationships between cyanobacterial populations and other living organisms, it is important to comprehend the cyanobacterial communities in the ecology of WWTPs. Monitoring strategies of these cyanotoxins typically involved combined assessments of biological, biochemical, and physicochemical methodologies. Microscopic observations and physicochemical factors analysis cannot be carried out for toxicity potential analysis of blooms. Due to their high sensitivity, molecular-based approaches allow for the early detection of toxic cyanobacteria, while biological analysis is carried out by using water bloom material and cell extracts to screen cyanotoxins build up in organisms. As each approach has benefits and drawbacks, the development of an integrated multi-method laboratory system is essential to obtain trustworthy results and accurate detection of cyanotoxin levels in WWTPs allowing us to take necessary proactive and preventative approaches for effective wastewater treatment.

Chronological development of functional fluorophores for bio-imaging

Functional fluorophores represent an emerging research field, distinguished by their diverse applications, especially in sensing and cellular imaging. After the discovery of quinine sulfate and subsequent elucidation of the fluorescence mechanism by Sir George Stokes, research in the field of fluorescence gained momentum. Over the past few decades, advancements in sophisticated instruments, including super-resolution microscopy, have further promoted cellular imaging using traditional fluorophores. These advancements include deciphering sensing mechanisms via photochemical reactions and scrutinizing the applications of fluorescent probes that specifically target organelles. This approach elucidates molecular interactions with biomolecules. Despite the abundance of literature illustrating different classes of probe development, a concise summary of newly developed fluorophores remains inadequate. In this review, we systematically summarize the chronological discovery of traditional fluorophores along with new fluorophores. We briefly discuss traditional fluorophores ranging from visible to near-infrared (NIR) in the context of cellular imaging and in vivo imaging. Furthermore, we explore ten new core fluorophores developed between 2007 and 2022, which exhibit advanced optical properties, providing new insights into bioimaging. We illustrate the utilization of new fluorophores in cellular imaging of biomolecules, such as reactive oxygen species (ROS), reactive nitrogen species (RNS), and proteins and microenvironments, especially pH and viscosity. Few of the fluorescent probes provided new insights into disease progression. Furthermore, we speculate on the potential prospects and significant challenges of existing fluorophores and their potential biomedical research applications. By addressing these aspects, we intend to illuminate the compelling advancements in fluorescent probe development and their potential influence across various fields.

Using FlowCam and molecular techniques to assess the diversity of Cyanobacteria species in water used for food production

Globally, the occurrence of cyanobacteria in water currently remains an important subject as they produce cyanotoxins that pose threat to human health. Studies on the contamination of maize meals during mill grinding processes using cyanobacteria-contaminated water have not been conducted. The present study aimed to assess the diversity of cyanobacteria in the samples (process water, uncooked maize meal, and cooked maize meal (porridge)). Polymerized Chain Reaction (PCR) and Advanced digital flow cytometry (FlowCAM) were used to detect and identify cyanobacterial species available in these samples. 16S Primers (forward and reverse) tailed with Universal Sequences were used for amplification and sequencing of full-length 16S rRNA genes from cyanobacteria found in all samples. Cyanobacterial species from order Nostocales, Pseudanabaenales, Oscillatoriales Chroococcales, Synechococcales, and unclassified cyanobacterial order, some of which have the potential to produce cyanotoxins were amplified and identified in process water, raw maize meal and porridge samples using PCR. Images of the genus Microcystis, Phormidium, and Leptolyngbya were captured in process water samples using FlowCAM. These findings show the presence of cyanobacteria species in process water used for maize meal and the absence in cooked maize meal. The presence of cyanobacteria in process water is likely another route of human exposure to cyanotoxins.