Machine learning for enumeration of cell colony forming units

Synergistic enhancement of polycyclic aromatic hydrocarbon degradation by Arthrobacter sp. SZ-3 and Pseudomonas putida B6-2 under high Tween80 concentration: mechanisms and efficiency

This study investigates the advantages of combined microbial degradation of polycyclic aromatic hydrocarbons (PAHs) in reducing the inhibitory effects of high-concentration eluents commonly used in soil washing. A microbial synergistic strategy was proposed using Arthrobacter sp. SZ-3 and Pseudomonas putida B6-2 as the key bacteria in the presence of Tween 80. The results show that in systems with Tween 80, the SZ-3 strain exhibits a strong capacity to degrade three types of PAH compounds, while the B6-2 strain follows multiple degradation pathways. Mixed bacteria achieved degradation rates 60.70% higher than single bacteria at varying concentrations of Tween 80. Additionally, the average growth rates of mixed bacteria increased by 1.17-1.37 times, aligning with the changes in the functional group. Protein activity detection within each degradation system corresponded with growth quantity and the cyclic variation characteristics of ETS enzyme activity. Notably, the ETS activity of mixed bacteria was 150% higher than that of single bacteria. At a Tween 80 concentration of 500 mg/L, the degradation rates of PAHs (Phe, Flu, Pyr) by mixed bacteria were significantly higher than those by single bacteria. The catechol 1,2-dioxygenase activity of mixed bacteria was 2.30 times higher than that of single bacteria. While Tween 80 did not alter the PAH degradation pathways, it significantly influenced the accumulation amount and duration of the characteristic intermediate product. This provides a reference for the remediation of recalcitrant pollutants under conditions involving high-concentration surfactants.

Rupatadine inhibits colorectal cancer cell proliferation through the PIP5K1A/Akt/CDK2 pathway

BACKGROUND

Phosphatidylinositol-4-phosphate 5-kinase type 1 alpha (PIP5K1A) acts upstream of the Akt regulatory pathway and is abnormally expressed in many types of malignancies. However, the role and mechanism of PIP5K1A in colorectal cancer (CRC) have not yet been reported. In this study, we aimed to determine the association between PIP5K1A and progression of CRC and assess the efficacy and mechanism by which rupatadine targets PIP5K1A.

METHODS

Firstly, expression and function of PIP5K1A in CRC were investigated by human colon cancer tissue chip analysis and cell proliferation assay. Next, rupatadine was screened by computational screening and cytotoxicity assay and interactions between PIP5K1A and rupatadine assessed by kinase activity detection assay and bio-layer interferometry analysis. Next, rupatadine's anti-tumor effect was evaluated by in vivo and in vitro pharmacodynamic assays. Finally, rupatadine's anti-tumor mechanism was explored by quantitative real-time reverse-transcription polymerase chain reaction, western blot, and immunofluorescence.

RESULTS

We found that PIP5K1A exerts tumor-promoting effects as a proto-oncogene in CRC and aberrant PIP5K1A expression correlates with CRC malignancy. We also found that rupatadine down-regulates cyclin-dependent kinase 2 and cyclin D1 protein expression by inhibiting the PIP5K1A/Akt/GSK-3β pathway, induces cell cycle arrest, and inhibits CRC cell proliferation in vitro and in vivo.

CONCLUSIONS

PIP5K1A is a potential drug target for treating CRC. Rupatadine, which targets PIP5K1A, could serve as a new option for treating CRC, its therapeutic mechanism being related to regulation of the Akt/GSK-3β signaling pathway.

An Indirect Fluorescence Microscopy Method to Assess Vaginal Lactobacillus Concentrations

Lactobacillus species are the main colonizers of the vaginal microbiota in healthy women. Their absolute quantification by culture-based methods is limited due to their fastidious growth. Flow cytometry can quantify the bacterial concentration of these bacteria but requires the acquisition of expensive equipment. More affordable non-culturable methods, such as fluorescence microscopy, are hampered by the small size of the bacteria. Herein, we developed an indirect fluorescence microscopy method to determine vaginal lactobacilli concentration by determining the correlation between surface area bacterial measurement and initial concentration of an easily cultivable bacterium (Escherichia coli) and applying it to lactobacilli fluorescence microscopy counts. In addition, vaginal lactobacilli were quantified by colony-forming units and flow cytometry in order to compare these results with the indirect method results. The colony-forming-unit values were lower than the results obtained from the other two techniques, while flow cytometry and fluorescence microscopy results agreed. Thus, our developed method was able to accurately quantify vaginal lactobacilli.

Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting.

Annotated dataset for deep-learning-based bacterial colony detection

Quantifying bacteria per unit mass or volume is a common task in various fields of microbiology (e.g., infectiology and food hygiene). Most bacteria can be grown on culture media. The unicellular bacteria reproduce by dividing into two cells, which increases the number of bacteria in the population. Methodologically, this can be followed by culture procedures, which mostly involve determining the number of bacterial colonies on the solid culture media that are visible to the naked eye. However, it is a time-consuming and laborious professional activity. Addressing the automation of colony counting by convolutional neural networks in our work, we have cultured 24 bacteria species of veterinary importance with different concentrations on solid media. A total of 56,865 colonies were annotated manually by bounding boxes on the 369 digital images of bacterial cultures. The published dataset will help developments that use artificial intelligence to automate the counting of bacterial colonies.