eXtreme gradient boosting-based classification of bacterial mixtures in water and milk using wireless microscopic imaging of quorum sensing peptide-conjugated particles

Machine learning classification of quorum sensing-induced bacterial aggregation using flow rate assays on paper chips toward bacterial species identification in potable water sources

Preventing waterborne disease caused by bacteria is especially important in low-resource settings, where skilled personnel and laboratory equipment are scarce. This work reports a straightforward method for classifying bacterial species by monitoring the capillary flow rates on a multi-channel paper microfluidic chip, where quorum sensing (QS)-induced bacterial aggregation leads to measurable changes in flow rates, enabling species differentiation. It required no fluorescent molecules, microscope, particles, covalent conjugation, or surface immobilization. Five representative QS molecules and control were added to each bacterial sample, and their different extents of bacterial aggregation resulted in varied flow rates. Flow rates were collected for the duration of the flow to build the learning database, and the XGBoost machine learning algorithm predicted the accuracy for classifying ten bacterial species, including 7 gram-negative and 3 gram-positive species. Three different algorithms were developed for high, medium, and low bacterial concentration ranges, and the classification accuracies of all the algorithms exceeded 75.0 %. Using XGBoost and the previously established database, we tested bacteria in the field water samples and successfully predicted the dominant species. The technology developed in this study, using only QS molecules and a paper microfluidic chip, offers a simple system for detecting microorganisms in drinking water to help prevent waterborne diseases.

Low-cost, multispectral machine learning classification of simulated airborne micro/nanoplastics

This study presents a novel smartphone-based, machine-learning-assisted multispectral classification method for identifying airborne micro- and nanoplastics (MNPs). Instead of commercial polymeric microspheres, coffee grinder-based cryogrinding generated nonuniform MNPs from real-world plastic products with highly irregular shapes and heterogenous size distributions. The low-cost handheld device comprises a smartphone, a spectral mask array made from plastic color films, and a discrete multiplexed illumination device. A stack of images was captured across multiple wavelength ranges, and the RGB ratios were extracted without using morphological information. An XGBoost model was trained on two datasets: dry and wet MNP samples passively collected on a glass slide, simulating two types of airborne MNPs. The model successfully distinguished plastics from clay with 89-99 % accuracy and classified six plastic types with 79-87 % accuracy for dry and wet MNPs. This method offers a promising toolkit for airborne MNP monitoring.

Machine Learning-Enhanced Bacteria Detection Using a Fluorescent Sensor Array with Functionalized Graphene Quantum Dots

Pathogenic bacteria are the source of many serious health problems, such as foodborne diseases and hospital infections. Timely and accurate detection of these pathogens is of vital significance for disease prevention, control of epidemic spread, and protection of public health security. Rapid identification of pathogenic bacteria has become a research focus in recent years. In contrast to traditional large-scale detection equipment, the fluorescent sensor array developed in this study can detect bacteria within just five min and is cost-effective. The array employs nitrogen- and sulfur-doped graphene quantum dots (NS-GQDs) synthesized through a simple hydrothermal process, making it environmentally friendly by avoiding toxic metal elements. Functionalized with antibiotics, spectinomycin, kanamycin, and polymyxin B, the NS-GQDs (renamed as S-NS-GQDs, K-NS-GQDs, and B-NS-GQDs) exhibit variable affinities for different bacteria, enabling broad-spectrum detection without targeting specific species. Upon binding with bacteria, the fluorescence intensity of the functionalized NS-GQDs decreases significantly. The sensor array exhibits distinct fluorescence responses to different bacterial species, which can be distinguished by using various machine learning algorithms. The results demonstrate that the platform can quickly and accurately identify and quantify five bacterial species, showing excellent performance in terms of accuracy, sensitivity, and stability. This makes it a promising tool with great practical application prospects in pathogenic bacterial detection.

Progress of machine learning-based biosensors for the monitoring of food safety: A review

Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their application. The emergence of machine learning (ML) has enhanced the efficiency of different sensors for food safety assessment. The ML combined with various noninvasive biosensors has been implemented efficiently to monitor food safety by considering the stability of bio-recognition molecules. This review comprehensively summarizes the application of ML-powered biosensors to investigate food safety. Initially, different detector-based biosensors using biological molecules with their advantages and disadvantages and biosensor-related various ML algorithms for food safety monitoring have been discussed. Next, the application of ML-powered biosensors to detect antibiotics, foodborne microorganisms, mycotoxins, pesticides, heavy metals, anions, and persistent organic pollutants has been highlighted for the last five years. The challenges and prospects have also been deliberated. This review provides a new prospect in developing various biosensors for multi-food contaminants powered by suitable ML algorithms to monitor in-situ food safety.

Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study

BACKGROUND

IgA nephropathy (IgAN) is intimately linked to mucosal immune responses, with nasopharyngeal and intestinal lymphoid tissues being crucial for its abnormal mucosal immunity. The specific pathogenic bacteria in these sites associated with IgAN, however, remain elusive. Our study employs 16S rRNA sequencing and machine learning (ML) approaches to identify specific pathogenic bacteria in these locations and to investigate common pathogens that may exacerbate IgAN.

METHODS

In this cross-sectional analysis, we collected pharyngeal swabs and stool specimens from IgAN patients and healthy controls. We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. Spearman correlation analysis was employed to link key bacteria with clinical parameters.

RESULTS

We observed a reduced microbial diversity in IgAN patients compared to healthy controls. In the gut microbiota of IgAN patients, increases in Bacteroides, Escherichia-Shigella, and Parabacteroides, and decreases in Parasutterella, Dialister, Faecalibacterium, and Subdoligranulum were notable. In the respiratory microbiota, increases in Neisseria, Streptococcus, Fusobacterium, Porphyromonas, and Ralstonia, and decreases in Prevotella, Leptotrichia, and Veillonella were observed. Post-immunosuppressive therapy, Oxalobacter and Butyricoccus levels were significantly reduced in the gut, while Neisseria and Actinobacillus levels decreased in the respiratory tract. Veillonella and Fusobacterium appeared to influence IgAN through dual immune loci, with Fusobacterium abundance correlating with IgAN severity.

CONCLUSIONS

This study revealing that changes in flora structure could provide important pathological insights for identifying therapeutic targets, and ML could facilitate noninvasive diagnostic methods for IgAN.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

PURPOSE

We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.

METHODS

The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.

RESULTS

At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).

CONCLUSION

The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

Soil microbiome characterization and its future directions with biosensing

Soil microbiome characterization is typically achieved with next-generation sequencing (NGS) techniques. Metabarcoding is very common, and meta-omics is growing in popularity. These techniques have been instrumental in microbiology, but they have limitations. They require extensive time, funding, expertise, and computing power to be effective. Moreover, these techniques are restricted to controlled laboratory conditions; they are not applicable in field settings, nor can they rapidly generate data. This hinders using NGS as an environmental monitoring tool or an in-situ checking device. Biosensing technology can be applied to soil microbiome characterization to overcome these limitations and to complement NGS techniques. Biosensing has been used in biomedical applications for decades, and many successful commercial products are on the market. Given its previous success, biosensing has much to offer soil microbiome characterization. There is a great variety of biosensors and biosensing techniques, and a few in particular are better suited for soil field studies. Aptamers are more stable than enzymes or antibodies and are more ready for field-use biosensors. Given that any microbiome is complex, a multiplex sensor will be needed, and with large, complicated datasets, machine learning might benefit these analyses. If the signals from the biosensors are optical, a smartphone can be used as a portable optical reader and potential data-analyzing device. Biosensing is a rich field that couples engineering and biology, and applying its toolset to help advance soil microbiome characterization would be a boon to microbiology more broadly.

Machine learning-assisted liquid crystal-based aptasensor for the specific detection of whole-cell Escherichia coli in water and food

We have developed a rapid, facile liquid crystal (LC)-based aptasensor for E. coli detection in water and juice samples. A textile grid-anchored LC platform was used with specific aptamers adsorbed via a cationic surfactant, cetyltrimethylammonium bromide (CTAB), on the LC surface. The presence of E. coli dissociates the aptamers from CTAB and restores the dark signal induced by the surfactant. Using polarized microscopy, the images of the LCs in the presence of various concentrations of E. coli were captured and analyzed using image analysis and machine learning (ML). The artificial neural networks (ANN) and extreme gradient boosting (XGBoost) rendered the best results for water samples (R2 = 0.986 and RMSE = 0.209) and juice samples (R2 = 0.976 and RMSE = 0.262), respectively. The platform was able to detect E. coli with a detection limit (LOD) of 6 CFU mL-1.