Pith. sign in

REVIEW 1 major objections 1 minor 46 references

An AI-directed analytical study on the optical transmission microscopic images of Pseudomonas aeruginosa in planktonic and biofilm states

T0 review · 1 major / 1 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper reports that a U-Net with a ResNet34 encoder detects Pseudomonas aeruginosa biofilm in bright-field microscope images with 86.62% accuracy, and that aptamer-templated silver nanoclusters visibly prevent biofilm formation.

desk verdict A modest, honest U-Net segmentation application on a small new biofilm dataset, with a qualitative Ag-NC result that needs stronger evidence. read the letter →

arxiv 2412.18205 v1 pith:ZW6E33GY submitted 2024-12-24 physics.med-ph physics.bio-phphysics.optics

Bidisha Sengupta , Mousa Alrubayan , Yibin Wang , Esther Mallet , Angel Torres , Ravyn Solis , Haifeng Wang , Prabhakar Pradhan This is my paper · ORCID
classification physics.med-phphysics.bio-phphysics.optics
keywords Pseudomonasaeruginosabiofilmbright-fieldmicroscopyU-NetResNetimagesegmentationsilvernanoclustersaptamer
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper attempts to establish that a deep-learning model, specifically a U-Net whose encoder is a ResNet34, can segment Pseudomonas aeruginosa biofilm from ordinary bright-field transmission microscope images with an accuracy of 86.62%, an F1 score of 71.43%, and an intersection-over-union of 59.70%. The same study reports that aptamer-DNA-templated silver nanoclusters prevent or reduce biofilm formation, making the bacteria remain in a planktonic state that is visible in the images. The motivation is practical: biofilms resist antibiotics and contaminate medical devices and food, so a fast, label-free detection method plus a preventive nanocluster treatment would be useful in healthcare and industry. The paper also shows that the deeper ResNet34 encoder outperforms ResNet18 on this segmentation task, suggesting that additional learned features improve biofilm detection.

What carries the argument

The central object is the U-Net-ResNet34 segmentation model. U-Net is an encoder-decoder convolutional network with skip connections that pass high-resolution features from the contracting path to the expanding path, and the ResNet encoder adds residual skip connections that let the network learn deeper features without vanishing gradients. The model outputs a biofilm probability map per pixel, which is then binarized with Otsu thresholding to produce the predicted mask for comparison with manual ground truth. The other load-bearing component is the aptamer-DNA-templated silver nanocluster, synthesized on a Pseudomonas-specific aptamer, whose effect on biofilm is read out by turbidity clearing in the culture wells and by the absence of biofilm in the bright-field images.

What would settle it

Re-annotate the same bright-field fields using an independent biofilm assay, such as crystal violet staining, confocal or scanning electron microscopy, or a genetic biofilm marker, and recompute the segmentation metrics; a large drop in IoU would mean the original manual labels were not true biofilm. Separately, perform a live/dead viability stain and viable cell counts on Ag-NC-treated wells: if cells remain alive but do not form biofilm, the prevention claim holds, but if the cells are dead, the effect is bactericidal rather than antibiofilm-specific.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is twofold. First, a U-Net architecture with a ResNet34 encoder, trained on 150 annotated bright-field micrographs and tested on 34, produces biofilm probability maps that, after Otsu thresholding, align with expert hand-drawn masks; the authors report 86.62% accuracy, 71.12% precision, 73.84% recall, 71.43% F1-score, and 59.70% IoU, with the ResNet34 backbone beating ResNet18 on every metric. Second, incubating Pseudomonas aeruginosa with aptamer-DNA-templated silver nanoclusters clears the turbidity that the authors use as a biofilm indicator, and the bright-field images show planktonic cells rather than biofilm aggregates, which the authors interpret as prevention of 2D biofilm formation. The two claims are connected: the AI model provides a quantitative, statistical way to detect biofilm presence or absence in the large volume of images, and the Ag-NC treatment is the intervention whose effect the model can measure.

Load-bearing premise

The hand-drawn ground-truth masks on the bright-field images are assumed to mark the biofilm correctly, and the clearing of turbidity after silver-nanocluster treatment is assumed to mean biofilm prevention rather than bacterial death or reduced growth; if either assumption fails, the reported accuracies and the antibiofilm conclusion would be measured against the wrong phenomenon.

Editorial extensions

If this is right

  • Standard bright-field microscopes could be used to screen for P. aeruginosa biofilm without stains or labels, since the model segments biofilm directly from transmission images.
  • The reported performance metrics give a quantitative baseline that future biofilm segmentation models can be compared against.
  • The same U-Net-ResNet34 pipeline could be retrained for other biofilm-forming species or other imaging modalities, since the architecture is generic.
  • The Ag-NC treatment, if it acts by preventing biofilm formation rather than killing cells, offers an antibiofilm strategy that keeps bacteria in a planktonic state and could reduce the need for antibiotics.
  • The automated large-volume imaging plus AI analysis could allow many samples to be screened quickly, which matters for healthcare, food safety, and environmental monitoring.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not benchmark its model against a non-AI method such as plain intensity thresholding, so the specific contribution of the deep network is untested; a direct comparison would show how much of the 86.62% accuracy is due to the architecture rather than to the image statistics.
  • Because the annotated dataset has only 184 images and the ground-truth labels came from the same group that collected the images, testing on a larger set annotated independently by other labs would establish whether the model generalizes beyond this microscope and these slide preparations.
  • The Ag-NC prevention claim rests on turbidity clearing; adding a quantitative biofilm assay such as crystal violet staining or viable cell counts would distinguish biofilm prevention from bacteriostatic or bactericidal effects.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 1 minor

Summary. The paper reports two connected claims: (i) a U-Net with ResNet18/ResNet34 backbones can segment Pseudomonas aeruginosa biofilms in large-volume bright-field transmission microscopy images, with the ResNet34 model achieving 86.62% accuracy, 71.12% precision, 73.84% recall, 71.43% F1-score, and 59.70% IoU on a held-out test set; and (ii) aptamer-DNA templated silver nanoclusters (Ag-NC) added to PA cultures prevent biofilm formation, as inferred from clearing of turbidity in culture wells. The AI pipeline uses 184 manually annotated bright-field images, split approximately 150/34 into training and test sets, with Otsu thresholding applied to probability maps. The authors conclude that the model can detect biofilm with high accuracy and that Ag-NC reduces 2D biofilm formation.

Significance. If the segmentation claim were supported, the paper would offer a relatively accessible deep-learning tool for biofilm detection in standard bright-field microscopy, which is valuable because biofilms are clinically and environmentally important. The comparison of ResNet18 and ResNet34 backbones within a U-Net framework is a reasonable, if standard, methodological exercise, and the authors are transparent about overfitting appearing after roughly ten epochs. The Ag-NC prevention claim rests on turbidity, which is a much weaker readout, but the authors do connect it to their earlier spectroscopy work. The main significance is thus as a proof-of-concept for automated biofilm segmentation in this specific imaging setup, rather than as a definitive biological or clinical study. The lack of independent ground-truth validation and of statistical error assessment materially limits the strength of the reported quantitative results.

major comments (1)
  1. [§1 and §4] The paper claims that the AI model 'can be applied to any image to detect biofilm formation,' but the model is trained and tested on a single organism (Pseudomonas aeruginosa), a single imaging modality (bright-field transmission), a single magnification (40x), and a single laboratory setup. No external validation on other organisms, imaging conditions, or clinical/environmental samples is provided. This generalization goes beyond the evidence and should be tempered or explicitly supported with additional experiments.
minor comments (1)
  1. [References] Reference 23 is a raw URL without author/title details and should be converted to a proper citation; other references have inconsistent formatting (e.g., volume/page formatting for refs 4, 16, 22, 33).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the segmentation metrics are genuine held-out evaluations and the prevention claim is an empirical proxy-based assay, not a derivation from its own conclusion.

full rationale

The paper's central AI claim is a standard supervised segmentation evaluation: the U-Net-ResNet models are trained on ~150 annotated bright-field images and tested on 34 held-out images with manually produced masks, and Table 1 reports test-set accuracy, precision, recall, F1, and IoU. The network parameters are optimized only against training labels, so the reported test metrics are out-of-sample predictions conditional on the annotation protocol; no fitted parameter or test label enters the training objective. The biofilm-prevention claim rests on the observation that aptamer-DNA templated Ag-NC cleared broth turbidity, interpreted as inhibition of biofilm formation using a turbidity proxy cited from prior work [7,44]. That is an experimental-validation limitation, not a circular derivation: the conclusion is not mathematically forced by the premise, and the proxy is an empirical calibration rather than a definitional identity. Self-citations appear for synthesis protocols and spectral characterization, but the present paper repeats those measurements and reports agreement, so the self-citations are not load-bearing in a way that makes the central claim equivalent to its inputs. Overall, the derivation chain is self-contained with respect to circularity, and the main risks are label validity and assay specificity rather than circular reasoning.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. Ag-NC on aptamer DNA comes from prior work [8,43], and the deep learning model is a standard architecture. The load-bearing assumptions are about label reliability, turbidity as a biofilm proxy, and the transferability of the imaging method to real biofilm states.

free parameters (4)
  • learning_rate = 5e-4
    Adam optimizer learning rate set by hand in Section 3; no ablation or tuning analysis reported.
  • batch_size = 16
    Batch size chosen for ResNet18 and ResNet34; no justification is given for this value.
  • input_image_size = 512x512
    All images and masks are resized to 512x512; this choice affects detail and is not motivated or ablated.
  • early_stopping_epoch = approximately 10
    Models started overfitting after about ten epochs and the best model was saved; epoch count is a post hoc selection from the training curves in Figure 5.
assumptions (5)
  • domain assumption Manually annotated biofilm masks are accurate ground truth
    Section 2.6 says domain experts collected and validated masks; no independent method such as crystal violet staining, confocal, or SEM is used to confirm that the annotated bright-field regions are biofilm.
  • domain assumption Turbidity in culture wells indicates biofilm formation
    Section 2.2 cites prior work for using turbidity as a biofilm indicator; Section 3 uses turbidity clearing to conclude Ag-NC prevents biofilm, without a quantitative biofilm assay.
  • standard math U-Net with ResNet encoders generalizes from training to unseen bright-field images
    This is a standard deep learning architecture assumption accepted in the cited literature; the paper adds no theoretical guarantee.
  • domain assumption The aptamer DNA sequence is specific to Pseudomonas aeruginosa
    Adopted from reference [42]; specificity is not re-tested in this work beyond silver nanocluster formation.
  • domain assumption Heat-fixed cells on glass slides represent planktonic and biofilm states
    Section 2.2 heat-fixes cells before imaging; fixation can alter morphology and may not preserve 3D biofilm architecture.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An AI-directed analytical study on the optical transmission microscopic images of Pseudomonas aeruginosa in planktonic and biofilm states." pith.science (2026). https://pith.science/paper/ZW6E33GY

@misc{pith2026241218205,
  author       = {Pith},
  title        = {Pith review of: An AI-directed analytical study on the optical transmission microscopic images of Pseudomonas aeruginosa in planktonic and biofilm states},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZW6E33GY}},
  note         = {Machine review of arXiv:2412.18205}
}
read the original abstract

Biofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 46 canonical work pages

  1. [1]

    Cohn, Beiträge Zur Biologie Der Bacillen, 7 (Beiträge zur Biologie der Pflanzen

    F. Cohn, Beiträge Zur Biologie Der Bacillen, 7 (Beiträge zur Biologie der Pflanzen. Untersuchungen über Bacterien, 1877), V ol. IV , pp. 249–276

    work page

  2. [2]

    Sticking together: building a biofilm the Bacillus subtilis way,

    H. Vlamakis, Y . Chai, P. Beauregard, R. Losick, and R. Kolter, "Sticking together: building a biofilm the Bacillus subtilis way," Nat. Rev. Microbiol. 11, 157–168 (2013)

    work page 2013

  3. [3]

    Mechanisms and Impact of Biofilms and Targeting of Biofilms Using Bioactive Compounds—A Review,

    A. V . Samrot, A. Abubakar Mohamed, E. Faradjeva, L. Si Jie, C. Hooi Sze, A. Arif, T. Chuan Sean, E. Norbert Michael, C. Yeok Mun, N. Xiao Qi, P. Ling Mok, and S. S. Kumar, "Mechanisms and Impact of Biofilms and Targeting of Biofilms Using Bioactive Compounds—A Review," Medicina (Mex.) 57, 839 (2021)

    work page 2021

  4. [4]

    Biofilm Formation and Cell Surface Properties among Pathogenic and Nonpathogenic Strains of the Bacillus cereus Group,

    S. Auger, N. Ramarao, C. Faille, A. Fouet, S. Aymerich, and M. Gohar, "Biofilm Formation and Cell Surface Properties among Pathogenic and Nonpathogenic Strains of the Bacillus cereus Group," Appl. Environ. Microbiol. 75, 6616–6618 (2009)

    work page 2009

  5. [5]

    Role of Bacillus species in biofilm persistence and emerging antibiofilm strategies in the dairy industry,

    M. Shemesh and I. Ostrov, "Role of Bacillus species in biofilm persistence and emerging antibiofilm strategies in the dairy industry," J. Sci. Food Agric. 100, 2327–2336 (2020)

    work page 2020

  6. [6]

    The effects of ferulic and salicylic acids on Bacillus cereus and Pseudomonas fluorescens single - and dual -species biofilms,

    M. Lemos, A. Borges, J. Teodósio, P. Araújo, F. Mergulhão, L. Melo, and M. Simões, "The effects of ferulic and salicylic acids on Bacillus cereus and Pseudomonas fluorescens single - and dual -species biofilms," Int. Biodeterior. Biodegrad. 86, 42–51 (2014)

    work page 2014

  7. [7]

    Influence of Aptamer -Enclosed Silver Nanocluster on the Prevention of Biofilm by Bacillus thuringiensis,

    B. Sengupta, S. S. Sinha, B. L. Garner, I. Arany, C. Corley, K. Cobb, E. Brown, and P. C. Ray, "Influence of Aptamer -Enclosed Silver Nanocluster on the Prevention of Biofilm by Bacillus thuringiensis," Nanosci. Nanotechnol. Lett. 8, 1054–1060 (2016)

    work page 2016

  8. [8]

    Spectroscopic Study on Pseudomonas Aeruginosa Biofilm in the Presence of the Aptamer -DNA Scaffolded Silver Nanoclusters,

    B. Sengupta, P. Adhikari, E. Mallet, R. Havner, and P. Pradhan, "Spectroscopic Study on Pseudomonas Aeruginosa Biofilm in the Presence of the Aptamer -DNA Scaffolded Silver Nanoclusters," Molecules 25, 3631 (2020)

    work page 2020

Show all 46 references
  1. [9]

    Pseudomonas aeruginosa Biofilms,

    M. T. T. Thi, D. Wibowo, and B. H. A. Rehm, "Pseudomonas aeruginosa Biofilms," Int. J. Mol. Sci. 21, 8671 (2020)

    2020

  2. [10]

    The Clinical Importance of Fungal Biofilms,

    G. Ramage and C. Williams, "The Clinical Importance of Fungal Biofilms," in Advances in Applied Microbiology (Elsevier, 2013), V ol. 84, pp. 27–83

    2013

  3. [11]

    Biofilm Formation as a Pathogenicity Factor of Medically Important Fungi,

    T. V . M. Vila and S. Rozental, "Biofilm Formation as a Pathogenicity Factor of Medically Important Fungi," in Fungal Pathogenicity, S. Sultan, ed. (InTech, 2016)

    2016

  4. [12]

    Fungal Biofilms,

    S. Fanning and A. P. Mitchell, "Fungal Biofilms," PLOS Pathog. 8, e1002585 (2012)

    2012

  5. [13]

    Biofilm Formation in Medically Important Candida Species,

    Z. Malinovská, E. Čonková, and P. Váczi, "Biofilm Formation in Medically Important Candida Species," J. Fungi 9, 955 (2023)

    2023

  6. [14]

    Understanding biofilm resistance to antibacterial agents,

    D. Davies, "Understanding biofilm resistance to antibacterial agents," Nat. Rev. Drug Discov. 2, 114–122 (2003)

    2003

  7. [15]

    Antimicrobial Activity of Selected Phytochemicals against Escherichia coli and Staphylococcus aureus and Their Biofilms,

    J. Monte, A. C. Abreu, A. Borges, L. C. Simões, and M. Simões, "Antimicrobial Activity of Selected Phytochemicals against Escherichia coli and Staphylococcus aureus and Their Biofilms," Pathogens 3, 473–498 (2014)

    2014

  8. [16]

    Biofilms and their role on diseases,

    V . S. Gondil and B. Subhadra, "Biofilms and their role on diseases," BMC Microbiol. 23, 203, s12866-023-02954–2 (2023)

    2023

  9. [17]

    Risk factors for chronic biofilm -related infection associated with implanted medical devices,

    P. S. Stewart and T. Bjarnsholt, "Risk factors for chronic biofilm -related infection associated with implanted medical devices," Clin. Microbiol. Infect. 26, 1034–1038 (2020)

    2020

  10. [18]

    The Role of Bacterial Biofilm in Antibiotic Resistance and Food Contamination,

    G. M. Abebe, "The Role of Bacterial Biofilm in Antibiotic Resistance and Food Contamination," Int. J. Microbiol. 2020, 1–10 (2020)

    2020

  11. [19]

    Biofilms in Medicine, Industry and Environmental Biotechnology - Characteristics, Analysis and Control,

    P. Lens, V . O’Flaherty, A. Moran, P. Stoodley, and T. Mahony, "Biofilms in Medicine, Industry and Environmental Biotechnology - Characteristics, Analysis and Control," Water Intell. Online 6, 9781780402161–9781780402161 (2015)

    2015

  12. [20]

    Medical Device-Associated Infections Caused by Biofilm- Forming Microbial Pathogens and Controlling Strategies,

    A. Mishra, A. Aggarwal, and F. Khan, "Medical Device-Associated Infections Caused by Biofilm- Forming Microbial Pathogens and Controlling Strategies," Antibiotics 13, 623 (2024)

    2024

  13. [21]

    Bacterial biofilm and associated infections,

    M. Jamal, W. Ahmad, S. Andleeb, F. Jalil, M. Imran, M. A. Nawaz, T. Hussain, M. Ali, M. Rafiq, and M. A. Kamil, "Bacterial biofilm and associated infections," J. Chin. Med. Assoc. 81, 7–11 (2018)

    2018

  14. [22]

    Biofilm vs. planktonic bacterial mode of growth: Which do human macrophages prefer?,

    E. Hernández-Jiménez, R. Del Campo, V . Toledano, M. T. Vallejo-Cremades, A. Muñoz, C. Largo, F. Arnalich, F. García-Rio, C. Cubillos-Zapata, and E. López-Collazo, "Biofilm vs. planktonic bacterial mode of growth: Which do human macrophages prefer?," Biochem. Biophys. Res. Com...

    2013

  15. [23]

    The use of superparamagnetic nanoparticles for prosthetic biofilm prevention,

    "The use of superparamagnetic nanoparticles for prosthetic biofilm prevention," https://www.tandfonline.com/doi/epdf/10.2147/IJN.S5976?needAccess=true&role=button

    doi

  16. [24]

    Recent progress on magnetic iron oxide nanoparticles: synthesis, surface functional strategies and biomedical applications,

    W. Wu, Z. Wu, T. Yu, C. Jiang, and W. -S. Kim, "Recent progress on magnetic iron oxide nanoparticles: synthesis, surface functional strategies and biomedical applications," Sci. Technol. Adv. Mater. 16, 023501 (2015)

    2015

  17. [25]

    Advanced Nanotechnological Approaches for Biofilm Prevention and Control,

    M. P. Ferraz, "Advanced Nanotechnological Approaches for Biofilm Prevention and Control," Appl. Sci. 14, 8137 (2024)

    2024

  18. [26]

    Recent Advances in Surface Nanoengineering for Biofilm Prevention and Control. Part I: Molecular Basis of Biofilm Recalcitrance. Passive Anti -Biofouling Nanocoatings,

    P. C. Balaure and A. M. Grumezescu, "Recent Advances in Surface Nanoengineering for Biofilm Prevention and Control. Part I: Molecular Basis of Biofilm Recalcitrance. Passive Anti -Biofouling Nanocoatings," Nanomaterials 10, 1230 (2020)

    2020

  19. [27]

    Enhanced antibacterial and anti -biofilm activities of silver nanoparticles against Gram -negative and Gram -positive bacteria,

    S. Gurunathan, J. W. Han, D. -N. Kwon, and J. -H. Kim, "Enhanced antibacterial and anti -biofilm activities of silver nanoparticles against Gram -negative and Gram -positive bacteria," Nanoscale Res. Lett. 9, 373 (2014)

    2014

  20. [28]

    Interactions of Gold and Silver Nanoparticles with Bacterial Biofilms: Molecular Interactions behind Inhibition and Resistance,

    A. S. Joshi, P. Singh, and I. Mijakovic, "Interactions of Gold and Silver Nanoparticles with Bacterial Biofilms: Molecular Interactions behind Inhibition and Resistance," Int. J. Mol. Sci. 21, 7658 (2020)

    2020

  21. [29]

    Silver, Its Salts and Application in Medicine and Pharmacy,

    D. Żyro, J. Sikora, M. I. Szynkowska -Jóźwik, and J. Ochocki, "Silver, Its Salts and Application in Medicine and Pharmacy," Int. J. Mol. Sci. 24, 15723 (2023)

    2023

  22. [30]

    Anti - biofilm and Antibacterial Activities of Silver Nanoparticles Synthesized by the Reducing Activity of Phytoconstituents Present in the Indian Medicinal Plants,

    Y . K. Mohanta, K. Biswas, S. K. Jena, A. Hashem, E. F. Abd_Allah, and T. K. Mohanta, "Anti - biofilm and Antibacterial Activities of Silver Nanoparticles Synthesized by the Reducing Activity of Phytoconstituents Present in the Indian Medicinal Plants," Front. Microbiol. 11, 1...

    2020

  23. [31]

    Effect of Biosynthesized Silver Nanoparticles on Bacterial Biofilm C hanges in S. aureus and E. coli,

    B. Hosnedlova, D. Kabanov, M. Kepinska, V . H. B Narayanan, A. A. Parikesit, C. Fernandez, G. Bjørklund, H. V . Nguyen, A. Farid, J. Sochor, A. Pholosi, M. Baron, M. Jakubek, and R. Kizek, "Effect of Biosynthesized Silver Nanoparticles on Bacterial Biofilm C hanges in S. aureu...

    2022

  24. [32]

    Silver Nanoparticles: Bactericidal and Mechanistic Approach against Drug Resistant Pathogens,

    P. R. More, S. Pandit, A. D. Filippis, G. Franci, I. Mijakovic, and M. Galdiero, "Silver Nanoparticles: Bactericidal and Mechanistic Approach against Drug Resistant Pathogens," Microorganisms 11, 369 (2023)

    2023

  25. [33]

    Development of Aptamer Beacons for Rapid Presumptive Detection of Bacillus Spores,

    J. G. Bruno and M. P. Carrillo, "Development of Aptamer Beacons for Rapid Presumptive Detection of Bacillus Spores," J. Fluoresc. 22, 915–924 (2012)

    2012

  26. [34]

    Fluorescence Assay Based on Aptamer-Quantum Dot Binding to Bacillus thuringiensis Spores,

    M. Ikanovic, W. E. Rudzinski, J. G. Bruno, A. Allman, M. P. Carrillo, S. Dwarakanath, S. Bhahdigadi, P. Rao, J. L. Kiel, and C. J. Andrews, "Fluorescence Assay Based on Aptamer-Quantum Dot Binding to Bacillus thuringiensis Spores," J. Fluoresc. 17, 193–199 (2007)

    2007

  27. [35]

    New Insights into Aptamers: An Alternative to Antibodies in the Detection of Molecular Biomarkers,

    M. Domsicova, J. Korcekova, A. Poturnayova, and A. Breier, "New Insights into Aptamers: An Alternative to Antibodies in the Detection of Molecular Biomarkers," Int. J. Mol. Sci. 25, 6833 (2024)

    2024

  28. [36]

    Real-time diagnosis and monitoring of biofilm and corrosion layer formation on different water pipe materials using non -invasive imaging methods,

    H. R. Im, S. J. Im, D. V . Nguyen, S. P. Jeong, and A. Jang, "Real-time diagnosis and monitoring of biofilm and corrosion layer formation on different water pipe materials using non -invasive imaging methods," Chemosphere 361, 142577 (2024)

    2024

  29. [37]

    Deep Residual Learning for Image Recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in (2016), pp. 770–778

    2016

  30. [38]

    The Importance of Skip Connections in Biomedical Image Segmentation,

    M. Drozdzal, E. V orontsov, G. Chartrand, S. Kadoury, and C. Pal, "The Importance of Skip Connections in Biomedical Image Segmentation," in Deep Learning and Data Labeling for Medical Applications, G. Carneiro, D. Mateus, L. Peter, A. Bradley, J. M. R. S. Tavares, V . Belagian...

    2016

  31. [39]

    A novel approach for biofilm detection based on a convolutional neural network,

    G. Dimauro, F. Deperte, R. Maglietta, M. Bove, F. La Gioia, V . Renò, L. Simone, and M. Gelardi, "A novel approach for biofilm detection based on a convolutional neural network," Electronics 9, 881 (2020)

    2020

  32. [40]

    U -Net: Convolutional Networks for Biomedical Image Segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, "U -Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, eds., Lecture Notes in Computer Scie...

    2015

  33. [41]

    Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma,

    J. Le’Clerc Arrastia, N. Heilenkötter, D. Otero Baguer, L. Hauberg-Lotte, T. Boskamp, S. Hetzer, N. Duschner, J. Schaller, and P. Maass, "Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma," J. Imaging 7, 71 (2021)

    2021

  34. [42]

    Aptamer-mediated colorimetric and electrochemical detection of Pseudomonas aeruginosa utilizing peroxidase -mimic activity of gold NanoZyme,

    R. Das, A. Dhiman, A. Kapil, V . Bansal, and T. K. Sharma, "Aptamer-mediated colorimetric and electrochemical detection of Pseudomonas aeruginosa utilizing peroxidase -mimic activity of gold NanoZyme," Anal. Bioanal. Chem. 411, 1229–1238 (2019)

    2019

  35. [43]

    Base- Directed Formation of Fluorescent Silver Clusters,

    B. Sengupta, C. M. Ritchie, J. G. Buckman, K. R. Johnsen, P. M. Goodwin, and J. T. Petty, "Base- Directed Formation of Fluorescent Silver Clusters," J. Phys. Chem. C 112, 18776–18782 (2008)

    2008

  36. [44]

    Critical Assessment of Methods to Quantify Biofilm Growth and Evaluate Antibiofilm Activity of Host Defence Peptides,

    E. Haney, M. Trimble, J. Cheng, Q. Vallé, and R. Hancock, "Critical Assessment of Methods to Quantify Biofilm Growth and Evaluate Antibiofilm Activity of Host Defence Peptides," Biomolecules 8, 29 (2018)

    2018

  37. [45]

    Biomedical Image Segmentation with Modified U -Net,

    U. Tatli and C. Budak, "Biomedical Image Segmentation with Modified U -Net," Trait. Signal 40, 523–531 (2023)

    2023

  38. [46]

    A Threshold Selection Method from Gray -Level Histograms,

    N. Otsu, "A Threshold Selection Method from Gray -Level Histograms," IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979). Disclaimer/Publisher’s Note: The authors declare no conflicts o

    1979

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.