The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index; none of the 98 sources its claims cite is checked for support yet.
SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning
Open accessAbstract
Objective:To develop scanning electron microscopy-based Trainable Weka (Waikato Environment for Knowledge Analysis) Intelligent Segmentation Technology (SEMTWIST), an open-source software tool, for structural detection and rigorous quantification of wound biofilm aggregates in complex human wound tissue matrix. Approach:SEMTWIST model was standardized to quantify biofilm infection (BFI) abundance in 240 distinct SEM images from 60 human chronic wound-edge biospecimens (four technical replicates of each specimen). Results from SEMTWIST were compared against human expert assessments and the gold standard for molecular BFI detection, that is, peptide nucleic acid fluorescence in situ hybridization (PNA-FISH). Results:Correlation and Bland–Altman plot demonstrated a robust correlation (r = 0.82, p < 0.01), with a mean bias of 1.25, and 95% limit of agreement ranging from −43.40 to 47.11, between SEMTWIST result and the average scores assigned by trained human experts. While interexpert variability highlighted potential bias in manual assessments, SEMTWIST provided consistent results. Bacterial culture detected infection but not biofilm aggregates. Whereas the wheat germ agglutinin staining exhibited nonspecific staining of host tissue components and failed to provide a specific identification of BFI. The molecular identification of biofilm aggregates using PNA-FISH was comparable with SEMTWIST, highlighting the robustness of the developed approach. Innovation:This study introduces a novel approach “SEMTWIST” for in-depth analysis and precise differentiation of biofilm aggregates from host tissue elements, enabling accurate quantification of BFI in chronic wound SEM images. Conclusion:Open-source SEMTWIST offers a reliable and robust framework for standardized quantification of BFI burden in human chronic wound-edge tissues, supporting clinical diagnosis and guiding treatment.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
SEMTWIST is an open-source, Trainable Weka-based machine-learning tool for segmenting and quantifying biofilm infection (BFI) in scanning electron microscopy images of human chronic wound-edge tissue. It showed strong correlation with expert human ratings and molecular PNA-FISH detection, while culture and WGA staining were less reliable for biofilm-specific assessment.
Key findings
- SEMTWIST BFI quantification correlated strongly with average trained human expert scores: r = 0.82, p < 0.01, with mean bias 1.25 and 95% limits of agreement from −43.40 to 47.11.
- Human expert raters showed substantial interrater variability: mean bias 17.8 and wide limits of agreement from −33.34 to 68.93, indicating low reliability/reproducibility of manual BFI assessment.
- Among 15 patient samples, 8 were culture-negative; 5 of those 8 culture-negative biospecimens showed dense biofilm structures by SEM, and PNA-FISH detected polymicrobial BFI-like structures in culture-negative samples.
- WGA staining was nonspecific in human chronic wound tissue and showed only weak correlation with TWIST BFI quantification (r = 0.24). Image-resolution sensitivity testing showed BFI abundances of 42.6%, 26.4%, and 25.8% at 320 × 256, 960 × 768, and 2,560 × 2,048 pixels, respectively.
Methods
- Blinded subset of 240 SEM images from 60 human chronic wound-edge biospecimens, with four technical replicates per specimen; 160 SEM images were used to train the software for BFI and non-BFI features.
- SEM images were processed with TWIST using a fast random forest classifier and feature filters including Gaussian blur, Sobel, Hessian, difference of Gaussians, membrane projections, variance, mean, minimum, maximum, and median; postprocessing used histogram thresholding and k-means clustering to calculate percentage biofilm coverage.
- Validation compared SEMTWIST against four blinded human biofilm experts rating 60 SEM images from 15 subjects, PNA-FISH for S. aureus and P. aeruginosa, WGA-Alexa Fluor 488 staining, and standard bacterial culture; analysis used correlation, regression, and Bland–Altman plots.
Limitations
- Paper-stated: human expert assessment is subjective, time-intensive, limited by expert availability, and prone to interobserver variability; the study observed interrater variability and concluded human rating is valid only when performed by >3 raters.
- Paper-stated: TWIST performance decreased at lower pixel densities, and low-resolution SEM images with rare (<50%) biofilm may be unreliable; high-resolution images require more processing time and power.
- Evident from the text: the paper has no dedicated limitations section, and the SEMTWIST validation used a blinded subset from an ongoing, unreported clinical trial whose primary outcomes are not reported here.
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Claims the paper makes
40 Check pending · 27 Own result · 21 Own interpretation · 10 No basis given · 9 Own method · 7 No full text of the sources · 3 Not checked yet · 1 Sources not found
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In cases of low-resolution SEM images, TWIST-based detection was less susceptible to errors when biofilm abundance in the image is high, that is, when >75% of the SEM image is covered by biofilm structures (image 2, Supplementary Fig. S3).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Analysis of image sensitivity in SEMTWIST segmentation
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“The biospecimens with negative culture reports showed the presence of polymicrobial BFI-like structures when examined using PNA-FISH (Fig. 6A)”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Validation using traditional biofilm methodologies
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In the paper
“The constituents of wound-edge tissue, including red blood cells (RBC), white blood cells (WBC), fibrin, activated platelets, and extracellular matrix (ECM), were distinguishable alongside the biofilm (Fig. 5A, B).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
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In the paper
“In general, the performance of the ML algorithm decreased at lower pixel densities (Fig. 4B).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Analysis of image sensitivity in SEMTWIST segmentation
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whereas the wheat germ agglutinin staining exhibited nonspecific staining of host tissue components and failed to provide a specific identification of BFI.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > Abstract > Results:
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Correlation and Bland–Altman plot demonstrated a robust correlation (r = 0.82, p < 0.01), with a mean bias of 1.25, and 95% limit of agreement ranging from −43.40 to 47.11, between SEMTWIST result and the average scores assigned by trained human experts.”
- Also stated in: § SEM-ML tool for BFI assessment · boosted
“As shown in Fig. 2E, F, a strong correlation (r = 0.82, p < 0.01) was observed between the results from human experts and those produced by SEM-ML analysis, which was further validated by the Bland–Altman plot (Fig. 2G).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > Abstract > Results:
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § SEM-ML tool for BFI assessment · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a cohort of 15 patient samples analyzed in this study, eight showed negative results for the presence of any bacterial species in culture reports.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Validation using traditional biofilm methodologies
plainn = 15- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“PNA-FISH specifically detected S. aureus and P. aeruginosa involvement in wound BFI.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, other features observed included activated platelets that appeared as elongated spheres with spicules, neutrophils with a ruffled morphology, and monocytes with pseudopods such as protrusions.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observed that SEM-ML analysis was able to reliably detect all the abovementioned grades of BFI by accurately segmenting BFI and non-BFI regions.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > SEM-ML tool for BFI assessment
plaindirect- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 39
- It may state two separate claims as one. 28
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, other features observed included activated platelets that appeared as elongated spheres with spicules, neutrophils with a ruffled morphology, and monocytes with pseudopods such as protrusions.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that the images are derived from chronic human wound-edge tissue samples, notable morphological alterations in RBC shape and ECM topography were observed, as depicted in Fig. 5A (Supplementary Fig. S4).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While interexpert variability highlighted potential bias in manual assessments, SEMTWIST provided consistent results.”
- Also stated in: § SEM-ML tool for BFI assessment · plain
“Nonetheless, correlation analysis revealed interrater variability in the human analysis, highlighting the subjective nature of human assessment (Fig. 2H).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > Abstract > Results:
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Also stated in: § SEM-ML tool for BFI assessment · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Bacterial culture detected infection but not biofilm aggregates.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > Abstract > Results:
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This combination provided the most effective segmentation within the shortest time frame.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, other features observed included activated platelets that appeared as elongated spheres with spicules, neutrophils with a ruffled morphology, and monocytes with pseudopods such as protrusions.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These filters helped in identifying irregular surface features of bacterial biofilms, characterized by their clustered microbial cells and thick EPS matrix by emphasizing the subtle variations in topography, texture, intensity, and contrast within the image. This resulted in a reliable distinction between biofilm aggregates and surrounding host tissue features with enhanced efficiency.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Substantial variability between the trained human raters was also noted in the Bland–Altman plot as shown by mean bias and the wide LoAs 17.8 (−33.34 to 68.93) (Supplementary Fig. S1).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > SEM-ML tool for BFI assessment
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, fibrins were identified as thread-like structures.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
plain- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
In the paper
“The European Society of Clinical Microbiology and Infectious Diseases recommends using SEM as one of the most reliable diagnostic techniques for detecting biofilms in wounds.23,24”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
boosted- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In the fastidious state, bacteria within biofilm exhibit metabolic dormancy, which slows down their growth.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The conventional methods, such as colony forming unit (CFU) assays and bacterial culture, can only identify planktonic bacterial growth and may not be reliable in detecting BFI in wounds because of the slow and fastidious characteristics of the growth of biofilm bacteria.12,16”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Not checked yetcites [12] source
- Not checked — no full text of the sourcecites [16] Wilson C, 2017, Res Rev J Eng Technol
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Biofilms in chronic wounds act as hostile microbial aggregates as they remain encased within their polymeric matrix cocoons, making them resistant to both antimicrobial therapy and host immune defenses.1”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Check pendingcites [1] source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Chronic wounds with biofilms are more likely to develop recalcitrant severe infections, leading to complications such as amputation and tissue necrosis.3”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Check pendingcites [3] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The high resolution of SEM allows detailed analysis of biofilm structures, allowing direct visualization by achieving a resolution of 50 to 100 nm.25”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Check pendingcites [25] source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Reliable and robust clinical assessment of BFI burden in wounds is critical to determining treatment strategy.12,13”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The extent to which the BFI burden negatively impacts wound healing outcomes depends on the balance between BFI threat and countering defenses, including host factors and therapeutic intervention.1,3,14,15”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Diagnosing BFI in chronic wounds remains challenging, with no standardized clinical protocol currently established for clinical research practice.8-11”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Biofilms exhibit up to 1,000-fold increased resistance to antibiotics compared with planktonic bacteria, making treatment challenging.6”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [6] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“When PNA probes are attached to a fluorochrome dye, they can be detected by epifluorescence microscopy or flow cytometry using the FISH method.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This work represents a maiden effort seeking in-depth characterization using the SEM-ML approach to identify typical structures of BFI-infected or noninfected features in chronic wound SEM images.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Understanding these complex structural interactions is crucial for accurate and reliable biofilm quantification, ultimately supporting improved diagnostics and treatment strategies.48”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INNOVATION > Clinical problem addressed
What the sources say
- Not checked — no full text of the sourcecites [48] source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“It is important to recognize that WGA has a high affinity for N-acetylglucosamine residues and glycoproteins, which are present in abundance in both bacterial biofilm EPS and in human wound tissue.70”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
- Check pendingcites [70] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“PNA-FISH has been established as a productive approach to detect and characterize wound biofilm in human tissues.76-78”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Bacteria within biofilm aggregates are encased in a dense EPS, which poses challenges for effective bacterial separation and quantification.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Conventional SEM and FESEM are the two rigorous methods for high magnification and high-resolution visualization of biofilm morphology.21”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
- Check pendingcites [21] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This resolution allows for in-depth examination of fine bacterial surface features, biofilm architecture, and nanoscale bacterial interactions.21”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Check pendingcites [21] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Weka, an open-source software tool that enables users to perform ML-based image segmentation, object classification, and feature extraction with minimal programming experience,33-35 has been widely used in biological and clinical research to quantitatively analyze various types of microorganisms, cells, and tissues.36-40”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
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In the paper
“We performed sensitivity analysis for TWIST performance on five randomly selected SEM images.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
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In the paper
“Open-source SEMTWIST offers a reliable and robust framework for standardized quantification of BFI burden in human chronic wound-edge tissues, supporting clinical diagnosis and guiding treatment.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > Abstract > Conclusion:
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In the paper
“A blinded subset of 240 SEM images (n = 60 subjects) from an ongoing, unreported clinical trial (IRB# 10011, Indiana University) were used for SEMTWIST optimization and validation. These images were analyzed without the knowledge of subject identity or clinical outcome.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
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In the paper
“SEM images were coded and provided to four biofilm experts (3–4 years of experience with wound BFI and SEM images) for blinded evaluation.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
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In the paper
“The specificity of WGA to specifically detect biofilm has therefore been questioned.75”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
- Check pendingcites [75] source
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In the paper
“Deep-learning procedures based on convolutional neural networks have already been utilized to automate the SEM image analysis. However, such solutions have been practiced mostly in the context of material sciences.61-64”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Interestingly, a strong correlation was noted between the human expert rater averages and the SEM-ML approach, demonstrating the validity and robustness of the SEM-ML-based assessment of clinical BFI abundance in human wound specimens.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > SEM-ML tool for BFI assessment
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In the paper
“Chronic wound-edge SEM images are complex and consist of diverse features.53-56”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
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In the paper
“Weka, an open-source software tool that enables users to perform ML-based image segmentation, object classification, and feature extraction with minimal programming experience,33-35 has been widely used in biological and clinical research to quantitatively analyze various types of microorganisms, cells, and tissues.36-40”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
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In the paper
“Images were captured from four different regions/locations within the same tissue sample to ensure comprehensive coverage of the tissue regions.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
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In the paper
“Fluorescence-based techniques such as wheat germ agglutinin (WGA) staining have shown promising results for in vitro biofilm experiments with limited efficacy for detecting biofilms within infected wound tissue samples.42,43”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INNOVATION > Clinical problem addressed
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In the paper
“The article has been designed as per the Strengthening the Reporting of Observational Studies in Epidemiology guidelines (Fig. 1).”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast, PNA probes using FISH have been designed and optimized for the detection of several bacteria.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Recognizing these morphological variations is critical for training the software to accurately distinguish biofilm dispersed within these tissue components in an SEM image.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Training SEMTWIST for specificity
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Rests on an assumption: Biofilm may be embedded within or interspersed between host tissue structures in wound-edge tissue.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even with mechanical disruption techniques such as vortexing or ultrasonication, biofilm aggregates often remain intact, leading to skewed results where clumps of bacteria may get erroneously counted as single colonies.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“WGA, a widely used lectin-based stain, is often used for the detection of EPS-encasing biofilm aggregates.42,43,70”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“PNA-FISH specifically identifies bacterial species and displays aggregation characteristics thus helping identify and characterize BFI.44”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > Validation using traditional biofilm methodologies
What the sources say
- Check pendingcites [44] source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reading such images requires trained human experts limiting the significance of these analytical approaches as it relates to clinical research use.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While WGA may be useful to detect biofilm on abiotic structures, in a tissue environment it can crossreact with tissue components.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plainconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Similarly, reverse transcription-polymerase chain reaction evaluates biofilm-related gene expression but does not offer insights into the spatial organization of the EPS matrix and biofilm morphology.45-47”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INNOVATION > Clinical problem addressed
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By integrating software capabilities with human assessments and establishing rigorous gradation criteria, the BFI-assessing TWIST-model sets a new benchmark for biofilm quantification.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“Advanced methods such as peptide nucleic acid fluorescence in situ hybridization (PNA-FISH)44 provide insights into the microbial composition and distribution but do not offer details about the structural and topographic interactions between biofilms and tissues that SEM provides.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INNOVATION > Clinical problem addressed
What the sources say
- Check pendingcites [44] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No background subtraction or filtering techniques were applied before segmentation, ensuring that the integrity of the tissue features was preserved for our study.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“SEM images often contain noise, artifacts, or irregularities in tissue architecture that can obscure biofilm structures.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Various ML algorithms within the TWIST framework have been used for several pixel-based binary segmentation tasks across different image modalities, including bacterial counting, growth assessment, and cell segmentation.41”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
- Not matchedcites [41] Kanuri N, 2022, Cureus
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Although the visual system of humans, with expert knowledge, can interpret and provide relative BFI abundance in SEM images from wound-edge tissue,26 such assessments are primarily subjective, time-intensive, limited by the availability of expert raters, and prone to interobserver variability.27-30”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > INTRODUCTION
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This study introduces a novel approach “SEMTWIST” for in-depth analysis and precise differentiation of biofilm aggregates from host tissue elements, enabling accurate quantification of BFI in chronic wound SEM images.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > Abstract > Innovation:
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These images were taken from different regions of each sample (left, right, and center) to minimize bias.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
plaindirectn = 15- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By applying the training strategy similar to the approach used that was used for classifying RBCs, WBCs, fibrin, and platelets in wound tissue SEM images, the infected bone model will be able to differentiate distinct features of that tissue, ensuring precise classification and segmentation of bone microstructures from BFI.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION > Generalizability
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“This abrupt environmental shift can lead to bacterial stress, reduced viability, or even cell death.80”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
- Not checked — no full text of the sourcecites [80] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As demonstrated in the current study, where TWIST was trained to differentiate between biofilm and nonbiofilm features in wound SEM images, the same approach can be applied to specialized datasets for studies focused on other tissues such as bone and muscle.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION > Generalizability
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Therefore, selecting the appropriate image resolution for TWIST involves balancing the desired analysis with the available computational resources, as higher resolutions require significant processing time and power.81”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
- Not checked — no full text of the sourcecites [81] source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In certain medical applications, such as pathological imaging and digital mammography, high-resolution images often need to be downsampled due to the limitations of storage capacity.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consequently, culture results, which typically require 1–3 days to obtain, can lead to an underestimation of biofilm abundance and inaccurate data interpretation, making this method unsuitable for early and precise diagnosis of biofilm presence in the clinical samples.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As an example for infected bone tissue sample, the TWIST ML algorithm needs to be trained for distinguishing specific features in the SEM images from bone tissue.”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION > Generalizability
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In this context, it is important to recognize that best results may be obtained from high-resolution images only when an appropriate classification model and interpolation methods are used.49”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION
What the sources say
- Not checked — no full text of the sourcecites [49] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The model must be trained to accurately identify key structural components, including the layered bone matrix (lamellae), honeycomb-like network (cancellous bone), osteoblasts, osteoclasts, osteocytes, mineralized bone matrix, and canaliculi.82”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > DISCUSSION > Generalizability
What the sources say
- Check pendingcites [82] source
plain- Asserted: fails — The authors may not put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Wound biofilm is a polymicrobial aggregate including host cells.
“SEM analysis reveals that wound biofilm, shaped by an iterative exchange between microbial and host-defense processes, is a polymicrobial aggregate including host cells.”
The paper relies on this without support.
Wound biofilm aggregates in chronic human wounds exhibit unique characteristics, and their quantification in clinical samples has long been challenging due to limited understanding of complex structural interactions between biofilm and wound tissue.
“Wound biofilm aggregates in chronic human wounds exhibit unique characteristics, and their quantification in clinical samples has long been challenging due to limited understanding of the complex structural interactions between biofilm and the wound tissue.”
The paper relies on this without support.
Gradation of BFI abundance is essential to diagnose BFI promptly, allowing for implementation of effective treatment strategies and significantly reducing risk of poor outcomes.
“This gradation of BFI abundance is essential to diagnose BFI promptly, allowing for the implementation of effective treatment strategies and significantly reducing the risk of poor outcomes.”
The paper relies on this without support.
A reliable quantification approach is essential for clinical research studies evaluating antibiofilm treatments, where BFI reduction is a primary outcome.
“A reliable quantification approach is also essential for clinical research studies evaluating antibiofilm treatments, where BFI reduction is a primary outcome.”
The paper relies on this without support.
One of the challenges of SEM-based BFI detection in wound-edge tissue is the lack of availability of robust and objective quantification methods.
“One of the challenges of SEM-based BFI detection in wound-edge tissue is the lack of availability of robust and objective quantification methods.”
The paper relies on this without support.
Variability in human expert assessments often stems from differences in expertise, image noise, and complex anatomical architecture of tissues.
“This variability often stems from differences in expertise, image noise, and the complex anatomical architecture of tissues.”
The paper relies on this without support.
Biofilm may be embedded within or interspersed between host tissue structures in wound-edge tissue.
“Given that the biofilm may be embedded within, or interspersed between these structures, accurate segmenting for these structures is necessary.”
The paper relies on this without support.
Human scores are valuable as a gold standard for biofilm quantification.
“Therefore, while human scores are valuable as a gold standard, it is essential to account for potential biases and discrepancies that may arise from human evaluations.67-69”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“the mean bias and 95% LoA was 1.25 with −43.40 to 47.11”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > RESULTS > SEM-ML tool for BFI assessment
Reported 1.25 · recomputed 1.855
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
“≤10% (no biofilm, with prominent host tissue features visible)”
§ SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning > MATERIALS AND METHODS > Methods
“10% to <40% (low biofilm, characterized by few bacteria and associated EPS matrix observed)”
Citations
Cites
Flemming, H.-C., & Wingender, J. (2010) · Nature Reviews Microbiology · doi:10.1038/nrmicro2415 · Cited by 10,137
Biofilms: Microbial Life on Surfaces
Donlan, R. M. (2002) · Emerging Infectious Diseases · doi:10.3201/eid0809.020063 · Cited by 4,145
Understanding bland altman analysis
Giavarina, D. (2015) · Biochemia Medica · doi:10.11613/bm.2015.015 · Cited by 4,022
Cubic convolution interpolation for digital image processing
Keys, R. (1981) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1981.1163711 · Cited by 3,255
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
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Human Wound and Its Burden: Updated 2025 Compendium of Estimates
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Metadata
- Authors
- Singh, S., Muniz De Oliveira, F., Wang, C., Kumar, M., Xuan, Y., DeMazumder, D., Sen, C. K., & Roy, S.
- Affiliations
- University of Pittsburgh (US)
- Department of Surgery, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
- Corresponding author
- Roy, Sashwati
- Venue
- Advances in Wound Care
- Publisher
- SAGE Publications
- Published
- 2025-08-01
- Volume
- 14
- Issue
- 8
- Pages
- 393-408
- ISSN
- 2162-1918 (ISSN-L)
- 2162-1934
- PMC
- PMC12359142
- OpenAlex
- W4410313918
- Cited by
- 6
- References
- 75
- Citations by year
- 2026: 5 · 2025: 1
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- green
- pmc.ncbi.nlm.nih.gov
- Locations
- Advances in Wound Care (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (DP2HL157941)
- National Institute of General Medical Sciences (K25GM143572)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125835)
- Topics
- Health Sciences › Medicine › Surgery › Wound Healing and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bacterial biofilms and quorum sensing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- chronic wounds, biofilm quantification, image segmentation, machine learning, scanning electron microscopy, PNA-FISH, interobserver variability, open source software
- MeSH terms
- Machine Learning, Chronic Disease, Humans, Microscopy, Electron, Scanning, Software, Wound Infection, In Situ Hybridization, Fluorescence, Biofilms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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