Powerpoint presentation based on a research paper regarding the use of Machine Learning in Sepsis Diagnosis (Paper from Frontiers in Immunology). *Contains original images that explain machine learning models*: XGBoost GaussianNB(GNB) RandomForest

1. Name: Nimmy Pious
Reg. No.: 221703024
Semester: 3rd MSc: MBHG
Manipal School of Life Sciences, MAHE, Manipal
Authors: Zetian Wang et al.
Journal: Frontiers in Immunology
Published year: 2023
Impact factor: 8.786
Quartile: Q1
Circulating sepsis-related metabolite sphinganine could
protect against intestinal damage during sepsis

2. Screenshot of the paper
2

3. CONTENTS
• INTRODUCTION
• AIM
• METHODOLOGY
• RESULTS
• DISCUSSION
• CONCLUSION
• REFERENCES
3

4. Introductio
n
1
Sepsis
• severe and potentially life-threatening condition
• triggered by bacterial, viral, or fungal infection
• body's response to an infection that causes
widespread inflammation and organ dysfunction
Intestinal damage during sepsis
• sepsis triggers the release of cytokines
• reduced blood supply to the intestines
• leads to damage to the cells lining the intestines
• compromised intestinal barrier- microbial
translocation
Symptoms:
• fever
• rapid heart rate
• rapid breathing
• low blood pressure
• confusion
• organ dysfunction
Fig1. Development of Sepsis
By SickKidsStaff 2011, Development of Sepsis, Sepsis, AboutKidsHealth

5. Introduction
1
Sphinganine
• a fundamental building block of sphingolipids
• naturally present in the body
• found in various tissues and cell membranes
• helps in cell structure, signaling, and regulation of cellular
processes
• studies have shown its potential therapeutic applications

6. Machine Learning (ML)
1
• a subset of AI
• computer systems are able to learn and adapt without
following explicit instructions using algorithms and
statistical models to extract patterns from large databases
• Machine learning techniques can enhance the predictive
power of disease prediction models
Machine Learning Models
Five machine learning models were used:
• Logistic Regression
• Support vector machines(SVM)
• XGBoost
• GaussianNB(GNB)
• RandomForest
Fig2. Visual Representation of Logistic Regression
Haq Nawaz 2022, Logistic Regression Overview, Medium
Fig3. Visual
Representation
of SVM
Support Vector
Machines Tutorial
– Learn to
implement SVM in
Python(n.d.),
DataFlair

7. Machine Learning
1
• The ML models were constructed to distinguish
sepsis including a training set (75%) and validation
set(25%).
• XGBoost, or eXtreme Gradient Boosting: is
a popular and powerful machine learning
algorithm where you combine multiple weak
models to create a strong predictive model.
Gradient Boosting:
• Weak models are trained sequentially, and
each new model corrects the errors of the
previous ones.
Fig4. Visual Representation of XGBoost
Fig5. Visual Representation of Gaussian NB

8. Machine Learning
1
Fig7. Representation of the Decision Boundary for Model
Al Arief, et al. (Simplified Representation of the Decision
Boundary for Model, n.d.)
Fig6. Visual Representation of Random
Forest

9. Aim and Objective:
• to screen and identify sepsis-associated metabolites from serum samples of septic patients in
comparison to healthy individuals using the XGBoost machine-learning model
• to highlight the potential diagnostic value of Machine Learning
• provide new insight into enhanced therapy and/or preventative measures against sepsis-
induced intestinal barrier injury using sepsis-related metabolite i.e. Sphinganine
9

10. Materials and methods
10
Human serum samples
Healthy patients (n=13)
Septic patients (n=13)
Metabolomics
analysis of serum
(UPLC-TOFMS)
ML Analysis-
XGBoost Model
6-8 week-old
C57BL/6
mice
Control
n=2
Sepsis
n=20
Sepsis + Sphinganine
n=20
Sphinganine
n=20
Functional Assays
Human colorectal
adenocarcinoma
(Caco-2) cell line
in EMEM
Treated with
lipopolysaccharide
(LPS) (1 mg/mL)
Lactate
dehydrogenase (LDH)
cytotoxicity assay

11. Methods
Clinical sample collection
• Human serum samples were retrieved from healthy individuals (n =13) and septic patients (n = 13) within
12h of admission between the ages of 18 and 80
• All samples underwent a 10 min centrifugation at 1,500 r/min, prior to storage at −80°C
Metabolomics analysis of serum
• Diluted serum samples were inserted into Ultra-Performance Liquid Chromatography-Time of Flight Mass
Spectrometry(UPLC-TOFMS) machine
• Chemical components underwent separation at 35°C via a UPLC C18 column.
11

12. Methods
ML analysis
• The Extreme gradient boosting (XGBoost)was employed using Python 3.7.
• The data set was randomly split into: a training (75%) data set and a validation (25%) data set.
• Five machine learning models were used for model construction: Logistic Regression, XGBoost,
GaussianNB(GNB), Support Vector Machines(SVM), and RandomForest.
• To better understand the decision-making process of machine learning SHAPELY Additive explanations
(SHAP)values were used.
Mouse models
• 6-8 week-old C57BL/6 mice, weighing between 20-23 g
• All animal protocols received ethical approval.
• Mice were randomly divided into 4 groups (20 mice per group):
Control, Sepsis, Sepsis + Sphinganine, Sphinganine
12

13. Methods
Cell culture and treatment
• Human colorectal adenocarcinoma (Caco-2) cells were cultured in Eagle’s Minimum Essential
Medium with 10% fetal bovine serum.
• Caco-2 cells were grown in 6-well plates and were treated with Salmonella enterica
lipopolysaccharide (LPS). The optimal LPS dosage is to be 1 mg/mL for a duration of 48 hours.
H & E Staining
• Intestinal tissues underwent a 24-hour fixation in 10% formalin in PBS, followed by paraffin
embedding, then slicing into 4m thick sections, and staining with hematoxylin and eosin (H&E).
13

14. Methods
Immunohistochemistry
• Intestinal tissues were exposed to primary antibodies overnight at 4°C
• treated with FITC-labeled secondary antibodies for 1 hour
• Following nuclear counterstaining, the slices were treated to mounting media with DAPI
Western blot analysis
• The employed primary antibodies are listed as follows: anti-Occludin antibody; anti-ZO-1 antibody; and anti-
GAPDH antibody.
• The separated proteins were treated with secondary antibodies.
14

15. Methods
Lactate dehydrogenase cytotoxicity assay
• Target cell cytotoxicity was assessed based on the cellular LDH release, using an LDH cytotoxicity
detection kit.
Statistical analysis
• Data was analyzed with the one-way analysis of variance (ANOVA), and inter-group comparisons were
assessed using the t-test.
• Overall performance of each model was assessed via the accuracy, precision, and F1-measure.
• Finally, SHAP analysis was utilized for model explainability.
• Statistical analyses were conducted using SPSS statistical software, R statistical software, and Python
software. All statistical tests had P-values less than 0.05 were considered to be statistically significant.
15

16. Results
16
Figure 1 Analysis of sepsis-related
serum metabolites.
(A) Principal Component
Analysis of serum samples
obtained from septic patients and
healthy individuals.
(B) The metabolite sets
enrichment analysis
(C) Volcano plot for differentially
regulated metabolites between
control and sepsis groups.
(D) Heat map of differentially
regulated metabolites.
Fig 1.

17. Results
17
Fig1. (E) Significant Pearson
Correlation.
Fig 1.

18. Results
18
Figure 2 Machine learning model performance.-
(A) Linear regression analysis.
(B) XGBoost model: feature importance.
(C) Model performance. Receiver-operating characteristic
curves for 5 machine learning models.
Fig 2.

19. Results
19
Figure 2 The XGBoost model achieved a larger
(better) AUROC compared with the other models:
(D) Train ROC curve,
(E) Validation ROC curve.
(F) Forest plot of the AUC Score of the 5 models.
Fig 2.

20. Results
20
Figure 2
(G) Calibration plots of 5 models. The XGBoost
achieved a lower (better) Brier score
(H) Decision curve analysis for machine learning
models.
(I) SHAP analysis was performed on the XGBoost
Fig 2.

21. Results
21
Table 1 Performance metrics for five models in the training
dataset.
Table
1

22. Results
22
Table
2
Table 2 Performance metrics for five models in the validation
dataset.

23. Results
23
Figure 3 The correlation between the
expression of metabolites and the
severity of sepsis.
(A) Relative expression of sphinganine
in the healthy group and sepsis group.
(B-F) Pearson correlation of the
expression of sphinganine and
APACHE-II(R=0.69, P<0.001),
PCT(R=0.81, P<0.001), CRP(R=0.65,
P<0.001), IL-6(R=0.64, P<0.001),
WBC(R=0.73, P<0.001). (G) Relative
expression of Mannose 6-phosphate in
healthy group and sepsis group. (H-
L) Pearson correlation of the
expression of mannose-6-phosphate
and and APACHE-II(R=0.80, P<0.001),
CRP(R=0.63, P<0.001), IL-
6(R=0.93, P<0.001),
PCT(R=0.77, P<0.001),
WBC(R=0.75, P<0.001). P values 0.05
Fig
3

24. Results
24
Figure 4 Sphinganine alleviates
sepsis-induced intestinal injury in
vitro.
(A) Effect of a 24-hour treatment
with mannose-6-phosphate (10μm)
and sphinganine (10μm) on Caco-2
cell cytotoxicity, by the LDH assay.
(B) Chemical structure of
sphinganine.
(C) Effect of 24 h treatments with
varying sphinganine concentrations
on Caco-2 cell cytotoxicity, by the
LDH assay.
(D) ZO-1 and Occludin protein
expressions, assessed by Western
blot analysis.
(E) Statistical plot of gray value of
ZO-1 and Occludin were detected
by Western blot. P values 0.05 (*)
or 0.01 (**) were regarded as
significant.
Fig
4

25. Results
25
Figure 5 Sphinganine alleviates sepsis-
induced intestinal injury in vivo.
(A) Design of animal experiment.
(B) Effect of varying sphinganine
concentrations on the SR of sepsis mice.
(C) Colon length.
Serum levels of (D) D-lactic acid, (E) IL-6,
and (F) IL-1β.
(G) Colon tissues stained with HE and
histopathological scores analysis from
slides.
(H) Immunofluorescent staining of intestinal
Tight Junction proteins, namely, ZO-1 and
Occludin.
(I, J) Intestinal ZO-1 and occludin gene
expression analysis via qPCR. P values
0.05 (*), 0.01 (**), or 0.001 (***) were
regarded as significant.
Fig
5

26. Discussion
26
• The current manual assessment of sepsis can be complicated due to the number of clinical signs measured and may
also lack sufficient sensitivity.
• In contrast, automated decision support systems based on artificial intelligence (AI) and machine learning have
shown a marked improvement in treatment protocols in ICUs.
• Following sepsis, there is a rise in intestinal permeability, which can lead to the translocation of intestinal bacteria
and endotoxins. This process can exacerbate the sepsis and worsen the overall condition of the individual.
• Currently, the treatment of intestinal injury involves several approaches such as anti-infective therapy, immune
regulation, organ support, and protection.
• However, despite these efforts, the effectiveness of the treatment remains limited and the mortality rate remains
high.
• Hence, it is crucial to develop effective measures of sepsis-induced intestinal barrier injury prevention and
treatment.

27. Pros and Cons
27
Pros Cons
Early Detection Data Quality and Bias
Efficiency and Automation Black Box
Data Integration Overfitting and Generalization
Decision Support Ethical Concerns
Predictive Analytics Integration into Clinical Workflow

28. Conclusion
28
• Analysis of serum metabolites revealed that sepsis causes a strong dysregulation in serum
metabolites.
• Based on our ML findings, serum metabolites not only have a good value in sepsis
diagnosis but also possess a protective value against sepsis-induced intestinal barrier
injury.
• The findings highlighted the potential diagnostic value of the ML, and also provided new
insight into enhanced therapy and/or preventative measures against sepsis.
• However, the number of patients in the sample is relatively small, and large sampling sizes
are needed to comprehensively assess the diagnostic value of metabolites for sepsis.

29. • Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., et al., (2018). Clinical applications of machine
learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal, 40(24), 1975–1986.
https://doi.org/10.1093/eurheartj/ehy404
• Wang, Z., Yue, Q., Wang, F., Zhang, B., & Tang, J. (2023). Circulating sepsis-related metabolite sphinganine could
protect against intestinal damage during sepsis. Frontiers in Immunology, 14.
https://doi.org/10.3389/fimmu.2023.1151728
References

30. Acknowledgments
I would like to thank my teachers for giving me the opportunity to present
today. I thank my family and my amazing friends Adline and Roshni for their
support. Thank you all.

31. THANK YOU!