Bowel Preparation for Colonoscopy Changes Serum Composition as Detected by Thermal Liquid Biopsy and Fluorescence Spectroscopy

Hermoso-Durán, Sonia; Domper-Arnal, María José; Roncales, Pilar; Vega, Sonia; Sánchez-Gracia, Oscar; Ojeda, J. L.; Lanas, Ángel; Velázquez‐Campoy, Adrián; Abián, Olga

doi:10.3390/cancers15071952

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Administration of biosensor. Standard protocols such as special liquid diets and administration of a laxative prior to ingestion of the biosensor prepare the subject's body for clean sampling and measurements (Hermoso-Durán et al, 2023). 4.…”

Section: Proposed Clinical Implementationmentioning

confidence: 99%

The development of an ingestible biosensor for the characterization of gut metabolites related to major depressive disorder: hypothesis and theory

Densil,

George,

Mahdi

et al. 2023

Front. Syst. Biol.

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The diagnostic process for psychiatric conditions is guided by the Diagnostic and Statistical Manual of Mental Disorders (DSM) in North America. Revisions of the DSM over the years have led to lowered diagnostic thresholds across the board, incurring increased rates of both misdiagnosis and over-diagnosis. Coupled with stigma, this ambiguity and lack of consistency exacerbates the challenges that clinicians and scientists face in the clinical assessment and research of mood disorders such as Major Depressive Disorder (MDD). While current efforts to characterize MDD have largely focused on qualitative approaches, the broad variations in physiological traits, such as those found in the gut, suggest the immense potential of using biomarkers to provide a quantitative and objective assessment. Here, we propose the development of a probiotic Escherichia coli (E. coli) multi-input ingestible biosensor for the characterization of key gut metabolites implicated in MDD. DNA writing with CRISPR based editors allows for the molecular recording of signals while riboflavin detection acts as a means to establish temporal and spatial specificity for the large intestine. We test the feasibility of this approach through kinetic modeling of the system which demonstrates targeted sensing and robust recording of metabolites within the large intestine in a time- and dose- dependent manner. Additionally, a post-hoc normalization model successfully controlled for confounding factors such as individual variation in riboflavin concentrations, producing a linear relationship between actual and predicted metabolite concentrations. We also highlight indole, butyrate, tetrahydrofolate, hydrogen peroxide, and tetrathionate as key gut metabolites that have the potential to direct our proposed biosensor specifically for MDD. Ultimately, our proposed biosensor has the potential to allow for a greater understanding of disease pathophysiology, assessment, and treatment response for many mood disorders.

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Section: Proposed Clinical Implementationmentioning

confidence: 99%

The development of an ingestible biosensor for the characterization of gut metabolites related to major depressive disorder: hypothesis and theory

Densil,

George,

Mahdi

et al. 2023

Front. Syst. Biol.

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Development of a Machine‐Learning Model for Diagnosis of Pancreatic Cancer from Serum Samples Analyzed by Thermal Liquid Biopsy

Hermoso‐Durán,

Fraunhoffer,

Millastre‐Bocos

et al. 2024

Advanced Intelligent Systems

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Pancreatic ductal adenocarcinoma (PDAC) poses a considerable diagnostic and therapeutic challenge due to the lack of specific biomarkers and late diagnosis. Early detection is crucial for improving prognosis, but current techniques are insufficient. An innovative approach based on differential scanning calorimetry (DSC) of blood serum samples, thermal liquid biopsy (TLB), combined with machine‐learning (ML) analysis, may offer a more efficient method for diagnosing PDAC. Serum samples from a cohort of 212 PDAC patients and 184 healthy controls are studied. DSC thermograms are analyzed using ML models. The generated models are built applying algorithms based on penalized regression, resampling, categorization, cross validation, and variable selection. The ML‐based model demonstrates outstanding ability to discriminate between PDAC patients and control subjects, with a sensitivity of 90% and an area under the ROC receiver operating characteristic curve of 0.83 in the training and test groups. Application of the model to an independent validation cohort of 113 PDAC patients confirms its robustness and utility as a diagnosis tool. The application of ML to serum TLB data emerges as a promising methodology for early diagnosis, representing a significant advance for detecting and managing PDAC, envisaging a minimally invasive and more efficient methodology for identifying biomarkers.

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Bowel Preparation for Colonoscopy Changes Serum Composition as Detected by Thermal Liquid Biopsy and Fluorescence Spectroscopy

Cited by 2 publications

References 32 publications

The development of an ingestible biosensor for the characterization of gut metabolites related to major depressive disorder: hypothesis and theory

The development of an ingestible biosensor for the characterization of gut metabolites related to major depressive disorder: hypothesis and theory

Development of a Machine‐Learning Model for Diagnosis of Pancreatic Cancer from Serum Samples Analyzed by Thermal Liquid Biopsy

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