Blood speaks: Personalised medicine profiling for heart failure patients

Firouzi, Fareheh; Sussman, Mark A.

doi:10.1016/j.ebiom.2020.102900

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The stratification accuracy and sensitivity for a larger cohort increased by more than 10% [ 10 ]. Firouzi and Sussman suggested that this novel strategic approach of combining transcriptome profiling with precise patient phenotyping and AI-guided feature selection is a potentially valuable tool for advancing personalized medicine and cell-based therapies [ 110 ].…”

Section: Ai-based R and Nr Stratification Of Patients With Pad Undergoing Stem Cell Transplantationmentioning

confidence: 99%

Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction

Salybekov

Wolfien

Kobayashi

et al. 2021

Cells

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Stem/progenitor cell transplantation is a potential novel therapeutic strategy to induce angiogenesis in ischemic tissue, which can prevent major amputation in patients with advanced peripheral artery disease (PAD). Thus, clinicians can use cell therapies worldwide to treat PAD. However, some cell therapy studies did not report beneficial outcomes. Clinical researchers have suggested that classical risk factors and comorbidities may adversely affect the efficacy of cell therapy. Some studies have indicated that the response to stem cell therapy varies among patients, even in those harboring limited risk factors. This suggests the role of undetermined risk factors, including genetic alterations, somatic mutations, and clonal hematopoiesis. Personalized stem cell-based therapy can be developed by analyzing individual risk factors. These approaches must consider several clinical biomarkers and perform studies (such as genome-wide association studies (GWAS)) on disease-related genetic traits and integrate the findings with those of transcriptome-wide association studies (TWAS) and whole-genome sequencing in PAD. Additional unbiased analyses with state-of-the-art computational methods, such as machine learning-based patient stratification, are suited for predictions in clinical investigations. The integration of these complex approaches into a unified analysis procedure for the identification of responders and non-responders before stem cell therapy, which can decrease treatment expenditure, is a major challenge for increasing the efficacy of therapies.

show abstract

Section: Ai-based R and Nr Stratification Of Patients With Pad Undergoing Stem Cell Transplantationmentioning

confidence: 99%

Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction

Salybekov

Wolfien

Kobayashi

et al. 2021

Cells

View full text Add to dashboard Cite

show abstract

“…The stratification accuracy and sensitivity for a larger cohort increased by more than 10% [13]. Firouzi and Sussman suggested that this novel strategic approach of combining transcriptome profiling with precise patient phenotyping and AI-guided feature selection is a potentially valuable tool for advancing personalized medicine and cell-based therapies [111].…”

Section: Identification Of Responsive Patients For Cell Therapymentioning

confidence: 99%

Personalized Cell Therapy for Patients With Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-based Responder and Non-responder Prediction

Salybekov¹,

Wolfien²,

Kobayashi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Stem/progenitor cell transplantation is a potential novel therapeutic strategy to induce angiogenesis in ischemic tissue, which can prevent major amputation in patients with advanced peripheral artery disease (PAD). Thus, clinicians can use cell therapies worldwide to treat PAD. However, some cell therapy studies did not report beneficial outcomes. Clinical researchers suggested that classical risk factors and comorbidities may adversely affect the efficacy of cell therapy. Some studies have indicated that the response to stem cell therapy varies among patients even in those harboring limited risk factors. This suggested the role of undetermined risk factors, including genetic alterations, somatic mutations, and clonal hematopoiesis. Personalized stem cell-based therapy can be developed by analyzing individual risk factors. These approaches must consider several clinical biomarkers and perform studies (such as genome-wide association studies (GWAS)) on disease-related genetic traits and integrate the findings with those of transcriptome-wide association studies (TWAS) and whole-genome sequencing in PAD. Additional unbiased analyses with state-of-the-art computational methods, such as machine learning-based patient stratification, are suited for predictions in clinical investigations. The integration of these complex approaches into a unified analysis procedure for the identification of responders and non-responders before stem cell therapy, which can decrease treatment expenditure, is a major challenge to increase the efficacy of therapies.

show abstract

Blood speaks: Personalised medicine profiling for heart failure patients

Cited by 2 publications

References 10 publications

Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction

Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction

Personalized Cell Therapy for Patients With Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-based Responder and Non-responder Prediction

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