Researchers at Nottingham Trent University have developed a diagnostic model for predicting the outcome of liver cancer interstitial pleural stem cell (LPSC) an often-terminal side effect of fasting-induced hypoglycemia in patients.
The team led by Professor Mark Tiller examined data from a national population-based cohort and developed an algorithm that leveraged the performance of liver-function tests and biomarkers to examine liver cancers in the cohort. This approach complemented the use of Count-Eng and GrEEG for predicting liver cancers incidence incidences and survival rates.
The feasibility and speed of predicting liver cancer death and survival rates has been demonstrated by this technology.
This study presents a novel diagnostic tool that can reliably be used on blood serum from patients within a general population to accurately predict patient outcomes.
Susceptible or resistant?
The team of researchers behind this research project who aim to act as a reference for future developments in liver cancer were assessed for their use in a clinical setting before and after fasting compared to a continuous glucose environment (control condition) respectively. By measuring the levels of glucose in a measured serum before fasting they were able to demonstrate their predictive potential.
To facilitate future work in this area the team has dropped the need for the outdated EVG fluid analysis which is and has remained the gold standard for biomarker evaluation. To date no clinical validation studies have been carried out for the use of the EVG on serum samples from under patients following fasting. The validation studies which were conducted on patient samples served as the seed for this study.
I am very excited to find the system our team developed for integrating liver functions with fasting metabolism. This technology would complement the existing award-winning Count-Eng technology which has helped identify liver cancer added Professor Tiller.
These research findings demonstrate the applicability of this technology which was founded by Professor Tiller mainly as a means of predicting the outcome of liver cancer patients and could play an important role for informing surgical decisions.
Clinicians can now confidently predict patient outcomes across age groups and at a significantly larger life stage than is previously the case added Professor Tiller. Analysing biomarkers at a significant clinical scale however does require a new complex technique and an unprecedented understanding of the complex dynamic liver biochemistry. This technology holds great promise as an agile means of assessing biomarkers to potentially integrate in a single multi-centre cohort where the margin of error is currently too wide to allow inclusion.
