Metabolomics analysis of amino acid and fatty acids in colorectal cancer patients based on tandem mass spectrometry.

Metabolic differences between colorectal cancer (CRC) and NI (NI) play an important role in early diagnoses and in-time treatments. We investigated the metabolic alterations between CRC patients and NI, and identified some potential biomarkers, and these biomarkers might be used as indicators for diagnosis of CRC. In this study, there were 79 NI, 50 CRC I patients, 52 CRC II patients, 56 CRC III patients, and 52 CRC IV patients. MS-MS was used to measure the metabolic alterations. Univariate and multivariate data analysis and metabolic pathway analysis were applied to analyze metabolic data and determine differential metabolites. These indicators revealed that amino acid and fatty acids could separate these groups. Several metabolites indicated an excellent variables capability in the separation of CRC patients and NI. Ornithine, arginine, octadecanoyl carnitine, palmitoyl carnitine, adipoyl carnitine, and butyryl carnitine/propanoyl carnitine were selected to distinguish the CRC patients and NI. And methionine and propanoyl carnitine, were directly linked to different stages of CRC. Receiver operating characteristics curves and variables importance in projection both represented an excellent performance of these metabolites. In conclusion, we assessed the difference between CRC patients and NI, which supports guidelines for an early diagnosis and effective treatment.

A random forest model based classification scheme for neonatal amplitude-integrated EEG.

BACKGROUND: Modern medical advances have greatly increased the survival rate of infants, while they remain in the higher risk group for neurological problems later in life. For the infants with encephalopathy or seizures, identification of the extent of brain injury is clinically challenging. Continuous amplitude-integrated electroencephalography (aEEG) monitoring offers a possibility to directly monitor the brain functional state of the newborns over hours, and has seen an increasing application in neonatal intensive care units (NICUs). METHODS: This paper presents a novel combined feature set of aEEG and applies random forest (RF) method to classify aEEG tracings. To that end, a series of experiments were conducted on 282 aEEG tracing cases (209 normal and 73 abnormal ones). Basic features, statistic features and segmentation features were extracted from both the tracing as a whole and the segmented recordings, and then form a combined feature set. All the features were sent to a classifier afterwards. The significance of feature, the data segmentation, the optimization of RF parameters, and the problem of imbalanced datasets were examined through experiments. Experiments were also done to evaluate the performance of RF on aEEG signal classifying, compared with several other widely used classifiers including SVM-Linear, SVM-RBF, ANN, Decision Tree (DT), Logistic Regression(LR), ML, and LDA. RESULTS: The combined feature set can better characterize aEEG signals, compared with basic features, statistic features and segmentation features respectively. With the combined feature set, the proposed RF-based aEEG classification system achieved a correct rate of 92.52% and a high F1-score of 95.26%. Among all of the seven classifiers examined in our work, the RF method got the highest correct rate, sensitivity, specificity, and F1-score, which means that RF outperforms all of the other classifiers considered here. The results show that the proposed RF-based aEEG classification system with the combined feature set is efficient and helpful to better detect the brain disorders in newborns.

Flash visual evoked potentials are not specific enough to identify parieto-occipital lobe involvement in term neonates after significant hypoglycaemia.

AIM: Hypoglycaemia is a significant problem in high-risk neonates and predominant parieto-occipital lobe involvement has been observed after severe hypoglycaemic insult. We explored the use of flash visual evoked potentials (FVEP) in detecting parieto-occipital lobe involvement after significant hypoglycaemia. METHODS: Full-term neonates (n = 15) who underwent FVEP from January 2008 to May 2013 were compared with infants (n = 11) without hypoglycaemia or parietal-occipital lobe injury. Significant hypoglycaemia was defined as being symptomatic or needing steroids, glucagon or a glucose infusion rate of ≥12 mg/kg/min. RESULTS: The hypoglycaemia group exhibited delayed latency of the first positive waveform on FVEP. The initial detected time for hypoglycaemia was later in the eight subjects with seizures (median 51-h-old) than those without (median 22-h-old) (P = 0.003). Magnetic resonance imaging showed that 80% of the hypoglycaemia group exhibited occipital-lobe injuries, and they were more likely to exhibit abnormal FVEP morphology (P = 0.007) than the controls. FVEP exhibited 100% sensitivity, but only 25% specificity, for detecting injuries to the parieto-occipital lobes. CONCLUSION: Flash visual evoked potential (FVEP) was sensitive, but not sufficiently specific, in identifying parieto-occipital lobe injuries among term neonates exposed to significant hypoglycaemia. Larger studies exploring the potential role of FVEP in neonatal hypoglycaemia are required.