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In this study, we aimed to investigate the effects of agmatine, nitric oxide (NO), arginine, and glutamate, which are the metabolites in the polyamine pathway, on the performance of executive functions (EF) in attention deficit hyperactivity disorder (ADHD). The ADHD group included 35 treatment-naive children (6-14 years old) who were ewly diagnosed with ADHD. The control group consisted of 35 healthy children with the same age and sex, having no previous psychiatric disorders. In the study groups, Stroop test (ST) and trail making test (TMT) were used to monitor EF, and blood samples were collected to measure agmatine with ultra-high-performance liquid chromatography and NO, glutamate, and arginine with enzyme-linked immunosorbent assay (ELISA). The EFs were significantly impaired in the ADHD group. The agmatine and arginine levels of the ADHD group were significantly higher than their peers. The NO and glutamate levels were also higher in the ADHD group compared to the control group, but these differences did not reach statistical significance. Children with ADHD had more difficulties during EF tasks compared to healthy children. The elevated NO and glutamate levels may be related with the impairment during EF tasks. Therefore, agmatine and arginine may increase to improve EF tasks through its inhibitory effect on the synthesis of NO and glutamate. Further studies are needed about polyamine pathway molecules to shed light on the pathophysiology of ADHD.

The key aspect in cancer treatment is to predict the subclass of the cancer accurately. To this aim, making use of the microarray gen data collected from cancerous tissues has been increasingly popular recently. In the present study, to predict the subclass of various cancer types, we employ the Conformal Prediction method, which outputs a set of classes guaranteeing to some extent that the true subclass is in the set of the predicted classes, different from the conventional machine learning classification algorithms. We tested the performance of the conformal prediction on five different microarray cancer data, ranging from leukemia to prostate cancer. The results showed that the true subclass of the cancer is in the predicted set in almost all settings; and the predicted sets includes as less as possible classes, hence are compacts.

Current demographic trends suggest that there will be increasing numbers of older people in the future. Relatively little information is available regarding factors which influence mortality in the acutely unwell oldest old. This study uses the CART technique on data relating to the oldest old, to identify potential predictors of inpatient mortality in patients over 90 years old admitted acutely to the hospital due to various medical emergencies in two UK centers. The sample included 393 patients aged 90 years and older, with 67.5% females and 32.5% males and a mean age of 91.1 years. We aimed to generate hypotheses in order to identify potential acute illness prognostic indicators of inpatient mortality in this age group. The factors identified in this analysis which were associated with inpatient mortality in this patient population were raised serum urea concentration (13.95 mmol/L), low oxygen saturation levels (94%), hyponatremia (128 mmol/L), and raised white cell count (17 × 10(9)/L). The predictability of using these cut off points in inpatient as well as early in-hospital death should be validated in future studies.

Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.