MATERIALS AND METHODS: Male and female Sprague-Dawley rats received three doses of mitragynine (1, 10, 100mg/kg, p.o) for 28 days respectively. Food intake and relative body weight were measured during the experiment. After completion of drug treatment biochemical, hematological, and histological analyses were performed.
RESULTS: No mortality was observed in any of the treatment groups. The groups of rats treated with the lower and intermediate doses showed no toxic effects during the study. However, the relative body weight of the group of female rats treated with the 100mg/kg dose was decreased significantly. Food intake also tended to decrease in the same group. Only relative liver weight increased after treatment with the high dose of mitragynine (100mg/ kg) in both the male and female treatment groups of rats. Biochemical and hematological parameters were also altered especially in high dose treatment group which corresponds to the histopathological changes.
CONCLUSIONS: The study demonstrated that mitragynine is relatively safe at lower sub-chronic doses (1-10mg/kg) but exhibited toxicity at a highest dose (sub-chronic 28 days: 100mg/kg). This was confirmed by liver, kidney, and brain histopathological changes, as well as hematological and biochemical changes.
MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
MATERIALS AND METHODS: The case records of 125 patients with NSCLC and brain metastases consecutively treated with radiotherapy at two tertiary centres from January 2006 to June 2012 were analysed for patient, tumour and treatment-related prognostic factors. Patients receiving SRS/SRT were treated using Cyberknife. Variables were examined in univariate and multivariate testing.
RESULTS: Overall median survival was 3.4 months (95%CI: 1.7-5.1). Median survival for patients with multiple metastases receiving WBRT was 1.5 months, 1-3 metastases receiving WBRT was 3.6 months and 1-3 metastases receiving surgery or SRS/SRT was 8.9 months. ECOG score (≤2 vs >2, p=0.001), presence of seizure (yes versus no, p=0.031), treatment modality according to number of brain metastases (1-3 metastases+surgery or SRS/SRT±WBRT vs 1-3 metastases+WBRT only vs multiple metastases+WBRT only, p=0.007) and the use of post-therapy systemic treatment (yes versus no, p=0.001) emerged as significant on univariate analysis. All four factors remained statistically significant on multivariate analysis.
CONCLUSIONS: ECOG ≤2, presence of seizures, oligometastatic disease treated with aggressive local therapy (surgery or SRS/SRT) and the use of post-therapy systemic treatment are favourable prognostic factors in NSCLC patients with brain metastases.