Altered neuroepithelial morphogenesis and migration defects in iPSC-derived cerebral organoids and 2D neural stem cells in familial bipolar disorder.

Bipolar disorder (BD) is a severe mental illness that can result from neurodevelopmental aberrations, particularly in familial BD, which may include causative genetic variants. In the present study, we derived cortical organoids from BD patients and healthy (control) individuals from a clinically dense family in the Indian population. Our data reveal that the patient organoids show neurodevelopmental anomalies, including organisational, proliferation and migration defects. The BD organoids show a reduction in both the number of neuroepithelial buds/cortical rosettes and the ventricular zone size. Additionally, patient organoids show a lower number of SOX2-positive and EdU-positive cycling progenitors, suggesting a progenitor proliferation defect. Further, the patient neurons show abnormal positioning in the ventricular/intermediate zone of the neuroepithelial bud. Transcriptomic analysis of control and patient organoids supports our cellular topology data and reveals dysregulation of genes crucial for progenitor proliferation and neuronal migration. Lastly, time-lapse imaging of neural stem cells in 2D in vitro cultures reveals abnormal cellular migration in BD samples. Overall, our study pinpoints a cellular and molecular deficit in BD patient-derived organoids and neural stem cell cultures.

Behavioral Modeling for Mental Health using Machine Learning Algorithms.

Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

Expression pattern and prognostic significance of IGFBP isoforms in anaplastic astrocytoma.

The role of insulin-like growth factors and their regulatory proteins (IGFBP isoforms) in gliomas, particularly glioblastoma, has been a subject of active research in recent years. There is paucity of literature on their expression and impact on clinical outcome in anaplastic astrocytomas. To evaluate the expression patterns of IGFBP isoforms in anaplastic astrocytoma and correlate with clinical outcome, a retrospective study of 53 adult patients operated for supratentorial lobar anaplastic astrocytoma was performed. The protein expression of IGFBP isoforms (IGFBP-2, -3, -5 and -7), was studied by immunohistochemistry on all samples. The patients were followed up and outcome was documented. The median age at presentation in the present study was 35 years. The pattern of staining was intra cytoplasmic, homogenous and diffuse for IGFBP-2, -3 and -5 and granular for IGFBP-7. IGFBP-2 expression was significantly low in anaplastic astrocytoma as compared to other isoforms (P < 0.001). IGFBP-3 expression was higher than the other isoforms. However, its' expression correlated with favorable overall survival and demonstrated a trend towards significance on univariate analysis. The present study is the first of its kind to describe comprehensively the pattern of expression of IGFBP isoforms (IGFBP-2, -3, -5 and -7) in anaplastic astrocytomas. IGFBP-2 and IGFBP-3 expression patterns and correlation to prognosis were distinct in anaplastic astrocytoma patients, contradictory to what has been reported in glioblastoma, thus giving further evidence that anaplastic astrocytomas are molecularly distinct from glioblastoma.

Age-dependent prognostic effects of EGFR/p53 alterations in glioblastoma: study on a prospective cohort of 140 uniformly treated adult patients.

AIMS: To assess the prognostic influence of EGFR amplification/overexpression, p53 immunoreactivity and their age-dependent prognostic effects in a large prospective cohort of uniformly treated adult patients with newly diagnosed glioblastoma. METHODS: Tumours from a uniformly treated prospective cohort of adult patients with newly diagnosed glioblastoma (n=140) were examined for EGFR amplification by fluorescence in situ hybridisation and EGFR/p53 expression by immunohistochemistry. Statistical methods were employed to assess the degree of association between EGFR amplification/overexpression and p53 immunopositivity. Survival analyses were performed by employing Cox proportional hazard models to assess the independent prognostic value of EGFR/p53 alterations and test the propensity for risk with age by assessing their interaction with patient age. RESULTS: A strong positive correlation between EGFR amplification and EGFR overexpression (rho=0.5157; p<0.0001; CI 0.3783 to 0.6309) and a negative association of EGFR amplification (rho=-0.3417; p<0.0001; CI -0.4842 to -0.1816) and EGFR overexpression (rho=-0.3095; p<0.001; CI -0.4561 to -0.1465) with p53 immunopositivity was observed. Only patient age (HR: 1.029; p=0.004; CI 1.009 to 1.049) was associated with shorter survival by univariate Cox regression analysis. Multivariable Cox proportional hazards models revealed a statistically significant interaction between EGFR overexpression and age to be associated with shorter survival (HR: 1.001; p<0.0001; CI 1.000 to 1.002), thus predicting a higher hazard with increasing age. No age interaction of EGFR amplification status (HR: 1.001; p=0.642; CI 0.995 to 1.008) and p53 immunopositivity (HR: 1.000; p=0.841; CI 0.999 to 1.001) was noted in this cohort. CONCLUSIONS: The prognostic value of EGFR overexpression is age-dependent, and there is a propensity for a higher hazard with increasing patient age. Identifying such groups of patients with more aggressive disease becomes mandatory, since they would benefit from intense therapeutic protocols targeting EGFR.