Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning.

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aß) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aß biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aß biomarkers and identify the Aß-related dominant brain regions involved with cognitive impairment. We employed Aß biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aß biomarkers on the test set. To identify Aß-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aß in MCI compared to controls and a stronger correlation between Aß and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aß biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aß biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aß biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Salience network anatomical and molecular markers are linked with cognitive dysfunction in mild cognitive impairment.

BACKGROUND AND PURPOSE: Recent studies indicate disrupted functional mechanisms of salience network (SN) regions-right anterior insula, left anterior insula, and anterior cingulate cortex-in mild cognitive impairment (MCI). However, the underlying anatomical and molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multimodal-anatomical and molecular-markers could predict cognitive impairment better in MCI. METHODS: Herein we quantified anatomical volumetric markers via structural MRI and molecular amyloid markers via PET with Pittsburgh compound B in SN regions of MCI (n = 33) and healthy controls (n = 27). From these markers, we built support vector machine learning models aiming to estimate cognitive dysfunction in MCI. RESULTS: We found that anatomical markers are significantly reduced and molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < .05). These altered markers in MCI patients were associated with their worse cognitive performance (p < .05). Our machine learning-based modeling further suggested that the integration of multimodal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers. CONCLUSIONS: These findings shed light on the underlying anatomical volumetric and molecular amyloid alterations in SN regions and show the significance of multimodal markers integration approach in better predicting cognitive impairment in MCI.

Locally available agroresidues as potential sorbents: modelling, column studies and scale-up.

Sawdust, cotton stalk and groundnut shell were used for removal of methylene blue from aqueous solution using batch sorption. Effect of initial dye concentration, temperature, and particle size of sorbents on methylene blue removal was investigated. Sorption capacity increases with rise in initial dye concentration and temperature. Impact of particle size on sorption of methylene blue was investigated and indicated that removal of dye increases with decrease in particle size of sorbents. Maximum sorption for sawdust, cotton stalks and groundnut shell were 9.22 mg g-1, 8.37 mg g-1 and 8.20 mg g-1 respectively; at 60 °C and 100 ppm initial dye concentration. Sorption isotherms were analyzed using fundamental Freundlich isotherm. Subsequently, sips isotherm model was employed for better fitting. Kinetic study shows that, biosorption process is pseudo-second-order in nature. During the course of this study, adsorption dynamics revealed that film diffusion was key step for biosorption. In addition, thermodynamics of sorption was studied; and it was found that Gibbs free energy (∆G°) decreases with increase in temperature. Sawdust was found to be best among all the sorbents. Therefore, column studies and breakthrough curve modelling were performed using sawdust. Furthermore, it was estimated that a scaled-up column using sawdust can treat 6672 L of wastewater in 24 h with 80% efficiency.

Pilot scale oxidative fast pyrolysis of sawdust in a fluidized bed reactor: A biorefinery approach.

Oxidative fast pyrolysis of sawdust was performed in a pilot scale fluidized bed system (3 kg/h) under stabilized experimental conditions (0.20 equivalence ratio and 550 °C). Experiments were performed in non-catalytic and catalytic (ZSM-5) mode. During non-catalytic fast pyrolysis, bio-oil (∼38 wt%), bio-char (∼12 wt%) and pyro-gas (∼50 wt%) were obtained; in contrast, for catalytic fast pyrolysis bio-oil, bio-char and pyro-gas yields were ∼44 wt%, ∼4 wt% and ∼52 wt% respectively. The obtained bio-oil was characterized through CHNSO, NMR (1H and 13C NMR), FT-IR and GC-MS techniques. GC-MS analysis of the bio-oil shows it is a mixture of ∼21 chemical compounds. Furthermore, NMR (1H and 13C NMR) and FT-IR results indicates presence of hydrocarbon, alcohol, phenol and aldehyde in the bio-oil. The TGA of bio-char shows that it is stable up to 950 °C. The activation energies (Ea) of sawdust and bio-char are found to be 112.3 kJ/mol and 46.92 kJ/mol respectively. FT-IR analysis of bio-char clearly revealed removal of functionalized organic compounds during devolatilization of sawdust. In addition, GC analysis of pyro-gas suggests that it is a mixture of N2 (35.55 vol%), CO (34.49 vol%), CO2 (16.80 vol%), H2 (4.54 vol%), O2 (4.25 vol%), and CH4 (4.41 vol%).