Search

Alzheimer's disease (AD) progresses relentlessly from the preclinical to the dementia stage. The process begins decades before the diagnosis of dementia. Therefore, it is crucial to detect early manifestations to prevent cognitive decline. Recent advances in artificial intelligence help tackle the complex high-dimensional data encountered in clinical decision-making. In total, we recruited 206 subjects, including 69 cognitively unimpaired, 40 subjective cognitive decline (SCD), 34 mild cognitive impairment (MCI), and 63 AD dementia (ADD). We included 3 demographic, 1 clinical, 18 brain-image, and 3 plasma biomarker (Aß 1-42 , Aß 1-40 , and tau protein) features. We employed the linear discriminant analysis method for feature extraction to make data more distinguishable after dimension reduction. The sequential forward selection method was used for feature selection to identify the 12 best features for machine learning classifiers. We used both random forest and support vector machine as classifiers. The area under the receiver operative curve (AUROC) was close to 0.9 between diseased (combining ADD and MCI) and the controls. AUROC was higher than 0.85 between SCD and controls, 0.90 between MCI and SCD, and above 0.85 between ADD and MCI. We can differentiate between adjacent phases of the AD spectrum with blood biomarkers and brain MR images with the help of machine learning algorithms.

In this work, we demonstrated tunable p- and/or n-type doping of chemical vapor deposition-grown graphene with the use of protein bovine serum albumin (BSA) as a dopant. BSA undergoes protonation or deprotonation reaction subject to solution pH, thereby acting as either an electron donor or an electron acceptor on the graphene surface layered with denatured BSA through π-stacking interaction. This direct annealing of graphene with denatured BSA of amphoteric nature rendered facilitated fabrication of a p- and/or n-type graphene transistor by modulating pH-dependent net charges of the single dopant. Following AFM confirmation of the BSA/graphene interface assembly, the carrier transport properties of BSA-doped graphene transistors were assessed by I-V measurement and Raman spectra to show effective charge modulation of the graphene enabled by BSA doping at various pH conditions. The protein-mediated bipolar doping of graphene demonstrated in our work is simple, scalable, and straightforward; the proposed scheme is therefore expected to provide a useful alternative for fabricating graphene transistors of novel properties and promote their implementation in practice.

Graphene-based electrochemical impedance sensors have recently received much attention due to their outstanding sensing capability and economic viability. In this study, we present a novel means of constructing an impedance sensing platform via harnessing intrinsic π-stacking interactions between probe protein molecules and reduced graphene oxide (RGO) substrate, obviating the need for introducing external chemical groups often required for covalent anchoring of the probes. To achieve this goal, protein molecules used as a probe were denatured to render their hydrophobic residues exposed in order to facilitate their direct π-stacking interactions with the surface of RGO nanosheets. The protein molecules in denatured form, which would otherwise have difficulty in undergoing π-stacking interactions with the RGO surface, were found to uniformly cover the RGO nanosheets at high density, conducive to providing a graphene-based impedance sensing platform capable of detecting a probe-specific analyte at high sensitivity. The proof-of-concept performance of thus-constructed RGO-based impedance sensors was demonstrated via selective detection of biological binding events of antigen-antibody reaction at a femtomolar range. Notably, since the π-stacking interaction can occur on the entire RGO surface, it can desirably exclude a backfill process indispensable for the conventional biosensors to suppress background noise signals. Since the procedure of π-stacking mediated direct deposition of on-purpose denatured protein probes onto the RGO surface is facile and straightforward, the proposed strategy is anticipated to extend its applicability for fabrication of high performance graphene-based bio or chemical sensors.