Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course - protocol for systematic review and meta-analysis.

BACKGROUND: The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. METHODS AND ANALYSIS: We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. ETHICS AND DISSEMINATION: The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42022354179.

Real-time COVID-19 detection via graphite oxide-based field-effect transistor biosensors decorated with Pt/Pd nanoparticles.

Coronavirus 2019 (COVID-19) spreads an extremely infectious disease where there is no specific treatment. COVID-19 virus had a rapid and unexpected spread rate which resulted in critical difficulties for public health and unprecedented daily life disruption. Thus, accurate, rapid, and early diagnosis of COVID-19 virus is critical to maintain public health safety. A graphite oxide-based field-effect transistor (GO-FET) was fabricated and functionalized with COVID-19 antibody for the purpose of real-time detection of COVID-19 spike protein antigen. Thermal evaporation process was used to deposit the gold electrodes on the surface of the sensor substrate. Graphite oxide channel was placed between the gold electrodes. Bimetallic nanoparticles of platinum and palladium were generated via an ultra-high vacuum (UHV) compatible system by sputtering and inert-gas condensation technique. The biosensor graphite oxide channel was immobilized with specific antibodies against the COVID-19 spike protein to achieve selectivity and specificity. This technique uses the attractive semiconductor characteristics of the graphite oxide-based materials resulting in highly specific and sensitive detection of COVID-19 spike protein. The GO-FET biosensor was decorated with bimetallic nanoparticles of platinum and palladium to investigate the improvement in the sensor sensitivity. The in-house developed biosensor limit of detection (LOD) is 1 fg/mL of COVID-19 spike antigen in phosphate-buffered saline (PBS). Moreover, magnetic labelled SARS-CoV-2 spike antibody were studied to investigate any enhancement in the sensor performance. The results indicate the successful fabrication of a promising field effect transistor biosensor for COVID-19 diagnosis.

COVID-19 Detection via Silicon Nanowire Field-Effect Transistor: Setup and Modeling of Its Function.

Biomolecular detection methods have evolved from simple chemical processes to laboratory sensors capable of acquiring accurate measurements of various biological components. Recently, silicon nanowire field-effect transistors (SiNW-FETs) have been drawing enormous interest due to their potential in the biomolecular sensing field. SiNW-FETs exhibit capabilities such as providing real-time, label-free, highly selective, and sensitive detection. It is highly critical to diagnose infectious diseases accurately to reduce the illness and death spread rate. In this work, a novel SiNW-FET sensor is designed using a semiempirical approach, and the electronic transport properties are studied to detect the COVID-19 spike protein. Various electronic transport properties such as transmission spectrum, conductance, and electronic current are investigated by a semiempirical modeling that is combined with a nonequilibrium Green's function. Moreover, the developed sensor selectivity is tested by studying the electronic transport properties for other viruses including influenza, rotavirus, and HIV. The results indicate that SiNW-FET can be utilized for accurate COVID-19 identification with high sensitivity and selectivity.