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The transmitted data is converted to a feature value dimensional MFCC for stable transfer and for quality maintenance. This enables high-quality information communication from small quantities of data.


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All other company names and product names mentioned in this document are trademarks or registered trademarks of their respective companies. There are generally three different stages in the development of new biomarkers: the discovery phase i. Multiple reaction monitoring MRM , also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation [ 77 , 78 ].

Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity [ 77 , 78 ]. MRM can enhance the lower detection limit for peptides due to its ability to rapidly and continuously monitor exclusively for the specific ions of interest. MRM analysis combine with stable isotope also offers multiplexing capability and increases the reliability of quantification [ 77 , 78 ]. As AD is a multifactorial disease, a panel of proteins is more suitable as biomarker for AD.

Thus, MRM is a valuable tool to verify biomarker candidates for AD and possible future practical applications. PRM is related to the SRM approach but has the advantage of acquiring full fragment spectra instead of a choice of preselected fragments; interfering signals are avoided, whereas quantitation and high sensitivity are conserved [ 64 ]. Among these, IP is a common method. Interestingly, the levels of both SNAP and SYT1 are reduced in cortical areas in the AD brain [ 90 ], thus suggesting that a set of synaptic proteins covering different components of the synaptic unit may be valuable tools in clinical studies on the relevance of synaptic dysfunction and degeneration in AD pathogenesis.

This may also be used in the clinical evaluation of patients. Metabolomics is the newest omics platform that offers great potential for the diagnosis and prognosis of neurodegenerative diseases. This reflects alterations in genetics, transcription, and protein profiles and influences from the environment. Mass spectrometry MS and nuclear magnetic resonance NMR spectroscopy are two analytical platforms regularly used for detection.

NMR is a particularly powerful tool for metabolite structural test. An MS-based approach is a sensitive one to identify and quantify in complex biological systems [ 65 ]. Metabolomics encompasses several techniques including untargeted metabolomics, targeted metabolomics, lipidomics, and fluxomics [ 92 , 93 , 94 ]. Untargeted metabolomics measures hundreds of metabolites in order to identify metabolic signatures related to a particular disease state or phenotype. This approach provides relative changes in metabolites and is useful for discovery projects where affected metabolic pathways are unknown.

Targeted metabolomics provides quantitative measurements of a defined set of metabolites in a pathway of interest e. Lipidomics estimates changes in lipid profiles and requires specialized protocols for the detection and analysis of water-insoluble metabolites. Metabolomics analysis conducted with biological samples of patients with MCI and AD identified metabolic changes associated with preclinical and clinical AD, such as plasma, CSF, and saliva Table 2 [ 95 , 96 , 97 , 98 , 99 , , , , , ]. These findings suggest that metabolomics-based biomarkers could be used to improve disease diagnosis, which will allow target pathways altered earlier in AD.

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Fransquet and Ryan comprehensively reviewed the methods and findings from 26 studies comparing the measurement of miRNA in blood between AD cases and controls [ ]. Of individually measured miRNAs, 23 that were differentially expressed miRNAs were found to be significant in two or more studies. Interestingly, miR has been found to be associated with the dysregulation of proteins involved in aspects of AD pathology, as well as being consistently downregulated in AD brains [ ]. Thus, the differentially expressed miRNAs and the corresponding targets will be potential biomarkers and provide evidence for new strategies for design of drugs for AD treatment.

Exosomes contain proteins, messenger RNAs mRNAs , and microRNAs miRNAs that reflect their cellular origin, and they play a prominent role in cellular signaling, expulsion of toxic proteins, and transfer of cellular pathogens to other cells.

Evaluating the impact of prediction models: lessons learned, challenges, and recommendations

CNS-derived exosomes NEDs are present in biological fluids blood, CSF, and urine and circulate in the interstitial space, both in the brain and in the periphery [ ]. It may serve as markers of underlying CNS changes that occur in advance of changes in circulating proteins. Importantly, CNS-derived exosomes have unique surface markers that reflect their origin. By using the corresponding antibodies, targeted examinations of neuron-, astrocyte-, or endothelial cells can be performed Table 3 [ , , , , ]. Several proteins in neural-derived plasma exosomes have been identified to associate with preclinical AD [ ], and cargo proteins of plasma astrocyte-derived exosomes in AD have also been detected [ ].

Specific profiles of exosomal miRNAs from human biological fluids, such as plasma and CSF, have prompted the potential application of miRNAs as diagnostic biomarkers Table 3 [ , , , ]. These findings further support the search of exosome-based biomarkers for AD and other neurodegenerative diseases. AD is the most common type of dementia and is becoming a major challenge for global health and social care. However, CSF biomarker and brain imaging are not used as screening tools. Research efforts have focused on the development and validation of non-invasive blood-based biomarkers.

Recent advances in technical developments of novel ultrasensitive immunoassay, mass spectrometry methods, metabolomics, and exosomes show promise for blood biomarkers with potential applications as screening tools for AD Figure 1. These opened a window for the study of AD biomarkers. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.

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We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Advance in diagnostic criteria 2. Table 1. Biomarkers of AD 4. After repeated exposure to the predictions in a variety of index group patients, physicians may become better at estimating the probability in subsequent similar patients, even when these patients are part of the control group [ 8 , 9 ].

This likely dilutes the effectiveness and thus impact of the model use [ 47 ]. The effects of a learning curve may be minimized, though not completely prevented, by randomization at a cluster level, e. Because healthcare providers very often work in teams, contamination is much more likely to occur when healthcare providers are randomized than when, e. In a cluster-randomized study, physicians of the intervention group may still experience a learning curve, but this does not necessarily lead to a dilution of the contrast between the two groups, but rather in a change in improved effectiveness over time.

A drawback of randomization at the cluster level is that one often requires a larger sample size. More efficient alternatives are non-randomized before-after studies or interrupted time-series studies, which compare a period without the model to a period with the model, as in our example [ 8 , 10 ].

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Similarly, practices where a model is being used may be compared to practices where it is not being used parallel groups design. The challenge in such designs is to adjust for baseline differences between the two groups [ 8 , 9 ]. Also, one may first study how a prediction model use changes treatment decisions as compared to a control group, following a cross-sectional design—even in a randomized fashion. If the decision making is not changed in the index group compared to the control group, it seems less intuitive to start a longitudinal impact study on patient outcomes [ 8 , 9 ].

Although all these alternatives are more prone to bias, a negative result—i. In a cluster-randomized trial design, it can also be difficult to achieve balance between the intervention and control group. In our example, 79 physicians were randomized. As there was a large variation in the number and type of surgical patients each physician treated during the study, seemingly small baseline imbalances at the physician level caused substantial imbalances at the patient level.

Each cluster can then also serve as its own control, enhancing the balance between study groups.

Would we be able to redo our cluster-randomized trial, we might consider doing pre-trial observations of the potential users and their decision making behavior [ 36 , 53 , 54 ]. In our example, physicians of the care-as-usual group also provided probability-dependent PONV prophylaxis to their patients without explicitly using a prediction model Fig. Although this may simply represent the clinical expertise of the physicians, once the study is completed, one cannot distinguish this from any Hawthorne effects or contamination between study groups.

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Such pre-trial observations would also have enabled us to verify whether physicians who are more inclined to treat PONV were indeed well balanced between the two study groups. Evaluating the impact of using a prediction model in a large-scale comparative study requires a phased approach.

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That approach should be tailored to each specific setting in which the model will be used or studied on its impact. The prediction model should be applicable to patients of the new setting in which it is implemented.