Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.

PURPOSE: This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools. METHODS: This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included. RESULTS: A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data. CONCLUSION: AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

State, trait, and accumulated features of the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog) in mild Alzheimer's disease.

Background: The Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog) is used to assess decline in memory, language, and praxis in Alzheimer's disease (AD). Methods: A latent state-trait model with autoregressive effects was used to determine how much of the ADAS-Cog item measurement was reliable, and of that, how much of the information was occasion specific (state) versus consistent (trait or accumulated from one visit to the next). Results: Participants with mild AD (n = 341) were assessed four times over 24 months. Praxis items were generally unreliable as were some memory items. Language items were generally the most reliable, and this increased over time. Only two ADAS-Cog items showed reliability >0.70 at all four assessments, word recall (memory) and naming (language). Of the reliable information, language items exhibited greater consistency (63.4% to 88.2%) than occasion specificity, and of the consistent information, language items tended to reflect effects of AD progression that accumulated from one visit to the next (35.5% to 45.3%). In contrast, reliable information from praxis items tended to come from trait information. The reliable information in the memory items reflected more consistent than occasion-specific information, but they varied between items in the relative amounts of trait versus accumulated effects. Conclusions: Although the ADAS-Cog was designed to track cognitive decline, most items were unreliable, and each item captured different amounts of information related to occasion-specific, trait, and accumulated effects of AD over time. These latent properties complicate the interpretation of trends seen in ordinary statistical analyses of trials and other clinical studies with repeated ADAS-Cog item measures. Highlights: Studies have described unfavorable psychometric properties of the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog), bringing into question its ability to track changes in cognition uniformly over time. There remains a need to estimate how much of the ADAS-Cog measurement is reliable, of that how much is occasion specific versus consistent, and of the consistent information, how much represents enduring traits versus autoregressive effects (i.e., effects of Alzheimer's disease [AD] progression carried over from one assessment to the next).A latent state-trait model with autoregressive effects in mild AD found most items to be unreliable, and each item to capture different amounts of occasion-specific, trait, and autoregressive information. Language items, specifically, naming and the memory item word recall, were the most reliable.Psychometric idiosyncrasies of individual items complicate the interpretation of their summed score, biasing ordinary statistical analyses of repeated measures in mild AD. Future studies should consider item trajectories individually.

Dynamic relationships between depressive symptoms and insulin resistance over 20 years of adulthood.

BACKGROUND: Bidirectional longitudinal relationships between depression and diabetes have been observed, but the dominant direction of their temporal relationships remains controversial. METHODS: The random-intercept cross-lagged panel model decomposes observed variables into a latent intercept representing the traits, and occasion-specific latent 'state' variables. This permits correlations to be assessed between the traits, while longitudinal 'cross-lagged' associations and cross-sectional correlations can be assessed between occasion-specific latent variables. We examined dynamic relationships between depressive symptoms and insulin resistance across five visits over 20 years of adulthood in the population-based Coronary Artery Risk Development in Young Adults (CARDIA) study. Possible differences based on population group (Black v. White participants), sex and years of education were tested. Depressive symptoms and insulin resistance were quantified using the Center for Epidemiologic Studies Depression (CES-D) scale and the homeostatic model assessment for insulin resistance (HOMA-IR), respectively. RESULTS: Among 4044 participants (baseline mean age 34.9 ± 3.7 years, 53% women, 51% Black participants), HOMA-IR and CES-D traits were weakly correlated (r = 0.081, p = 0.002). Some occasion-specific correlations, but no cross-lagged associations were observed overall. Longitudinal dynamics of these relationships differed by population groups such that HOMA-IR at age 50 was associated with CES-D score at age 55 (ß = 0.076, p = 0.038) in White participants only. Longitudinal dynamics were consistent between sexes and based on education. CONCLUSIONS: The relationship between depressive symptoms and insulin resistance was best characterized by weak correlations between occasion-specific states and enduring traits, with weak evidence that insulin resistance might be temporally associated with subsequent depressive symptoms among White participants later in adulthood.

Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review.

Corneal and ocular surface diseases (OSDs) carry significant psychosocial and economic burden worldwide. We set out to review the literature on the application of artificial intelligence (AI) and bioinformatics for analysis of biofluid biomarkers in corneal and OSDs and evaluate their utility in clinical decision making. MEDLINE, EMBASE, Cochrane and Web of Science were systematically queried for articles using AI or bioinformatics methodology in corneal and OSDs and examining biofluids from inception to August 2021. In total, 10,264 articles were screened, and 23 articles consisting of 1058 individuals were included. Using various AI/bioinformatics tools, changes in certain tear film cytokines that are proinflammatory such as increased expression of apolipoprotein, haptoglobin, annexin 1, S100A8, S100A9, Glutathione S-transferase, and decreased expression of supportive tear film components such as lipocalin-1, prolactin inducible protein, lysozyme C, lactotransferrin, cystatin S, and mammaglobin-b, proline rich protein, were found to be correlated with pathogenesis and/or treatment outcomes of dry eye, keratoconus, meibomian gland dysfunction, and Sjögren's. Overall, most AI/bioinformatics tools were used to classify biofluids into diseases subgroups, distinguish between OSD, identify risk factors, or make predictions about treatment response, and/or prognosis. To conclude, AI models such as artificial neural networks, hierarchical clustering, random forest, etc., in conjunction with proteomic or metabolomic profiling using bioinformatics tools such as Gene Ontology or Kyoto Encylopedia of Genes and Genomes pathway analysis, were found to inform biomarker discovery, distinguish between OSDs, help define subgroups with OSDs and make predictions about treatment response in a clinical setting.

The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: A Systematic Review.

Purpose: This study aims to identify the available literature describing the utilization of artificial intelligence (AI) as a clinical tool in uveal diseases. Methods: A comprehensive literature search was conducted in 5 electronic databases, finding studies relating to AI and uveal diseases. Results: After screening 10,258 studies,18 studies met the inclusion criteria. Uveal melanoma (44%) and uveitis (56%) were the two uveal diseases examined. Ten studies (56%) used complex AI, while 13 studies (72%) used regression methods. Lactate dehydrogenase (LDH), found in 50% of studies concerning uveal melanoma, was the only biomarker that overlapped in multiple studies. However, 94% of studies highlighted that the biomarkers of interest were significant. Conclusion: This study highlights the value of using complex and simple AI tools as a clinical tool in uveal diseases. Particularly, complex AI methods can be used to weigh the merit of significant biomarkers, such as LDH, in order to create staging tools and predict treatment outcomes.

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review.

This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case-control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

Endosomal-Lysosomal and Autophagy Pathway in Alzheimer's Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: The endosomal-lysosomal and autophagy (ELA) pathway may be implicated in the progression of Alzheimer's disease (AD); however, findings thus far have been inconsistent. OBJECTIVE: To systematically summarize differences in endosomal-lysosomal and autophagy proteins in the cerebrospinal fluid (CSF) of people with AD and healthy controls (HC). METHODS: Studies measuring CSF concentrations of relevant proteins in the ELA pathway in AD and healthy controls were included. Standardized mean differences (SMD) with 95% confidence intervals (CI) between AD and healthy controls in CSF concentrations of relevant proteins were meta-analyzed using random-effects models. RESULTS: Of 2,471 unique studies, 43 studies were included in the systematic review and meta-analysis. Differences in ELA protein levels in the CSF between AD and healthy controls were observed, particularly in lysosomal membrane (LAMP-1: NAD/NHC = 348/381, SMD [95% CI] = 0.599 [0.268, 0.930], I2 = 72.8%; LAMP-2: NAD/NHC = 401/510, SMD [95% CI] = 0.480 [0.134, 0.826], I2 = 78.7%) and intra-lysosomal proteins (GM2A: NAD/NHC = 390/420, SMD [95% CI] = 0.496 [0.039, 0.954], I2 = 87.7%; CTSB: NAD/NHC = 485/443, SMD [95% CI] = 0.201 [0.029, 0.374], I2 = 28.5%; CTSZ: NAD/NHC = 535/820, SMD [95% CI] = -0.160 [-0.305, -0.015], I2 = 24.0%) and in proteins involved in endocytosis (AP2B1:NAD/NHC = 171/205, SMD [95% CI] = 0.513 [0.259, 0.768], I2 = 27.4%; FLOT1: NAD/NHC = 41/45, SMD [95% CI] = -0.489 [-0.919, -0.058], I2 <0.01). LC3B, an autophagy marker, also showed a difference (NAD/NHC = 70/59, SMD [95% CI] = 0.648 [0.180, 1.116], I2 = 38.3%)), but overall there was limited evidence suggesting differences in proteins involved in endosomal function and autophagy. CONCLUSION: Dysregulation of proteins in the ELA pathway may play an important role in AD pathogenesis. Some proteins within this pathway may be potential biomarkers for AD.

Questioning the Meaning of a Change on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Noncomparable Scores and Item-Specific Effects Over Time.

Longitudinal invariance indicates that a construct is measured over time in the same way, and this fundamental scale property is a sine qua non to track change over time using ordinary mean comparisons. The Alzheimer's Disease Assessment Scale-cognitive (ADAS-Cog) and its subscale scores are often used to monitor the progression of Alzheimer's disease, but longitudinal invariance has not been formally evaluated. A configural invariance model was used to evaluate ADAS-Cog data as a three correlated factors structure for two visits over 6 months, and four visits over 2 years (baseline, 6, 12, and 24 months) among 341 participants with Alzheimer's disease. We also attempted to model ADAS-Cog subscales individually, and furthermore added item-specific latent variables. Neither the three-correlated factors ADAS-Cog model, nor its subscales viewed unidimensionally, achieved longitudinal configural invariance under a traditional modeling approach. No subscale achieved scalar invariance when considered unidimensional across 6 months or 2 years of assessment. In models accounting for item-specific effects, configural and metric invariance were achieved for language and memory subscales. Although some of the ADAS-Cog individual items were reliable, comparisons of summed ADAS-Cog scores and subscale scores over time may not be meaningful due to a lack of longitudinal invariance.

The complement cascade in Alzheimer's disease: a systematic review and meta-analysis.

Genetic evidence implicates a causal role for the complement pathway in Alzheimer's disease (AD). Since studies have shown inconsistent differences in cerebrospinal fluid (CSF) and peripheral blood complement protein concentrations between AD patients and healthy elderly, this study sought to summarize the clinical data. Original peer-reviewed articles measuring CSF and/or blood concentrations of complement or complement regulator protein concentrations in AD and healthy elderly control (HC) groups were included. Of 2966 records identified, means and standard deviations from 86 studies were summarized as standardized mean differences (SMD) by random effects meta-analyses. In CSF, concentrations of clusterin (NAD/NHC = 625/577, SMD = 0.53, Z8 = 8.81, p < 0.005; I2 < 0.005%) and complement component 3 (C3; NAD/NHC = 299/522, SMD = 0.45, Z3 = 3.21, p < 0.005; I2 = 68.40%) were significantly higher in AD, but differences in C1q, C-reactive protein (CRP), serum amyloid protein (SAP), and factor H concentrations were not significant. In peripheral blood, concentrations of CRP were elevated in AD (NAD/NHC = 3404/3332, SMD = 0.44, Z43 = 3.43, p < 0.005; I2 = 93.81%), but differences between groups in C3, C4, C1-inhibitor, SAP, factor H and clusterin concentrations were not significant, and inconsistent between studies. Of 64 complement pathway proteins or regulators in the quantitative synthesis, trends in C1q, factor B, C4a, and late-stage complement pathway components (e.g. C9) in blood, C4 in CSF, and the membrane attack complex in blood and CSF, might be investigated further. The results collectively support elevated complement pathway activity in AD, which was best characterized by increased CSF clusterin concentrations and less consistently by CSF C3 concentrations. Complement activity related to an AD diagnosis was not reflected consistently by the peripheral blood proteins investigated.

A peripheral neutrophil-related inflammatory factor predicts a decline in executive function in mild Alzheimer's disease.

BACKGROUND: Studies suggest a role of the innate immune system, including the activity of neutrophils, in neurodegeneration related to Alzheimer's disease (AD), but prospective cognitive data remain lacking in humans. We aimed to investigate the predictive relationship between neutrophil-associated inflammatory proteins in peripheral blood and changes in memory and executive function over 1 year in patients with AD. METHODS: Participants with AD were identified from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Neutrophil gelatinase-associated lipocalin (NGAL), myeloperoxidase (MPO), interleukin-8 (IL-8), macrophage inflammatory protein-1 beta (MIP-1ß), and tumor necrosis factor (TNF) were assayed by luminex immunofluorescence multiplex assay at baseline. Confirmatory factor analysis was used to test an underlying neutrophil associated plasma inflammatory factor. Composite z-scores for memory and executive function were generated from multiple tests at baseline and at 1 year. A multiple linear regression model was used to investigate the association of the baseline inflammatory factor with changes in memory and executive function over 1 year. RESULTS: Among AD patients (n = 109, age = 74.8 ± 8.1, 42% women, Mini Mental State Examination [MMSE] = 23.6 ± 1.9), the neutrophil-related inflammatory proteins NGAL (λ = 0.595, p < .001), MPO (λ = 0.575, p < .001), IL-8 (λ = 0.525, p < .001), MIP-1ß (λ = 0.411, p = .008), and TNF (λ = 0.475, p < .001) were found to inform an underlying factor. Over 1 year, this inflammatory factor predicted a decline in executive function (ß = - 0.152, p = 0.015) but not memory (ß = 0.030, p = 0.577) in models controlling for demographics, brain atrophy, white matter hyperintensities, the ApoE ε4 allele, concomitant medications, and baseline cognitive performance. CONCLUSIONS: An inflammatory factor constructed from five neutrophil-related markers in peripheral blood predicted a decline in executive function over 1 year in people with mild AD.

Cellular models for discovering prion disease therapeutics: Progress and challenges.

Prions, which cause fatal neurodegenerative disorders such as Creutzfeldt-Jakob disease, are misfolded and infectious protein aggregates. Currently, there are no treatments available to halt or even delay the progression of prion disease in the brain. The infectious nature of prions has resulted in animal paradigms that accurately recapitulate all aspects of prion disease, and these have proven to be instrumental for testing the efficacy of candidate therapeutics. Nonetheless, infection of cultured cells with prions provides a much more powerful system for identifying molecules capable of interfering with prion propagation. Certain lines of cultured cells can be chronically infected with various types of mouse prions, and these models have been used to unearth candidate anti-prion drugs that are at least partially efficacious when administered to prion-infected rodents. However, these studies have also revealed that not all types of prions are equal, and that drugs active against mouse prions are not necessarily effective against prions from other species. Despite some recent progress, the number of cellular models available for studying non-mouse prions remains limited. In particular, human prions have proven to be particularly challenging to propagate in cultured cells, which has severely hindered the discovery of drugs for Creutzfeldt-Jakob disease. In this review, we summarize the cellular models that are presently available for discovering and testing drugs capable of blocking the propagation of prions and highlight challenges that remain on the path towards developing therapies for prion disease.

Reciprocal Predictive Relationships between Amyloid and Tau Biomarkers in Alzheimer's Disease Progression: An Empirical Model.

There is an urgent need to understand the relationships between amyloid-ß (Aß) and tau in the progression of Alzheimer's disease to identify treatment targets. Here we examine reciprocal predictions of brain Aß burden quantified by positron emission tomography and CSF concentrations of Aß42 and phosphorylated tau (p-tau). Each biomarker was examined over 48 months in two separate cross-lagged models; one in asymptomatic healthy elderly people (men and women), and one in patients with Alzheimer's disease (AD) dementia or mild cognitive impairment (MCI). The models examine predictions of each biomarker on the progression of the others, considering each previous and concurrent measure. In healthy elderly, lower CSF Aß42 predicted Aß deposition and reciprocally, Aß burden predicted a decrease in CSF Aß42. Lower CSF Aß42 predicted an increase in CSF p-tau, and CSF p-tau predicted Aß deposition. In AD/MCI, lower CSF Aß42 predicted Aß deposition and Aß burden reciprocally predicted CSF Aß42 changes; however, in contrast to healthy elderly, CSF p-tau concentrations did not predict Aß biomarkers, or vice versa. In post hoc models examining cognitive status, CSF Aß42 predicted Mini Mental State Examination (MMSE) scores in healthy elderly, whereas Aß burden and CSF p-tau predicted MMSE scores in AD/MCI. The findings describe reciprocal predictions between Aß and tau biomarkers in healthy elderly and they implicate mechanisms underlying low CSF Aß42 in Alzheimer's disease pathogenesis and progression. In symptomatic Alzheimer's disease, CSF Aß42 and Aß deposition predicted each other; however, Aß and CSF p-tau progressed independently and they independently predicted cognitive decline.SIGNIFICANCE STATEMENT This study offers empirical evidence concerning the hypothesized "amyloid cascade", as it progressed over 4 years in healthy elderly people and in Alzheimer's disease patients. In healthy elderly, CSF amyloid changes predicted amyloid deposition, CSF phosphorylated tau concentrations, and a decline in cognitive status. Phosphorylated tau concentrations specifically predicted amyloid deposition. In Alzheimer's disease patients, although amyloid deposition and CSF amyloid changes continued to "cascade", there was no evidence to suggest that amyloid and tau biomarkers predicted each other, although both amyloid deposition and CSF tau progression predicted cognitive decline independently. Taking advantage of repeated amyloid PET and CSF measures, this dynamic view offers new insight into the progression of Alzheimer's disease biomarkers and their relationships with cognitive decline.