Precise Detection of Cataracts with Specific High‐Risk Factors by Layered Binary Co‐Ionizers Assisted Aqueous Humor Metabolic Analysis

Abstract

Diabetes and high myopia as well-known high-risk factors can aggravate cataracts, yet clinical coping strategy remains a bottleneck. Metabolic analysis tends to be powerful for precisely detection and mechanism exploration since most of diseases including cataracts are accompanied by metabolic disorder. Herein, a layered binary co-ionizers assisted aqueous humor metabolic analysis tool is proposed for potentially etiological typing and detection of cataracts, including age-related cataracts (ARC), cataracts with diabetes mellitus (CDM), and cataracts with high myopia (CHM). Startlingly, taking advantage of the optimal machine learning algorithm and all metabolic fingerprints, 100% of accuracy, precision, and recall rates are achieved for arbitrary comparison between groups. Moreover, 11, 9, and 7 key metabolites with explicit identities are confirmed as markers of discriminating CDM from ARC, CHM from ARC, and CDM from CHM, and the corresponding area under the curve values of validation cohorts are 0.985, 1.000, and 1.000. Finally, the critical impact of diabetes/high myopia on cataracts is revealed by excavating the change levels and metabolic pathways of key metabolites. This work updates the insights of prevention and treatment about cataracts at metabolic level and throws out huge surprises and progresses metabolic diagnosis toward a reality.

Comprehensive Metabolomics and Machine Learning Identify Profound Oxidative Stress and Inflammation Signatures in Hypertensive Patients with Obstructive Sleep Apnea

“…Furthermore, these potential metabolic biomarkers showed significant correlation with PSG markers, including AHI, ArI, MAD, LSaO 2 , MSaO 2 , CT90, and ODI ( Figure 5 ). Using an advanced machine learning algorithm-based feature reduction method [ 26 , 28 , 51 , 52 ], a small panel of six metabolites were selected as biomarkers for the discrimination of hypertensive individuals with and without OSA. Of note, the AUC values of the six metabolite-based diagnostic model were up to one for the discrimination of OSA and non-OSA patients in both the discovery and validation sets ( Figure 6 ), which was quite high and fully proved the significant value of metabolites in the diagnosis of OSA.…”

“…Biomarker discovery requires not only the optimization of the biomarker usefulness regarding the biological relevance, but also the number of biomarkers [ 26 ], to select a small number of the representative biomarkers that can also maintain a significant performance for OSA diagnosis in hypertensive individuals. The classification and feature ranking models were established by using three machine learning algorithms in MetaboAnalyst software [ 26 , 27 , 28 ], including PLS-DA, support vector machine (SVM), and random forest. In each classification model, twenty top metabolite biomarkers were selected on the basis of the average importance.…”

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

“…17 However, the fragment interference of the traditional organic matrix impedes the expansion of MALDI MS to small-molecule analysis, which limits its application to metabolites analysis. Over the past decade, silicon-based nanomaterials, 18 carbon nanomaterials, 19 noble metal, 20,21 metal oxide, 22,23 organic polymers such as metal organic frameworks (MOFs), 24,25 covalent organic frameworks (COFs), 26 and so on 27 have been reported as the feasible LDI-MS substrates. COFs, as an advanced organic crystalline structure, featuring robust chemical and thermal stability, excellent optical properties, and ultralarge specific area, are regarded as a promising substrate.…”

Ultrathin Niobium Carbide MXenzyme for Remedying Hypertension by Antioxidative and Neuroprotective Actions

“…Nanozymes are nanomaterials with intrinsic enzyme-like characteristics, which have been developed to circumvent the drawbacks of natural enzymes, [11] exhibiting great potential in biomedicine. Up to now, various kinds of nanozymes, involving iron-based, copper-based, vanadium-based, notable metal-based, carbon-based, and metal-organic framework-based counterparts, have been designed and constructed for in vitro active molecules detection, [12] in vivo sensing, [13] biomedical imaging, [14] and diseases therapeutics (e.g., inflammation, [15] neurodegeneration, [16] vaccine, [17] celiac disease, [18] and cancer [19] ). However, no reports of nanozyme in hypertension therapy through special brain nuclei are available at current stage.…”