Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.

Effects of Sous-Vide on Quality, Structure and Flavor Characteristics of Tilapia Fillets.

To investigate the effects of traditional high-temperature cooking and sous-vide cooking on the quality of tilapia fillets, muscle microstructure, texture, lipid oxidation, protein structure, and volatile compounds were analyzed. In comparison with samples subjected to traditional high-temperature cooking, sous-vide-treated samples exhibited less protein denaturation, a secondary structure dominated by α-helices, a stable and compact structure, a significantly higher moisture content, and fewer gaps in muscle fibers. The hardness of the sous-vide-treated samples was higher than that of control samples, and the extent of lipid oxidation was significantly reduced. The sous-vide cooking technique resulted in notable changes in the composition and relative content of volatile compounds, notably leading to an increase in the presence of 1-octen-3-ol, α-pinene, and dimethyl sulfide, and a decrease in the levels of hexanal, D-limonene, and methanethiol. Sous-vide treatment significantly enhanced the structural stability, hardness, and springiness of muscle fibers in tilapia fillets and reduced nutrient loss, enriched flavor, and mitigated effects on taste and fishy odor.

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. METHODS: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. RESULTS: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. INTERPRETATION: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785-795.

Interpretation of artificial intelligence studies for the ophthalmologist.

PURPOSE OF REVIEW: The use of artificial intelligence (AI) in ophthalmology has increased dramatically. However, interpretation of these studies can be a daunting prospect for the ophthalmologist without a background in computer or data science. This review aims to share some practical considerations for interpretation of AI studies in ophthalmology. RECENT FINDINGS: It can be easy to get lost in the technical details of studies involving AI. Nevertheless, it is important for clinicians to remember that the fundamental questions in interpreting these studies remain unchanged - What does this study show, and how does this affect my patients? Being guided by familiar principles like study purpose, impact, validity, and generalizability, these studies become more accessible to the ophthalmologist. Although it may not be necessary for nondomain experts to understand the exact AI technical details, we explain some broad concepts in relation to AI technical architecture and dataset management. SUMMARY: The expansion of AI into healthcare and ophthalmology is here to stay. AI systems have made the transition from bench to bedside, and are already being applied to patient care. In this context, 'AI education' is crucial for ophthalmologists to be confident in interpretation and translation of new developments in this field to their own clinical practice.

Correlation between autochthonous microbial communities and key odorants during the fermentation of red pepper (Capsicum annuum L.).

High-throughput sequencing and gas chromatography-mass spectrometry (GC-MS) were used to investigate changes in bacterial and fungal communities and volatile flavor compounds during a 32-day fermentation process of red pepper (Capsicum annuum L.). Key odorants were identified by olfactometry combined with GC-MS. Sixteen volatile compounds differed significantly after fermentation, including seven odorants. After fermentation, 1-butanol, 3-methyl-, acetate, phenol, 4-ethyl-2-methoxy-, octanoic acid, ethyl ester, styrene and 2-methoxy-4-vinylphenol were the key odorants, producing a flavor described as peppery, fruity, sour, and spicy. The correlation between microorganisms and odorants in the fermentation was studied and 18 odorants significantly correlated with the core microbial communities in the fermented samples. For further analysis, strains of seven genera were isolated and correlation analysis by O2PLS indicated that Aspergillus, Bacillus, Brachybacterium, Microbacterium and Staphylococcus were highly correlated with the flavor formation. These findings would help to understand the fermentation mechanism of fermented red pepper flavor formation.

Effect of exogenous γ-aminobutyric acid treatment on the enzymatic browning of fresh-cut potato during storage.

The aim of this study was to evaluate the effect of γ-aminobutyric acid (GABA) treatment on the enzymatic browning of fresh-cut potatoes. The browning index and activities of browning and defense-related enzymes were analyzed after 0, 1, 2, 3, 4, 5, and 6 days of storage at 4 °C. The results showed that the treatment with 20 g/L GABA for 10 min significantly retarded the browning of fresh-cut potatoes. GABA inhibited the browning of fresh-cut potatoes by enhancing the activities of catalase and superoxide dismutase, and decreasing the activities of polyphenol oxidase and reactive oxygen species. The results suggest that GABA plays an important role in reducing the browning of fresh-cut potatoes. Hence, GABA treatment is a promising approach for reducing the browning and maintaining the quality of fresh-cut potatoes.

Anti-solvent precipitation for encapsulation of oyster protein hydrolysate nanoparticles: Effect on off-flavor elimination and bioaccessibility.

In this study, to investigate the potential for delivering bioactive substances, various cereal proteoglycan nanoparticles were prepared using anti-solvent precipitation for the delivery of oyster protein hydrolysate (OPH). The hydrogen bonding and hydrophobic interaction were the driving forces for OPH embedding. OPH-Loaded Zein/NaCas/Gum Arabic composite nanoparticles could self-assemble into spherical (191 nm) particles with strong stability, uniform distribution (about PDI = 0.2), and high negative charge (about -28 mV). The secondary structure of OPH-Loaded zein was converted from α-helix to random coil with heat treatment. The off-flavor, including (E, E)-2,4-heptadienal, octadecane, and 1-octanol in OPH-Loaded Zein nanoparticles significantly decreased. Furthermore, these nanoparticles containing OPH had excellent oxidation resistance, in vitro digestibility, and non-toxicity in Caco-2 and HT-29 cells. Overall, this work provides an efficient strategy for OPH delivery, suggesting that OPH could be ingested as a nutraceutical in powder form while also promoting the development of high-flavor-quality oyster products.

Research progress of fishy odor in aquatic products: From substance identification, formation mechanism, to elimination pathway.

Fishy odor in aquatic products has a significant impact on the purchasing decisions of consumers. The production of aquatic products is a complex process involving culture, processing, transportation, and storage, which contribute to decreases in flavor and quality. This review systematically summarizes the fishy odor composition, identification methods, generation mechanism, and elimination methods of fishy odor compounds from their origin and formation to their elimination. Fishy odor compounds include aldehydes (hexanal, heptanal, and nonanal), alcohols (1-octen-3-ol), sulfur-containing compounds (dimethyl sulfide), and amines (trimethylamine). The mechanism of action of various factors affecting fishy odor is revealed, including environmental factors, enzymatic reactions, lipid oxidation, protein degradation, and microbial metabolism. Furthermore, the control and removal of fishy odor are briefly summarized and discussed, including masking, elimination, and conversion. This study provides a theoretical basis from source to elimination for achieving targeted regulation of the flavor of aquatic products, promoting industrial innovation and upgrading.

A review of phycocyanin: Production, extraction, stability and food applications.

This paper aims to review the production, extraction, stability and food application of phycocyanin. Currently, light source modulation and organic solvent cultivation of high biomass phycocyanin is an important research direction. The development of nitrogen oxygen balanced system-assisted culture environments to raise production will become a trend, the green and sustainable characteristics of which compensate for the drawbacks of the former. Microfiltration, ultrafiltration, and ultrasonic cell rupture technologies address the drawbacks of solvent extraction and achieve a significant increase in purity. Biorefining technology may become the trend to achieve the highest purity and efficiency in large-scale production of phycocyanin. For the stability of phycocyanin, the development of complexes is a trend, but it should consider the suitability of the materials complex with them for production as foodstuffs. Phycocyanin is mainly developed as a natural pigment, and the main point is the coloring power and stability of natural pigments.

Identification and validation of core microbes for the formation of the characteristic flavor of fermented oysters (Crassostrea gigas).

Self-fermented oyster homogenates were prepared to investigate core microbes and their correlations with flavor formation mechanisms. Five bacterial and four fungal genera were identified. Correlation analysis showed that Saccharomyces cerevisiae, Kazachstania, and L. pentosus were core species for the flavor of fermented products. Four core microbes were selected for inoculation into homogenates. Twelve key aroma compounds with odor activity values >1 were identified by gas chromatography-mass spectrometry. L. plantarum and S. cerevisiae were beneficial for producing key aroma compounds such as 1-octen-3-ol, (E,Z)-2,6-nonadienal, and heptanal. Fermentation with four microbes resulted in significant increases in contents of Asp, Glu, Lys, inosine monophosphate, and guanosine monophosphate, which provided freshness and sweetness. Fermentation with four microbes resulted in high digestibility, antioxidant abilities, and zinc contents. This study has elucidated the mechanism of flavor formation by microbial action and provides a reference for targeted flavor control in fermented oyster products.

The influence mechanism of phospholipids structure and composition changes caused by oxidation on the formation of flavor substances in sturgeon caviar.

The oxidation-induced phospholipids (PLs) underwent structural and compositional analysis, alongside the establishment of a simulation system to verify the link between phospholipid oxidation and flavor substances formation in sturgeon caviar. Structural alterations of PLs were tracked using 31P and 1H nuclear magnetic resonance (NMR), electron spin resonance spectroscopy (ESR), and Raman spectroscopy. The findings revealed a reduction in phosphatidylcholine (PC) and phosphatidylethanolamine (PE) from 82.3% and 10.4% to 58.2% and 5.8% respectively. Free radical signals exhibited an initial increase followed by a decrease. The diminished intensity in Raman spectra at 970 and 1080 cm-1 indicated reduced fat unsaturation attributable to PLs oxidation. Correlation analysis highlighted a significant association between PC and PE containing C22:6, C20:5, C20:4, and C18:2 with flavor substances, suggesting their role as key precursors for flavor development. This study established a theoretical basis for understanding the change of flavor quality in sturgeon caviar during storage.

Preparation and characterization of kelp polysaccharide and its research on anti-influenza a virus activity.

The beneficial effects of kelp polysaccharide (KPS) have recently attracted attention. In this study, KPS was extracted from kelp using the enzyme hydrolysis combined with freeze-drying, namely, KPS-EF. The structural characterization showed that KPS-EF was a highly sulfated macromolecule with the Mw of 764.2 kDa and the sulfate content of 23.49 %. The antiviral activity of KPS-EF in vitro was verified, and the IC50 value of KPS against the PR8 virus was 0.58 mg/mL. Intranasal administration of KPS-EF significantly inhibited death and weight loss in IAV-infected mice and alleviated virus-induced pneumonia symptoms, meanwhile, KPS-EF (10 mg/kg/day) significantly decreased the production levels of chemokines (CXCL1, RANTES) and inflammatory cytokines (IL-6, TNF-α) in lungs (p < 0.05). KPS-EF could downregulate the activity of viral neuraminidase (NA) primarily in the late stage of viral adsorption with an IC50 value of 0.29 mg/mL. This study provides a theoretical basis for the using KPS as a supplement to NA inhibitors or anti-influenza drugs.

Study on antimicrobial activity of sturgeon skin mucus polypeptides (Rational Design, Self-Assembly and Application).

Despite the favorable biocompatibility of natural antimicrobial peptides (AMPs), their scarcity limits their practical application. Through rational design, the activity of AMPs can be enhanced to expand their application. In this study, we selected a natural sturgeon epidermal mucus peptide, AP-16 (APATPAAPALLPLWLL), as the model molecule and studied its conformational regulation and antimicrobial activity through amino acid substitutions and N-terminal lipidation. The structural and morphological transitions of the peptide self-assemblies were investigated using circular dichroism and transmission electron microscopy. Following amino acid substitution, the conformation of AL-16 (AKATKAAKALLKLWLL) did not change. Following N-terminal alkylation, the C8-AL-16 and C12-AL-16 conformations changed from random coil to ß-sheet or α-helix, and the self-assembly changed from nanofibers to nanospheres. AL-16, C8-AL-16, and C8-AL-16 presented significant antimicrobial activity against Pseudomonas and Shewanella at low concentrations. N-terminal alkylation effectively extended the shelf life of Litopenaeus vannamei. These results support the application of natural AMPs.

Contribution of phospholipase B to the formation of characteristic flavor in steamed sturgeon meat.

Sensory analysis and untargeted lipidomics were employed to study the impact of phospholipase B (PLB) on lipid oxidation and flavor in steamed sturgeon meat, revealing the inherent relationship between lipid oxidation and flavor regulation. The research verified that PLB effectively suppresses fat oxidation and improves the overall taste of steamed sturgeon meat. Furthermore, the PLB group identified 52 compounds, and the content of odor substances such as isoamyl alcohol and hexanal was reduced compared with other groups. Finally, lipid substances containing eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA) were screened out from 32 kinds of differential phospholipids. Through Pearson correlation analysis, it was observed that certain differential phospholipids such as PC (22:6) and PC (22:5) exhibited varying correlations with odor substances like hexanal and isovaleraldehyde. These findings suggest that PLB specifically affects certain phospholipids, leading to the production of distinct volatile substances through oxidative degradation.

Hypoglycemic peptide preparation from Bacillus subtilis fermented with Pyropia: Identification, molecular docking, and in vivo confirmation.

Hypoglycemic foods have attracted increasing research interest. This study prepared a hypoglycemic product from Bacillus subtilis fermented with Pyropia (PBP), which has promising industrial potential, and elucidated its hypoglycemic mechanism. The aqueous PBP solution was orange, with protein as the main functional component. In vivo experiments demonstrated that PBP could increase insulin secretion and inhibit α-glucosidase activity, resulting in a hypoglycemic effect superior to that of acarbose at the same dose. Molecular docking revealed that the peptides APPVDID, GPPDSPY, PPSSPRP, and SPPPPPA from PBP could inhibit both α-glucosidase and dipeptidyl peptidase-IV (DPP-IV) activities. Pro residues promoted PBP peptide binding to the hydrophobic pocket S1 of DPP-IV. Additionally, PBP reduced inflammation and promoted the growth of beneficial gut bacteria (Prevotellaceae_UCG_003, Lachnospiraceae_UCG_001). This study presents a novel approach for the high-value utilization of Pyropia and a new option for the production of hypoglycemic functional foods and medicines.

Multi-omics combined approach to analyze the mechanism of flavor evolution in sturgeon caviar (Acipenser gueldenstaedtii) during refrigeration storage.

Multi-omics techniques were combined with microstructure, molecular sensory science and non-volatile matrices for the first time to investigate variations in organic macromolecules and flavor in caviar during preservation. After 4-6 weeks of storage, the peroxide value was 35.38 mg/g and the accumulation of thiobarbiturates was significant with caviar membranes exhibiting a decrease in elasticity and an increase in viscosity. Sixteen key volatile compounds were detected by GC-MS, while the volatile compounds that contributed to the differences in caviar flavor at different storage times were mainly tetradecane, (E)-2-hexenal, and heptanal. The pathways associated with flavor release during storage were mainly abundant in the linolenic acid metabolism, alanine metabolism, and glycerophospholipid metabolism pathways. The correlation of 11 differential proteins and 24 differential lipids with odorants was further explored, such as arginine, proline, alanine, PE (20:4/22:6), PE (16:1/18:2), and PE (20:5/18:2). Overall, Aspartate, glutamate, oleic acid, linoleic acid, and phospholipids enriched in C22:6 and C18:2 chains are potential metabolic markers. This study provides a basis from a multi-omics perspective for the investigation of the relationship between quality deterioration and precursor metabolism in caviar storage process.