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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.
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Inteligência Artificial , Aprendizado Profundo , Pneumotórax , Humanos , Pneumotórax/diagnóstico por imagem , Algoritmos , Tomografia Computadorizada por Raios X/métodos , Informática Médica/métodosRESUMO
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.).
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Aprendizado Profundo , Fundo de Olho , Redes Neurais de Computação , Oftalmoscopia/métodos , Papiledema/diagnóstico , Fotografação , Retina/diagnóstico por imagem , Algoritmos , Área Sob a Curva , Conjuntos de Dados como Assunto , Diagnóstico Diferencial , Humanos , Valor Preditivo dos Testes , Curva ROC , Retina/patologia , Estudos Retrospectivos , Sensibilidade e EspecificidadeRESUMO
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.
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Aprendizado Profundo , Disco Óptico , Papiledema , Humanos , Disco Óptico/diagnóstico por imagem , Inteligência Artificial , Estudos Retrospectivos , Estudos TransversaisRESUMO
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.
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Tilápia , Animais , Culinária/métodos , LipídeosRESUMO
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.
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Extração de Catarata , Catarata , Aprendizado Profundo , Catarata/diagnóstico , Humanos , FotografaçãoRESUMO
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.
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Aprendizado Profundo , Técnicas de Diagnóstico Oftalmológico , Interpretação de Imagem Assistida por Computador/métodos , Disco Óptico , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Oftalmologistas , Sensibilidade e EspecificidadeRESUMO
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.
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Inteligência Artificial , Interpretação Estatística de Dados , Oftalmologistas , Atenção à Saúde , Humanos , Oftalmologia/métodosRESUMO
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.
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Capsicum/microbiologia , Alimentos Fermentados , Microbiota , Odorantes/análise , Ácidos/análise , Ácidos/química , Bactérias/classificação , Bactérias/genética , Bactérias/isolamento & purificação , Bactérias/metabolismo , Biodiversidade , Fermentação , Alimentos Fermentados/análise , Alimentos Fermentados/microbiologia , Fungos/classificação , Fungos/genética , Fungos/isolamento & purificação , Fungos/metabolismo , Concentração de Íons de Hidrogênio , Paladar , Compostos Orgânicos Voláteis/análise , Compostos Orgânicos Voláteis/química , Compostos Orgânicos Voláteis/metabolismoRESUMO
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.
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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.
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Aldeídos , OdorantesRESUMO
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.
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Bactérias , Crassostrea , Fermentação , Aromatizantes , Paladar , Animais , Crassostrea/microbiologia , Crassostrea/metabolismo , Crassostrea/química , Aromatizantes/metabolismo , Aromatizantes/química , Bactérias/metabolismo , Bactérias/classificação , Bactérias/isolamento & purificação , Cromatografia Gasosa-Espectrometria de Massas , Odorantes/análise , Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/química , Fungos/metabolismo , Fungos/classificação , Compostos Orgânicos Voláteis/metabolismo , Compostos Orgânicos Voláteis/química , Compostos Orgânicos Voláteis/análise , Frutos do Mar/análise , Frutos do Mar/microbiologiaRESUMO
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.
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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.
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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.
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This study investigated the effect of low-voltage electrostatic field on the flavor quality changes and generation pathways of refrigerated sturgeon caviar. Research has found that after storage for 3-6 weeks, the physicochemical properties of caviar in the LVEF treatment group are better than those in the control group. The results of two-dimensional gas chromatography-time-of-flight mass spectrometry showed that the contents of hexanal, nonanal, (E,Z)-2,6-nonadienal, (E)-2-octenal and 1-octene-3-one related to the characteristic flavor of caviar (sweet, fruity and green) increased significantly. The lipidomics results indicated that the effects of LVEF on caviar mainly involve glycerophospholipid metabolism, linoleic acid metabolism, and α-Linolenic acid metabolism. Methanophosphatidylcholine (15:0/18:1), phosphatidylcholine (18:0/20:5), and phosphatidylcholine (18,1e/22:6) were significantly correlated with odor formation. Therefore, low-voltage electrostatic field treatment preserved the quality and enhanced the flavor of sturgeon caviar. This study provided a new theoretical basis for the preservation of sturgeon caviar.
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As the strategic importance of Li in the energy sector increases, selective Li extraction technology from spent lithium-ion batteries (LIBs) is attracting increasing attention. Current Li extraction processes typically suffer from lengthy procedures, high costs, and low efficiency. To improve the efficiency of Li extraction, a novel approach to achieve efficient Li recovery is proposed in this study, namely, reacting pyrite (FeS2) with LiNixCoyMnzO2 (NCM) powder in a subcritical water reduction (SWR) system. The reducing solvent environment created by the enhanced reaction of FeS2 with subcritical water converts the high-valent metals in NCM to a low-valent state, causing the collapse of the stable laminar structure and allowing Li+ to be released smoothly. After dual activation through mechanochemical and roasting processes, more than 99 % of Li is preferentially extracted under optimal conditions. Furthermore, Li+ in solution is converted into highly pure Li2CO3, while other metallic elements remain in the residue. Using inexpensive FeS2 for efficient Li extraction without adding additional chemical reagents is a promising approach for recovering spent LIBs.
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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.
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The volatile and non-volatile compounds were monitored to investigate the microbial evolution associated with the characteristic flavors for sturgeon caviar during refrigeration. The results revealed that the composition of volatile compounds changed significantly with prolonged refrigeration time, especially hexanal, nonanal, phenylacetaldehyde, 3-methyl butyraldehyde, and 1-octen-3-ol. The nonvolatile metabolites were mainly represented by the increase of bitter amino acids (Thr. Ser, Gly, Ala, and Pro) and a decrease in polyunsaturated fatty acids, especially an 18.63 % decrease in 5 months of storage. A total of 332 differential metabolites were mainly involved in the biosynthetic metabolic pathways of α-linolenic acid, linoleic acid, and arachidonic acid. The precursors associated with flavor evolution were mainly phospholipids, including oleic, linoleic, arachidonic, eicosapentaenoic (EPA), and docosahexaenoic (DHA) acids. The most abundant at the genus level was Serratia, followed by Arsenophnus, Rhodococcus, and Pseudomonas, as obtained by high-throughput sequencing. Furthermore, seven core microorganisms were isolated and characterized from refrigerated caviar. Among them, inoculation with Mammalian coccus and Bacillus chrysosporium restored the flavor profile of caviar and enhanced the content of nonvolatile precursors, contributing to the characteristic aroma attributes of sturgeon caviar. The study presents a theoretical basis for the exploitation of technologies for quality stabilization and control of sturgeon caviar during storage.
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Ácidos Graxos Insaturados , Peixes , Animais , Fosfolipídeos , Produtos Pesqueiros , Ácido Linoleico , MamíferosRESUMO
Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR. Design: Retrospective study. Participants: A total of 48 269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included. Methods: Using 41 614 retinal photographs from the Kaggle dataset, we developed 5 CNN (Visual Geometry Group 19, ResNet50, InceptionV3, DenseNet201, and EfficientNetV2S) and 4 ViTs models (VAN_small, CrossViT_small, ViT_small, and Hierarchical Vision transformer using Shifted Windows [SWIN]_tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all 9 models was evaluated in the Kaggle internal test dataset (with 1045 study eyes), and in 2 external test sets, the SEED study (5455 study eyes) and the Messidor-1 (1200 study eyes). Main Outcome Measures: Area under operating characteristics curve (AUC), specificity, and sensitivity. Results: Among all models, the SWIN transformer displayed the highest AUC of 95.7% on the internal test set, significantly outperforming the CNN models (all P < 0.001). The same observation was confirmed in the external test sets, with the SWIN transformer achieving AUC of 97.3% in SEED and 96.3% in Messidor-1. When specificity level was fixed at 80% for the internal test, the SWIN transformer achieved the highest sensitivity of 94.4%, significantly better than all the CNN models (sensitivity levels ranging between 76.3% and 83.8%; all P < 0.001). This trend was also consistently observed in both external test sets. Conclusions: Our findings demonstrate that ViTs provide superior performance over CNNs in detecting referable DR from retinal photographs. These results point to the potential of utilizing ViT models to improve and optimize retinal photo-based deep learning for referable DR detection. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.