Molecular Architecture of the SARS-CoV-2 Virus.

SARS-CoV-2 is an enveloped virus responsible for the COVID-19 pandemic. Despite recent advances in the structural elucidation of SARS-CoV-2 proteins, the detailed architecture of the intact virus remains to be unveiled. Here we report the molecular assembly of the authentic SARS-CoV-2 virus using cryoelectron tomography (cryo-ET) and subtomogram averaging (STA). Native structures of the S proteins in pre- and postfusion conformations were determined to average resolutions of 8.7-11 Å. Compositions of the N-linked glycans from the native spikes were analyzed by mass spectrometry, which revealed overall processing states of the native glycans highly similar to that of the recombinant glycoprotein glycans. The native conformation of the ribonucleoproteins (RNPs) and their higher-order assemblies were revealed. Overall, these characterizations revealed the architecture of the SARS-CoV-2 virus in exceptional detail and shed light on how the virus packs its ∼30-kb-long single-segmented RNA in the ∼80-nm-diameter lumen.

In situ architecture and membrane fusion of SARS-CoV-2 Delta variant.

Among the current five Variants of Concern, infections caused by SARS-CoV-2 B.1.617.2 (Delta) variant are often associated with the greatest severity. Despite recent advances on the molecular basis of elevated pathogenicity using recombinant proteins, the architecture of intact Delta virions remains veiled. Moreover, pieces of molecular evidence for the detailed mechanism of S-mediated membrane fusion are missing. Here, we showed the pleomorphic nature of Delta virions from electron beam inactivated samples and reported the in situ structure and distribution of S on the authentic Delta variant. We also captured the virus-virus fusion events, which provided pieces of structural evidence for Delta's attenuated dependency on cellular factors for fusion activation, and proposed a model of S-mediated membrane fusion. Besides, site-specific glycan analysis revealed increased oligomannose-type glycosylation of native Delta S than that of the WT S. Together, these results disclose distinctive factors of Delta being the most virulent SARS-CoV-2 variant.

Adaptive Ion Channels Formed in Ultrathin and Semicrystalline Polymer Interphases for Stable Aqueous Batteries.

Aqueous Zn batteries have recently emerged as promising candidates for large-scale energy storage, driven by the need for a safe and cost-effective technology with sufficient energy density and readily accessible electrode materials. However, the energy density and cycle life of Zn batteries have been limited by inherent chemical, morphological, and mechanical instabilities at the electrode-electrolyte interface where uncontrolled reactions occur. To suppress the uncontrolled reactions, we designed a crystalline polymer interphase for both electrodes, which simultaneously promotes electrode reversibility via fast and selective Zn transport through the adaptive formation of ion channels. The interphase comprises an ultrathin layer of crystalline poly(1H,1H,2H,2H-perfluorodecyl acrylate), synthesized and applied as a conformal coating in a single step using initiated chemical vapor deposition (iCVD). Crystallinity is optimized to improve interphase stability and Zn-ion transport. The optimized interphase enables a cycle life of 9500 for Zn symmetric cells and over 11,000 for Zn-MnO2 full-cell batteries. We further demonstrate the generalizability of this interphase design using Cu and Li as examples, improving their stability and achieving reversible cycling in both. The iCVD method and molecular design unlock the potential of highly reversible and cost-effective aqueous batteries using earth-abundant Zn anode materials, pointing to grid-scale energy storage.

Ranging disambiguation of LiDAR using chirped amplitude-modulated phase-shift method.

Ranging ambiguity is the major challenge in most LiDAR techniques with amplitude modulation, which limits the performance of range detection due to the tradeoff between the ranging precision and the unambiguous range. Here we propose a novel disambiguation method using a laser with chirped amplitude modulation (sweeping modulation frequency), which can in theory infinitely expand the unambiguous range and completely solve the ranging ambiguation problem. The usage of the earlier proposed Chirped Amplitude-Modulated Phase-Shift (CAMPS) technique enables us to detect the phase-shift of chirped signals with high precision. Incorporating this technique with the proposed disambiguation method, the absolute distance well beyond the conventional unambiguous range can easily be found with merely <1% frequency sweep range. When certain conditions are met, the Non-Mechanical Spectrally Scanned LiDAR (NMSL) system employing the CAMPS method and the Dispersion-Tuned Swept Laser (DTSL) can also realize disambiguation in non-mechanical line-scanning measurement.

Broadband dispersion spectroscopy using interferometric phase modulation under background light suppression.

This Letter presents a dispersion spectroscopy method that achieves simultaneous detection of molecular vibrational dispersion over a broad spectral range. The method is implemented with an infrared mode-locked laser, a dispersion-compensated Michelson interferometer, and a multichannel detector. Synchronous detection under interferometric phase modulation near the destructive interference condition is employed to achieve a high signal-to-noise ratio. We successfully demonstrate the method by measuring the dispersion of carbon monoxide gas, achieving a noise-equivalent dispersion of 1.3 × 10-8 cm and a corresponding noise-equivalent absorbance of 6.5 × 10-4 with a measurement time of 2.2 s.

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

PURPOSE OF REVIEW: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). RECENT FINDINGS: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. SUMMARY: Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.

Zwitterionic Polymer Gradient Interphases for Reversible Zinc Electrochemistry in Aqueous Alkaline Electrolytes.

Aqueous alkaline zinc batteries are of scientific and technological interest because of the potential they offer for cost-effective and safe storage of electrical energy. Poor electrochemical reversibility and shape change of the Zn anode, propensity of Zn to become passivated by surface oxides and hydroxide films upon prolonged exposure to the electrolyte, and electroreduction of water are well-studied but remain unsolved challenges. Here, we create and study electrochemical and transport properties of precise, spatially tunable zwitterionic polymer interphases grown directly on Zn using an initiated-chemical vapor deposition polymerization methodology. In aqueous alkaline media, spatial gradients in composition─from the polymer-electrolyte interface to the solid-polymer interface─promote highly reversible redox reactions at high current density (20 mA cm-2) and high areal capacity (10 mAh cm-2). Via molecular dynamics and experimental analyses, we conclude that the interphases function by regulating the distribution and activity of interfacial water molecules, which simultaneously enables fast ion transport and suppression of surface passivation and the hydrogen evolution reaction. To illustrate the practical relevance of our findings, we study aqueous Zn||NiOOH and Zn||air batteries and observe that zwitterionic polymer interphases produce extended life at high currents and high areal capacity.

Super-resolution imaging of flat-mounted whole mouse cornea.

Super-resolution microscopy revolutionized biomedical research with significantly improved imaging resolution down to the molecular scale. To date, only limited studies reported multi-color super-resolution imaging of thin tissue slices mainly because of unavailable staining protocols and incompatible imaging techniques. Here, we show the first super-resolution imaging of flat-mounted whole mouse cornea using single-molecule localization microscopy (SMLM). We optimized immunofluorescence staining protocols for ß-Tubulin, Vimentin, Peroxisome marker (PMP70), and Histone-H4 in whole mouse corneas. Using the optimized staining protocols, we imaged these four intracellular protein structures in the epithelium and endothelium layers of flat-mounted mouse corneas. We also achieved simultaneous two-color spectroscopic SMLM (sSMLM) imaging of ß-Tubulin and Histone-H4 in corneal endothelial cells. The spatial localization precision of sSMLM in these studies was around 20-nm. This work sets the stage for investigating multiple intracellular alterations in corneal diseases at a nanoscopic resolution using whole corneal flat-mount beyond cell cultures.

Circulating angiopoietin-like proteins in metabolic-associated fatty liver disease: a systematic review and meta-analysis.

BACKGROUND: Angiopoietin-like proteins (ANGPTLs) are closely related to insulin resistance and lipid metabolism, and may be a key in metabolic syndrome. Non-alcoholic fatty liver disease (NAFLD) (newly named metabolic-associated fatty liver disease (MAFLD)) is based on metabolic dysfunction. There may be some correlation between ANGPTLs and MAFLD, but the specific correlation is unclear. This study aims to explore the predictive role of ANGPTLs in MAFLD and its progression. METHODS: Seven databases (PubMed, EMBASE, Cochrane Library, CNKI, WANFANG, CBM and Clinicaltrials.gov ) were searched with free terms and MeSH terms. The random-effects model was used to pool the data, and Standardized Mean Difference (SMD) and 95% confidence intervals (CI) were taken as the overall outcome. No language restrictions existed in the article selection. RevMan 5.3, Stata 16 and MetaXL software were applied to analyse the data and the GRADE system was utilized to assess the certainty of evidence. RESULTS: After reviewing 823 related articles, 13 studies (854 cases and 610 controls) met the inclusion criteria, and contributed to this meta-analysis. The results showed that circulating ANGPTL8 level was significantly elevated in the MAFLD group than in the healthy control group (SMD = 0.97 pg/mL, 95%CI: 0.77, 1.18). Conversely, there was no significant difference in the ANGPTL4 (SMD = 0.11 ng/mL, 95%CI: - 0.32, 0.54) and ANGPTL3 (SMD = - 0.95 ng/mL, 95%CI: - 4.38, 2.48) between the two groups. Subgroup analysis showed that: 1) the MAFLD group had significantly higher ANGPTL8 levels than the healthy control group in Asian and other races; 2) the ANGPTL8 levels in Body Mass Index (BMI) > 25 kg/m2 patients with MAFLD were higher than those in the healthy control group; 3) the higher ANGPTL8 levels were observed in moderate to severe MAFLD group than the healthy control group. Meta-regression demonstrated that BMI might effectively explain the high heterogeneity. No significant publication bias existed (P > 0.05). The certainty of evidence was assessed as very low by the GRADE system. CONCLUSIONS: The ANGPTLs may be related to MAFLD. The increased ANGPTL8 level may be positively correlated with different situations of MAFLD, which may act as a potential indicator to monitor the development trends.

Amplitude-modulated continuous-wave laser rangefinder employing Bessel-Gauss beamforming.

We propose an amplitude-modulated continuous-wave laser rangefinder employing Bessel-Gauss beamforming. Our Bessel-Gauss beam was generated by a newly proposed doublet configuration, which is simple and low cost. Such a beam was propagated >2m with a mainlobe having the diameter <1mm. We have conducted proof-of-concept ranging experiments employing the amplitude-modulated continuous-wave scheme with the Bessel-Gauss beam and obtained ranging results of a measurement distance up to 2 m. To the best of our knowledge, this is the first attempt to apply zeroth-order Bessel-Gauss beamforming to laser rangefinders.

Biological Effects of Hypomagnetic Field: Ground-Based Data for Space Exploration.

The future of mankind is tied to the exploration and eventual colonization of space. Currently, people have resided in orbit at a space station. In the future, we will have opportunities to stay on the moon, Mars, or in deeper space, where astronauts are exposed to the hypomagnetic field (HMF), which refers to an extremely weak magnetic field environment compared with the geomagnetic field. However, the potential risks of HMF exposure to human health are often overlooked. Here, we summarize the literature related to the biological effects of HMF and calculate the magnitude of the effect. Briefly, HMF impairs multiple animal systems, especially in the central nervous system. Additionally, HMF is a stress factor in plant growth and reproduction. Finally, HMF combined with other space environments, such as radiation and microgravity, can affect organisms. Further studies are required to explore (i) countermeasures to the adverse effects of HMF, (ii) combined effects of HMF with other factors, and (iii) the intensity-effect relationship. © 2021 Bioelectromagnetics Society.

Machine-learning based spectral classification for spectroscopic single-molecule localization microscopy.

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously captures the spatial locations and emission spectra of single molecular emissions and enables simultaneous multicolor super-resolution imaging. Existing sSMLM relies on extracting spectral signatures, such as weighted spectral centroids, to distinguish different molecular labels. However, the rich information carried by the complete spectral profiles is not fully utilized; thus, the misclassification rate between molecular labels can be high at low spectral analysis photon budget. We developed a machine learning (ML)-based method to analyze the full spectral profiles of each molecular emission and reduce the misclassification rate. We experimentally validated our method by imaging immunofluorescently labeled COS-7 cells using two far-red dyes typically used in sSMLM (AF647 and CF660) to resolve mitochondria and microtubules, respectively. We showed that the ML method achieved 10-fold reduction in misclassification and two-fold improvement in spectral data utilization comparing with the existing spectral centroid method.

Non-diffractive Bessel-Gauss beams for the detection of rotating object free of obstructions.

Bessel-Gauss beams carrying orbital angular momentum are widely known for their non-diffractive or self-reconstructing performance, and have been applied in lots of domains. Here we demonstrate that, by illuminating a rotating object with high-order Bessel-Gauss beams, a frequency shift proportional to the rotating speed and the topological charge is observed. Moreover, the frequency shift is still present once an obstacle exists in the path, in spite of the decreasing of received signals. Our work indicates the feasibility of detecting rotating objects free of obstructions, and has potential as obstruction-immune rotation sensors in engine monitoring, aerological sounding, and so on.

Maxillary molar distalization treated with clear aligners combined with mini-implants and angel button using different traction force: a finite element study.

OBJECTIVES: To evaluate the biomechanical system of molar distalization with clear aligner therapy (CAT) combined with angel button using interradicular mini-implants (IRMIs) with varying elastic forces. MATERIALS AND METHODS: FE models including maxilla, complete maxillary dentition, periodontal ligaments (PDL), composite attachments, mini-implants (MI), and dedicated orthodontic aligner, were constructed. Three groups were created in accordance with the sagittal position of MI. Elastic forces (0 N,1 N,1.5 N,2 N) were applied. RESULTS: CAT without elastics caused labial tipping and intrusion of the anterior teeth. Initial labial tipping and the von Mises stress of the maxillary anterior teeth decreased as the elastic forces increased.

New Insight into the Effects of Endogenous Protein and Lipids on the Enzymatic Digestion of Starch in Sorghum Flour.

The effects of endogenous lipids and protein in sorghum flour on starch digestion were studied following the depletion of lipids and/or protein and after the reconstitution of separated fractions. The removal of protein or lipids moderately increases the digestibility of starch in raw (uncooked) sorghum flour to values close to those for purified starch. Rapid Visco Analyzer data (as a model for the cooking process) show that cooked sorghum flours with lipids have a lower starch digestibility than those without lipids after RVA processing, due to the formation of starch-lipid complexes as evidenced by their higher final viscosity and larger enthalpy changes. Additionally, the formation of a starch-lipid-protein ternary complex was identified in cooked sorghum flour, rather than in a reconstituted ternary mixture, according to the unique cooling stage viscosity peak and a greater enthalpy of lipid complexes. After heating, the sorghum flour showed a lower digestibility than the depleted flours and the reconstituted flours. The results indicate that the natural organization of components in sorghum flour is an important factor in facilitating the interactions between starch, lipids, and protein during RVA processing and, in turn, reducing the starch digestion.

Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.

Background: Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated. Methods: We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset. Results: Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI. Conclusion: Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.

The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning.

BACKGROUND: Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved. METHODS: IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8. RESULTS: Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value. CONCLUSIONS: This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation.

Background: Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings. Methods: To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology. Results: Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from non-affected states. Conclusions: Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition.

Recent advances in glass-ceramics: Performance and toughening mechanisms in restorative dentistry.

The use of glass-ceramics in the medical field has grown significantly since the 1980s. With excellent aesthetic properties, semi-translucency, outstanding mechanical properties, corrosion resistance, wear resistance and great biocompatibility and workability glass-ceramics is one of the most commonly used materials in restorative dentistry and is widely used in veneers, inlays, onlays, all-ceramic crowns, and implant abutments. This review provides an overview of the research progress of glass-ceramics in restorative dentistry, focusing on the classification, performance requirements, toughening mechanisms and their association with clinical performance, as well as the manufacturing and fabrication of glass-ceramics in restorative dentistry. Finally, the developments and prospects of glass-ceramics in restorative dentistry are summarized and discussed.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study.

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.