Functional Interpretation Research Articles

Projectile points, dangers and Amerindian ontologies at eastern Catamarca (Argentina) during the first millennium CE

ABSTRACT Evaluating the links between South American Amerindian ontologies and the contextual study of knapped lithic technology is the main goal of this paper. To this end, we focus on a study case from eastern Catamarca province, Argentina, during the 1st millennium A.D. We identified projectile points in only one context, a rock art rock shelter (Oyola 7), while in other archaeological sites in the area, we have not recorded this type of stone tool. Drawing on ethnohistoric and ethnographic data regarding the connection between arrows and shamanic practices of illness and healing, we propose considering the agencies of these objects, as well as their intrinsic danger and their multiple possibilities of affecting beyond their traditional functional interpretation.

The solution of the task of dynamic interpretation of seismic data using machine learning methods

This article examines the problem of dynamically interpreting seismic data using machine learning models, which include Extremely Randomized Trees (Extra Trees), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost) for the given problem. The study analyzes some existing solutions of the problem and describes the advantages of these machine learning models. Accuracy is estimated using the root mean square error metric. The authors found that dynamic interpretation and prediction of seismic data using these machine learning methods had not been extensively explored in research on related topics, which became the main focus of the study. The article formalizes the use of the mentioned models and highlights features and advantages for the given problem. Several common machine learning methods were investigated to find functional relationships between input parameters. Computational experiments were conducted to evaluate their applicability and compare the algorithms. The results show that the Extra Trees method is the most suitable for practical use for the given problem, as it demonstrates the highest accuracy in determining functional relationships and dynamic interpretation.

A maternal germline mutator phenotype in a family affected by heritable colorectal cancer.

Variation in DNA repair genes can increase cancer risk by elevating the rate of oncogenic mutation. Defects in one such gene, MUTYH, are known to elevate the incidence of colorectal cancer in a recessive Mendelian manner. Recent evidence has also linked MUTYH to a mutator phenotype affecting normal somatic cells as well as the female germline. Here, we use whole genome sequencing to measure germline de novo mutation rates in a large extended family containing both mothers and fathers who are affected by pathogenic MUTYH variation. By developing novel methodology that uses siblings as "surrogate parents" to identify de novo mutations, we were able to include mutation data from several children whose parents were unavailable for sequencing. In the children of mothers affected by the pathogenic MUTYH genotype p.Y179C/V234M, we identify an elevation of the C>A mutation rate that is weaker than mutator effects previously reported to be caused by other pathogenic MUTYH genotypes, suggesting that mutation rates in normal tissues may be useful for classifying cancer-associated variation along a continuum of severity. Surprisingly, we detect no significant elevation of the C>A mutation rate in children born to a father with the same MUTYH genotype, and we similarly find that the mutator effect of the mouse homolog Mutyh appears to be localized to embryonic development, not the spermatocytes. Our results suggest that maternal MUTYH variants can cause germline mutations by attenuating the repair of oxidative DNA damage in the early embryo.

Translation of genome-wide association study: from genomic signals to biological insights.

Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.

Cortical development in the structural model and free energy minimization.

A model of neocortical development invoking Friston's Free Energy Principle is applied within the Structural Model of Barbas etal. and the associated functional interpretation advanced by Tucker and Luu. Evolution of a neural field with Hebbian and anti-Hebbian plasticity, maximizing synchrony and minimizing axonal length by apoptotic selection, leads to paired connection systems with mirror symmetry, interacting via Markov blankets along their line of reflection. Applied to development along the radial lines of development in the Structural Model, a primary Markov blanket emerges between the centrifugal synaptic flux in layers 2,3 and 5,6, versus the centripetal flow in layer 4, and axonal orientations in layer 4 give rise to the differing shape and movement sensitivities characteristic of neurons of dorsal and ventral neocortex. Prediction error minimization along the primary blanket integrates limbic and subcortical networks with the neocortex. Synaptic flux bypassing the blanket triggers the arousal response to surprising stimuli, enabling subsequent adaptation. As development progresses ubiquitous mirror systems separated by Markov blankets and enclosed blankets-within-blankets arise throughout neocortex, creating the typical order and response characteristics of columnar and noncolumnar cortex.

Evoked Component Analysis (ECA): Decomposing the Functional Ultrasound Signal With GLM-Regularization.

Analysis of functional neuroimaging data aims to unveil spatial and temporal patterns of interest. Existing analysis methods fall into two categories: fully data-driven approaches and those reliant on prior information, e.g. the stimulus time course. While using the stimulus signal directly can help identify the activated brain areas, it is known that the relationship between stimuli and the brain's response exhibits nonlinear and time-varying characteristics. As such, relying completely on the stimulus signal to describe the brain's temporal response leads to a restricted interpretation of the brain function. In this paper, we present a new technique called Evoked Component Analysis (ECA), which leverages prior information up to a defined extent. This is achieved by including the general linear model (GLM) design matrix as a regulatory term and estimating the factor matrices in both space and time through an alternating minimization approach. We apply ECA to 2D and swept-3D functional ultrasound (fUS) experiments conducted with mice. When decomposing 2D fUS data, we employ GLM regularization at various intensities to emphasize the role of prior information. Furthermore, we show that incorporating multiple hemodynamic response functions within the design matrix can provide valuable insights into region-specific characteristics of evoked activity. Finally, we use ECA to analyze swept-3D fUS data recorded from five mice engaged in two distinct visual tasks. Swept-3D fUS images the 3D brain sequentially using a moving probe, resulting in different slice acquisition time instants. We show that ECA can estimate factor matrices with a fine resolution at each slice acquisition time instant and yield higher t-statistics compared to GLM and correlation analysis for all subjects.

Metal-organic framework-derived hierarchical flower-like DyCo-layered double hydroxide amalgamated nitrogen-doped graphene for diphenylamine detection in fruit samples: Theoretical density functional theory interpretation

Diphenylamine (DPA) is an environmental pollutant that can be potentially toxic. As a result, it is crucial to use basic and affordable analytical techniques to detect DPA. Electrochemical detection of DPA is a cost-effective and simple method. Modifying the electrodes with nanomaterials can enhance the electrochemical characteristics and sensitivity of the sensor. Herein, metal-organic framework (MOF) derived dysprosium cobalt-layered double hydroxide integrated nitrogen-doped graphene (DyCo-LDH/NG) is reported for the fabrication of the DPA sensing platform. The electrochemical oxidation of DPA is enhanced by the exceptional electrocatalytic activity and electron transfer properties of the DyCo-LDH/NG nanocomposite. Interestingly, the glassy carbon electrode (GCE) modified with DyCo-LDH/NG nanocomposite demonstrates a large linear detection range (0.05-470μM) and a low limit of detection (0.012µM). The density functional theory (DFT) study is employed to examine the energy levels and electron transfer sites of DPA during the electro-oxidation process. Furthermore, the practical efficiency test of the developed DPA sensor demonstrates a substantial recovery in fruit samples.

DAVID Ortholog: an integrative tool to enhance functional analysis through orthologs.

The Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a web-based bioinformatics system for the functional interpretation of large lists of genes/proteins generated from high-throughput assays. It has been cited in 72 287 papers since its debut in 2003 as of 23 July 2024. The analysis is usually limited to the species of study. However, the knowledge of genes may be incomplete or unavailable for some species. Model organisms have been studied more extensively and analyzing gene lists in the context of these species can offer valuable insights, helping users better understand the genes and biological themes in their species of interest. We developed DAVID Ortholog for the conversion of gene lists between species. We utilized the ortholog data downloaded from Orthologous MAtrix (OMA) and Ensembl Compara as the base for the conversion. The OMA ortholog IDs and Ensembl gene IDs were converted to DAVID gene IDs and the pairing information of these IDs from these two sources was integrated into the DAVID Knowledgebase. DAVID Ortholog can convert the user's source gene list to an ortholog list of a desired species and the downstream DAVID analysis, in the context of that species, can be continued seamlessly, allowing users to further understand the biological meaning of their gene list based on the functional annotation found for the orthologs. https://davidbioinformatics.nih.gov/ortholog.jsp.

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC.

Three-dimensional nuclear DNA architecture comprises well-studied intra-chromosomal (cis) folding and less characterized inter-chromosomal (trans) interfaces. Current predictive models of 3D genome folding can effectively infer pairwise cis-chromatin interactions from the primary DNA sequence but generally ignore trans contacts. There is an unmet need for robust models of trans-genome organization that provide insights into their underlying principles and functional relevance. We present TwinC, an interpretable convolutional neural network model that reliably predicts trans contacts measurable through genome-wide chromatin conformation capture (Hi-C). TwinC uses a paired sequence design from replicate Hi-C experiments to learn single base pair relevance in trans interactions across two stretches of DNA. The method achieves high predictive accuracy (AUROC=0.80) on a cross-chromosomal test set from Hi-C experiments in heart tissue. Mechanistically, the neural network learns the importance of compartments, chromatin accessibility, clustered transcription factor binding and G-quadruplexes in forming trans contacts. In summary, TwinC models and interprets trans genome architecture, shedding light on this poorly understood aspect of gene regulation.

Confidence and second-order errors in cortical circuits.

Minimization of cortical prediction errors has been considered a key computational goal of the cerebral cortex underlying perception, action, and learning. However, it is still unclear how the cortex should form and use information about uncertainty in this process. Here, we formally derive neural dynamics that minimize prediction errors under the assumption that cortical areas must not only predict the activity in other areas and sensory streams but also jointly project their confidence (inverse expected uncertainty) in their predictions. In the resulting neuronal dynamics, the integration of bottom-up and top-down cortical streams is dynamically modulated based on confidence in accordance with the Bayesian principle. Moreover, the theory predicts the existence of cortical second-order errors, comparing confidence and actual performance. These errors are propagated through the cortical hierarchy alongside classical prediction errors and are used to learn the weights of synapses responsible for formulating confidence. We propose a detailed mapping of the theory to cortical circuitry, discuss entailed functional interpretations, and provide potential directions for experimental work.

Electrophysiological (EEG) microstates during dream-like bizarre experiences in a naturalistic scenario using immersive virtual reality.

Monitoring the reality status of conscious experience is essential for a human being to interact successfully with the external world. Despite its importance for everyday functioning, reality monitoring can systematically become erroneous, for example, while dreaming or during hallucinatory experiences. To investigate brain processes associated with reality monitoring occurring online during an experience, i.e., perceptual reality monitoring, we assessed EEG microstates in healthy, young participants. In a within-subjects design, we compared the experience of reality when being confronted with dream-like bizarre elements versus realistic elements in an otherwise highly naturalistic real-world scenario in immersive virtual reality. Dream-like bizarreness induced changes in the subjective experience of reality and bizarreness, and led to an increase in the contribution of a specific microstate labelled C'. Microstate C' was related to the suspension of disbelief, i.e. the suppression of bizarre mismatches. Together with the functional interpretation of microstate C' as reported by previous studies, the findings of this study point to the importance of prefrontal meta-conscious control processes in perceptual reality monitoring.

A zero precision loss framework for EEG channel selection: enhancing efficiency and maintaining interpretability

The brain-computer interface (BCI) systems based on motor imagery typically rely on a large number of electrode channels to acquire information. The rational selection of electroencephalography (EEG) channel combinations is crucial for optimizing computational efficiency and enhancing practical applicability. However, evaluating all potential channel combinations individually is impractical. This study aims to explore a strategy for quickly achieving a balance between maximizing channel reduction and minimizing precision loss. To this end, we developed a spatio-temporal attention perception network named STAPNet. Based on the channel contributions adaptively generated by its subnetwork, we propose an extended step bi-directional search strategy that includes variable ratio channel selection (VRCS) and strided greedy channel selection (SGCS), designed to enhance global search capabilities and accelerate the optimization process. Experimental results show that on the High Gamma and BCI Competition IV 2a public datasets, the framework respectively achieved average maximum accuracies of 91.47% and 84.17%. Under conditions of zero precision loss, the average number of channels was reduced by a maximum of 87.5%. Additionally, to investigate the impact of neural information loss due to channel reduction on the interpretation of complex brain functions, we employed a heatmap visualization algorithm to verify the universal importance and complete symmetry of the selected optimal channel combination across multiple datasets. This is consistent with the brain’s cooperative mechanism when processing tasks involving both the left and right hands.

Multi-Omics Analysis Revealed the rSNPs Potentially Involved in T2DM Pathogenic Mechanism and Metformin Response.

The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.

Noncanonical formation of SNX5 gene-derived circular RNA regulates cancer growth

Oral squamous cell carcinoma (OSCC) is a prevalent cancer worldwide, exhibiting unique regional prevalence. Despite advancements in diagnostics and therapy, the 5-year survival rate for patients has seen limited improvement. A deeper understanding of OSCC pathogenesis, especially its molecular underpinnings, is essential for improving detection, prevention, and treatment. In this context, noncoding RNAs, such as circular RNAs (circRNAs), have gained recognition as crucial regulators and potential biomarkers in OSCC progression. Our study highlights the discovery of previously uncharacterized circRNAs, including a SNX5 gene-derived circRNA, circSNX5, through deep sequencing of OSCC patient tissue transcriptomes. We established circSNX5’s tumor-specific expression and its strong correlation with patient survival using structure-specific and quantitative PCR analyses. In vitro and in vivo experiments underscored circSNX5 RNA’s regulatory role in cancer growth and metastasis. Further, our omics profiling and functional assays revealed that ADAM10 is a critical effector in circSNX5-mediated cancer progression, with circSNX5 maintaining ADAM10 expression by sponging miR-323. This novel circRNA-miRNA-mRNA regulatory axis significantly contributes to oral cancer progression and malignancy. Moreover, we discovered that circSNX5 RNA is produced via noncanonical sequential back-splicing of pre-mRNA, a process negatively regulated by the RNA-binding protein STAU1. This finding adds a new dimension to our understanding of exonic circRNA biogenesis in the eukaryotic transcriptome. Collectively, our findings offer a detailed mechanistic dissection and functional interpretation of a novel circRNA, shedding light on the role of the noncoding transcriptome in cancer biology and potentially paving the way for innovative therapeutic strategies.

Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response.

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem-cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-art predictive computational methods have facilitated the prediction of cancer stemness, currently there is no efficient resource that can meet various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at the bulk and single-cell levels. The resource integrates 8 robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five different aspects, including identifying the signature genes of cancer stemness, exploring the associations with cancer hallmarks, cellular states, the immune response, and communication with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding the downstream functional interpretation, including immune response as well as cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

Correctness Comparison of ChatGPT‐4, Gemini, Claude‐3, and Copilot for Spatial Tasks

ABSTRACTGenerative AI including large language models (LLMs) has recently gained significant interest in the geoscience community through its versatile task‐solving capabilities including programming, arithmetic reasoning, generation of sample data, time‐series forecasting, toponym recognition, or image classification. Existing performance assessments of LLMs for spatial tasks have primarily focused on ChatGPT, whereas other chatbots received less attention. To narrow this research gap, this study conducts a zero‐shot correctness evaluation for a set of 76 spatial tasks across seven task categories assigned to four prominent chatbots, that is, ChatGPT‐4, Gemini, Claude‐3, and Copilot. The chatbots generally performed well on tasks related to spatial literacy, GIS theory, and interpretation of programming code and functions, but revealed weaknesses in mapping, code writing, and spatial reasoning. Furthermore, there was a significant difference in the correctness of results between the four chatbots. Responses from repeated tasks assigned to each chatbot showed a high level of consistency in responses with matching rates of over 80% for most task categories in the four chatbots.

Antigen specificity affects analysis of natural antibodies.

Natural antibodies are used to compare immune systems across taxa, to study wildlife disease ecology, and as selection markers in livestock breeding. These immunoglobulins are present prior to immune stimulation. They are described as having low antigen specificity or polyreactive binding and are measured by binding to self-antigens or novel exogenous proteins. Most studies use only one or two antigens to measure natural antibodies and ignore potential effects of antigen specificity in analyses. It remains unclear how different antigen-specific natural antibodies are related or how diversity among natural antibodies may affect analyses of these immunoglobulins. Using genetically distinct lines of chickens as a model system, we tested the hypotheses that (1) antigen-specific natural antibodies are independent of each other and (2) antigen specificity affects the comparison of natural antibodies among animals. We used blood cell agglutination and enzyme-linked immunosorbent assays to measure levels of natural antibodies binding to four antigens: (i) rabbit erythrocytes, (ii) keyhole limpet hemocyanin, (iii) phytohemagglutinin, or (iv) ovalbumin. We observed that levels of antigen specific natural antibodies were not correlated. There were significant differences in levels of natural antibodies among lines of chickens, indicating genetic variation for natural antibody production. However, line distinctions were not consistent among antigen specific natural antibodies. These data show that natural antibodies are a pool of relatively distinct immunoglobulins, and that antigen specificity may affect interpretation of natural antibody function and comparative immunology.

Dysfunction of the magnocellular subdivision of the visual thalamus in developmental dyslexia.

Developmental dyslexia (DD) is one of the most common learning disorders, affecting millions of children and adults worldwide. To date, scientific research has attempted to explain DD primarily based on pathophysiological alterations in the cerebral cortex. In contrast, several decades ago, pioneering research on five post-mortem human brains suggested that a core characteristic of DD might be morphological alterations in a specific subdivision of the visual thalamus - the magnocellular LGN (M-LGN). However, due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans, this finding was never confirmed in-vivo, and its relevance for DD pathology remained highly controversial. Here, we leveraged recent advances in high-resolution magnetic resonance imaging (MRI) at high field strength (7 Tesla) to investigate the M-LGN in DD in-vivo. Using a case-control design, we acquired data from a large sample of young adults with DD (n = 26; age 28 ± 7 years; 13 females) and matched control participants (n = 28; age 27 ± 6 years; 15 females). Each participant completed a comprehensive diagnostic behavioral test battery and participated in two MRI sessions, including three functional MRI experiments and one structural MRI acquisition. We measured blood-oxygen-level-dependent responses and longitudinal relaxation rates to compare both groups on LGN subdivision function and myelination. Based on previous research, we hypothesized that the M-LGN is altered in DD and that these alterations are associated with a key DD diagnostic score, i.e., rapid letter and number naming (RANln). The results showed aberrant responses of the M-LGN in DD compared to controls, which was reflected in a different functional lateralization of this subdivision between groups. These alterations were associated with RANln performance, specifically in male DD. We also found lateralization differences in the longitudinal relaxation rates of the M-LGN in DD relative to controls. Conversely, the other main subdivision of the LGN, the parvocellular LGN (P-LGN), showed comparable blood-oxygen-level-dependent responses and longitudinal relaxation rates between groups. The present study is the first to unequivocally show that M-LGN alterations are a hallmark of DD, affecting both the function and microstructure of this subdivision. It further provides a first functional interpretation of M-LGN alterations and a basis for a better understanding of sex-specific differences in DD with implications for prospective diagnostic and treatment strategies.

Development & Validation of the IADL Context-Based Cognitive Flexibility Assessment for Individuals With Schizophrenia With the Delphi Method

Abstract Date Presented 03/23/24 This study's outcome will yield the first cognitive flexibility assessment in instrumental activities of daily living (IADL) contexts worldwide, offering crucial insights into investigating IADL context-based cognitive flexibility and the diverse interpretations of executive functions. Primary Author and Speaker: Lin-Jye Huang Contributing Authors: Hui-Fen Mao, Yi-Hong Yang, Chien-Te Wu

Meibography signal intensity as a novel image biomarker for meibomian gland function: evidence from cross-sectional and longitudinal analyses.

Meibomian gland dysfunction (MGD), one of the most common ocular surface diseases, can induce dry eye and reduce patients' quality of life. Methodological limitations have resulted in contradictory interpretations of gland function. This study sought to investigate the correlation between meibography signal intensity (SI) and meibomian gland (MG) function and to validate an MGD classification strategy based on different levels of SI. A multicenter, cross-sectional analysis was conducted on 817 eyes from 361 patients with MGD and 52 healthy controls. Additionally, 78 eyes from 39 patients with MGD who had undergone LipiFlow treatment were recruited for longitudinal analyses. The SI value was obtained via meibography using an automated analyzer, and all participants underwent ocular surface examinations. A cross-sectional analysis was performed to determine SI distribution and its relationship to clinical characteristics via a generalized estimating equation model. Longitudinal analyses were conducted on the treatment cohort using a mixed-effects model to explore the outcome in different SI levels. Regression analysis revealed significant correlations between SI and lipid layer thickness (β=0.016), meibum expressibility (β=-0.676), meibum quality (β=-0.251), and fluorescein-stained tear-film break-up time (FBUT) (β=0.064) (all P values <0.001 for the above associations). Low-level SI MGD cases exhibited the most severe clinical signs, including the worst meibum expressibility (16% for level 3) and quality scores (19% for level 3), the shortest FBUT (3.82±0.13 s), and the thinnest lipid layer (65.68±2.58 nm), (all P values <0.05, respectively). Patients with medium SI showed the lowest ocular surface disease index (OSDI) value (26.64±1.06), the longest FBUT (4.56±0.08 s), and the thickest lipid layer (80.20±2.90 nm). After treatment, the high SI values reduced significantly at each follow-up point compared to baseline (all P values <0.05). The medium SI group demonstrated the greatest improvement in symptoms and signs, followed by the high SI group, and the low SI group. Automated measurements of SI can effectively reflect MG secretory activity. The proposed low, medium, and high SI classifications represent different functional subtypes of MGD.