Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning.

The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.

Learned features of antibody-antigen binding affinity.

Defining predictors of antigen-binding affinity of antibodies is valuable for engineering therapeutic antibodies with high binding affinity to their targets. However, this task is challenging owing to the huge diversity in the conformations of the complementarity determining regions of antibodies and the mode of engagement between antibody and antigen. In this study, we used the structural antibody database (SAbDab) to identify features that can discriminate high- and low-binding affinity across a 5-log scale. First, we abstracted features based on previously learned representations of protein-protein interactions to derive 'complex' feature sets, which include energetic, statistical, network-based, and machine-learned features. Second, we contrasted these complex feature sets with additional 'simple' feature sets based on counts of contacts between antibody and antigen. By investigating the predictive potential of 700 features contained in the eight complex and simple feature sets, we observed that simple feature sets perform comparably to complex feature sets in classification of binding affinity. Moreover, combining features from all eight feature-sets provided the best classification performance (median cross-validation AUROC and F1-score of 0.72). Of note, classification performance is substantially improved when several sources of data leakage (e.g., homologous antibodies) are not removed from the dataset, emphasizing a potential pitfall in this task. We additionally observe a classification performance plateau across diverse featurization approaches, highlighting the need for additional affinity-labeled antibody-antigen structural data. The findings from our present study set the stage for future studies aimed at multiple-log enhancement of antibody affinity through feature-guided engineering.

Procedural Outcomes of Pulmonary Atresia With Intact Ventricular Septum in Neonates: A Multicenter Study.

BACKGROUND: Multicenter contemporary data describing short-term outcomes after initial interventions of neonates with pulmonary atresia with intact ventricular septum (PA-IVS) are limited. This multicenter study describes characteristics and outcomes of PA-IVS neonates after their initial catheter or surgical intervention and identifies factors associated with major adverse cardiac events (MACE). METHODS: Neonates with PA-IVS who underwent surgical or catheter intervention between 2009 and 2019 in 19 centers were reviewed. Risk factors for MACE, defined as cardiopulmonary resuscitation, mechanical circulatory support, stroke, or in-hospital mortality, were analyzed using multivariable logistic regression models. RESULTS: We reviewed 279 neonates: 79 (28%) underwent right ventricular decompression, 151 (54%) underwent systemic-to-pulmonary shunt or ductal stent placement only, 36 (13%) underwent right ventricular decompression with shunt or ductal stent placement, and 11 (4%) underwent transplantation. MACE occurred in 57 patients (20%): 26 (9%) received mechanical circulatory support, 37 (13%) received cardiopulmonary resuscitation, stroke occurred in 16 (6%), and 23 (8%) died. The presence of 2 major coronary artery stenoses (adjusted odds ratio, 4.99; 95% CI, 1.16-21.39) and lower weight at first intervention (adjusted odds ratio, 1.52; 95% CI, 1.01-2.27) were significantly associated with MACE. Coronary ischemia was the most frequent presumed mechanism of death (n = 10). CONCLUSIONS: In a multicenter cohort, 1 in 5 neonates with PA-IVS experienced MACE after their initial intervention. Patients with 2 major coronary artery stenoses or lower weight at the time of the initial procedure were most likely to experience MACE and warrant vigilance during preintervention planning and postintervention management.

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies.

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations.

Pulmonary Atresia with Intact Ventricular Septum: Midterm Outcomes from a Multicenter Cohort.

Contemporary multicenter data regarding midterm outcomes for neonates with pulmonary atresia with intact ventricular septum are lacking. We sought to describe outcomes in a contemporary multicenter cohort, determine factors associated with end-states, and evaluate the effect of right ventricular coronary dependency and coronary atresia on transplant-free survival. Neonates treated during 2009-2019 in 19 United States centers were reviewed. Competing risks analysis was performed to determine cumulative risk of each end-state, and multivariable regression analyses were performed to identify factors associated with each end-state and transplant-free survival. We reviewed 295 patients. Median tricuspid valve Z-score was - 3.06 (25%, 75%: - 4.00, - 1.52). Final end-state was biventricular repair for 45 patients (15.2%), one-and-a half ventricle for 16 (5.4%), Fontan for 75 (25.4%), cardiac transplantation for 29 (9.8%), and death for 54 (18.3%). Seventy-six patients (25.7%) remained in mixed circulation. Cumulative risk estimate of death was 10.9%, 16.1%, 16.9%, and 18.8% at 1, 6 months, 1 year, and 5 years, respectively. Tricuspid valve Z-score was inversely, and coronary atresia positively associated with death or transplantation [odds ratio (OR) = 0.46, (95% confidence interval (CI) = 0.29-0.75, p < 0.001) and OR = 3.75 (95% CI 1.46-9.61, p = 0.011), respectively]. Right ventricular coronary dependency and left coronary atresia had a significant effect on transplant-free survival (log-rank p < 0.001). In a contemporary multicenter cohort of patients with PAIVS, consisting predominantly of patients with moderate-to-severe right ventricular hypoplasia, we observed favorable survival outcomes. Right ventricular coronary dependency and left, but not right, coronary atresia significantly worsens transplant-free survival.

A structural dynamic explanation for observed escape of SARS-CoV-2 BA.2 variant mutation S371L/F.

The SARS-CoV-2 Omicron sub-variants BA.1 and BA.2 have become the dominant variants worldwide due to enhanced transmissibility and immune evasion. In response to the rise of BA.1 and BA.2, two recent studies by Liu et al. and Iketani et al. provide a detailed analysis of loss of therapeutic antibody potency through evaluation of escape by pseudotyped viruses harboring BA.1 and BA.2 receptor binding domain (RBD) point mutations. Surprisingly, Liu et al. and Iketani et al. observed a profoundly broad escape effect for the individual mutations S371L and S371F. This result cannot be explained by known escape mechanisms of the SARS-CoV-2 RBD, and conflicts with existing computational and experimental escape measurements for S371 mutations performed on monomeric RBD. Through an examination of these conflicting datasets and a structural analysis of the antibodies assayed by Liu et al. and Iketani et al., we propose a mechanism to explain S371L/F escape according to a perturbation of spike trimer conformational dynamics that has not yet been described for any SARS-CoV-2 escape mutation. The proposed mechanism is relevant to Omicron and future variant surveillance as well as therapeutic antibody design.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain.

The Omicron variant features enhanced transmissibility and antibody escape. Here, we describe the Omicron receptor-binding domain (RBD) mutational landscape using amino acid interaction (AAI) networks, which are well suited for interrogating constellations of mutations that function in an epistatic manner. Using AAI, we map Omicron mutations directly and indirectly driving increased escape breadth and depth in class 1-4 antibody epitopes. Further, we present epitope networks for authorized therapeutic antibodies and assess perturbations to each antibody's epitope. Since our initial modeling following the identification of Omicron, these predictions have been realized by experimental findings of Omicron neutralization escape from therapeutic antibodies ADG20, AZD8895, and AZD1061. Importantly, the AAI predicted escape resulting from indirect epitope perturbations was not captured by previous sequence or point mutation analyses. Finally, for several Omicron RBD mutations, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBDdown-RBDdown interface.

Conserved topology of virus glycoepitopes presents novel targets for repurposing HIV antibody 2G12.

Complex glycans decorate viral surface proteins and play a critical role in virus-host interactions. Viral surface glycans shield vulnerable protein epitopes from host immunity yet can also present distinct "glycoepitopes" that can be targeted by host antibodies such as the potent anti-HIV antibody 2G12 that binds high-mannose glycans on gp120. Two recent publications demonstrate 2G12 binding to high mannose glycans on SARS-CoV-2 and select Influenza A (Flu) H3N2 viruses. Previously, our lab observed 2G12 binding and functional inhibition of a range of Flu viruses that include H3N2 and H1N1 lineages. In this manuscript, we present these data alongside structural analyses to offer an expanded picture of 2G12-Flu interactions. Further, based on the remarkable breadth of 2G12 N-glycan recognition and the structural factors promoting glycoprotein oligomannosylation, we hypothesize that 2G12 glycoepitopes can be defined from protein structure alone according to N-glycan site topology. We develop a model describing 2G12 glycoepitopes based on N-glycan site topology, and apply the model to identify viruses within the Protein Data Bank presenting putative 2G12 glycoepitopes for 2G12 repurposing toward analytical, diagnostic, and therapeutic applications.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant.

The SARS-COV2 Omicron variant has sparked global concern due to the possibility of enhanced transmissibility and escape from vaccines and therapeutics. In this study, we describe the mutational landscape of the Omicron variant using amino acid interaction (AAI) networks. AAI network analysis is particularly well suited for interrogating the impact of constellations of mutations as occur on Omicron that may function in an epistatic manner. Our analyses suggest that as compared to previous variants of concern, the Omicron variant has increased antibody escape breadth due to mutations in class 3 and 4 antibody epitopes as well as increased escape depth due to accumulated mutations in class 1 antibody epitopes. We note certain RBD mutations that might further enhance Omicron's escape, and in particular advise careful surveillance of two subclades bearing R346S/K mutations with relevance for certain therapeutic antibodies. Further, AAI network analysis suggests that the function of certain therapeutic monoclonal antibodies may be disrupted by Omicron mutations as a result of the cumulative indirect perturbations to the epitope surface properties, despite point-mutation analyses suggesting these antibodies are tolerant of the set of Omicron mutations in isolation. Finally, for several Omicron mutations that do not appear to contribute meaningfully to antibody escape, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBD-RBD interface.

An Antigenic Space Framework for Understanding Antibody Escape of SARS-CoV-2 Variants.

The evolution of mutations in SARS-CoV-2 at antigenic sites that impact neutralizing antibody responses in humans poses a risk to immunity developed through vaccination and natural infection. The highly successful RNA-based vaccines have enabled rapid vaccine updates that incorporate mutations from current variants of concern (VOCs). It is therefore important to anticipate future antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this goal, we survey epitope-paratope interfaces of anti-SARS-CoV-2 antibodies to map an antigenic space that captures the role of each spike protein residue within the polyclonal antibody response directed against the ACE2-receptor binding domain (RBD) or the N-terminal domain (NTD). In particular, the antigenic space map builds on recently published epitope definitions by annotating epitope overlap and orthogonality at the residue level. We employ the antigenic space map as a framework to understand how mutations on nine major variants contribute to each variant's evasion of neutralizing antibodies. Further, we identify constellations of mutations that span the orthogonal epitope regions of the RBD and NTD on the variants with the greatest antibody escape. Finally, we apply the antigenic space map to predict which regions of antigenic space-should they mutate-may be most likely to complementarily augment antibody evasion for the most evasive and transmissible VOCs.

Leaf water potential of field crops estimated using NDVI in ground-based remote sensing-opportunities to increase prediction precision.

Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.

Glycans in Virus-Host Interactions: A Structural Perspective.

Many interactions between microbes and their hosts are driven or influenced by glycans, whose heterogeneous and difficult to characterize structures have led to an underappreciation of their role in these interactions compared to protein-based interactions. Glycans decorate microbe glycoproteins to enhance attachment and fusion to host cells, provide stability, and evade the host immune system. Yet, the host immune system may also target these glycans as glycoepitopes. In this review, we provide a structural perspective on the role of glycans in host-microbe interactions, focusing primarily on viral glycoproteins and their interactions with host adaptive immunity. In particular, we discuss a class of topological glycoepitopes and their interactions with topological mAbs, using the anti-HIV mAb 2G12 as the archetypical example. We further offer our view that structure-based glycan targeting strategies are ready for application to viruses beyond HIV, and present our perspective on future development in this area.

Mapping Potential Antigenic Drift Sites (PADS) on SARS-CoV-2 Spike in Continuous Epitope-Paratope Space.

SARS-CoV-2 mutations with antigenic effects pose a risk to immunity developed through vaccination and natural infection. While vaccine updates for current variants of concern (VOCs) are underway, it is likewise important to prepare for further antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this end, a wealth of data and tools exist that can augment existing genetic surveillance of VOC evolution. In this study, we integrate published datasets describing genetic, structural, and functional constraints on mutation along with computational analyses of antibody-spike co-crystal structures to identify a set of potential antigenic drift sites (PADS) within the receptor binding domain (RBD) and N-terminal domain (NTD) of SARS-CoV-2 spike protein. Further, we project the PADS set into a continuous epitope-paratope space to facilitate interpretation of the degree to which newly observed mutations might be antigenically synergistic with existing VOC mutations, and this representation suggests that functionally convergent and synergistic antigenic mutations are accruing across VOC NTDs. The PADS set and synergy visualization serve as a reference as new mutations are detected on VOCs, enable proactive investigation of potentially synergistic mutations, and offer guidance to antibody and vaccine design efforts.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics.

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Enhanced Photodetection in Glancing Angle Deposited One-Dimensional In2O3 Nanorod Array.

Glancing angle deposition (GLAD) oriented electron beam (e-beam) evaporation process has been employed to develop 1D In2O3 nanorod array over n-Si substrate. The morphology of as-deposited In2O3 thin film (∼70 nm) and GLAD 1D In2O3 nanorod array (∼400 nm) were explored using field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS) and high resolution transmission electron microscopy (HRTEM) analysis. The structural analysis were perceived by high-resolution X-ray diffraction (HRXRD) and atomic force microscopy (AFM) techniques. The clampdown of ∼4.4 fold photoluminescence (PL) emission intensity was observed for In2O3 nanorod array. Metallization were done to measure the current (I)-voltage (V) characteristics for n-Si/In2O3 thin film and n-Si/In2O3 nanorod devices. The In2O3 nanorod device displayed ∼2.2 fold enhancement in current conduction at -4.6 V and an averagely ∼1.1 fold augmentation in photosensitivity were also observed. The photoresponsivity of ∼28 µA/W, maximum specific detectivity of ∼9.9×107 Jones and low NEP of ∼4.5×10-12 W/√Hz was achieved for the In2O3 nanorod-based photodetectors. The maximum ∼2.5 fold high detectivity and ∼2.4 fold low noise equivalent power (NEP) were perceived for the 1D In2O3 nanorod array detector as compared to the bare In2O3 thin film detector.

Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References.

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Bandgap Modulation of Glancing Angle Deposition Aided Ag Nanoparticles Covered TiO2 Thin Film by High Temperature Annealing.

Glancing Angle Deposition (GLAD) technique has been used to fabricate the Ag nanoparticles (NPs) over TiO2 thin film (TF) on the n-Si substrate. The deposited Ag NPs are in the size of 3-5 nm. Open-air annealing has been done at 500 °C and 600 °C for the n-Si/TiO2 TF/Ag NP samples. High Resolution X-ray Diffraction (HRXRD) peaks were identified to calculate the crystalline size of the NPs and rutile phase of the annealed sample were exhibited. Morphological analysis has been done for the sample using Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive Spectroscopy (EDS) and Atomic Force Microscopy (AFM). The enhancement of plasmonic absorption and modulation in the bandgap for the annealed Ag NPs surrounded TiO2 TF has been verified by UV-Vis Spectroscopy and the bandgap has been calculated using Tauc plot. An overall 2.5 fold and 3 fold enhancement has been observed in the UV region and visible region for n-Si/TiO2 TF/Ag NP annealed at 500 °C and 600 °C samples as compared to the n-Si/TiO2 TF/Ag NP as-deposited samples. The modulation of bandgap due to the sub-band transition and Localized Surface Plasmon Resonance (LSPR) effect of Ag NPs and relevant sub-band transition due to change in annealing temperature has been reported.

In-depth structural characterization of pentosan polysulfate sodium complex drug using orthogonal analytical tools.

Rapid advances have been made in developing analytical technologies for characterization of highly heterogeneous active ingredients of complex drugs, such as pentosan polysulfate (PPS), active ingredient of the drug Elmiron®, approved by the Food and Drug Administration and marketed in the United States to treat interstitial cystitis. PPS sulfated polysaccharides comprise of a repeat unit of ß(1-4)-D-xylopyranoses randomly substituted by 4-O-methyl-glucopyranosyluronic acid. To define the critical quality attributes (CQAs) of such a complex drug, it is critical to develop an approach that integrates data from orthogonal analytical methodologies. Here, we developed an approach integrating diverse analytical tools including gel permeation chromatography, LC/ESI-MS and NMR to measure CQAs of PPS. The proposed mathematical framework integrates the data from these diverse analytical methods as function of PPS chain length and building blocks. Our approach would facilitate in establishing a scientific foundation for comparative characterization of drug products with complex active ingredients.

Baseline data of facial parameters in the population of Haryana: An anthropometric study.

CONTEXT: Anthropometry plays an important role in the assessment of ethnicity and identification of an individual. There is paucity of literature on various facial parameters in Haryanvi population. Thus, the present study was an initiation to collect this database in Haryanvi population. AIM: The aim of the present study was to create a database of craniofacial parameters of Haryanvi population. MATERIALS AND METHODS: The study was conducted on 300 individuals of Haryanvi ethnicity. A digital vernier caliper was used for the measurement of facial parameters. STATISTICAL ANALYSIS: Chi-square test, t-test, and Pearson's correlation test were used for finding the difference between the measurements for various parameters. RESULTS: In the present study, mesoprosopic was the predominant facial phenotype in both males and females. A significant sexual dimorphism was found between all the facial parameters measured in the study. However, upper facial height and facial index did not follow the same pattern in relation to gender determination. CONCLUSION: Based on the present study findings, we conclude that craniofacial parameters could be used as an important tool to assess the ethnicity and gender of an individual. In addition, our data could be used as a baseline for further studies in the identification of a Haryanvi individual.

The Diagnostic Role of Methyl Green-Pyronin Y Staining in Oral Leukoplakia and Oral Squamous Cell Carcinoma: An Exfoliative Cytology-Based Cytomorphometric Analysis.

BACKGROUND: Oral exfoliative cytology is a noninvasive and nonpainful technique for early diagnosis of oral potentially malignant disorders and oral cancer, and the use of cytomorphometry ameliorates its diagnostic reliability. The objective of the present study was to analyze methyl green-pyronin Y (MGP)-stained oral exfoliated cells (OECs) of oral leukoplakia (OL) and oral squamous cell carcinoma (OSCC) by cytomorphometry. MATERIALS AND METHOD: An observational study was conducted on 150 individuals equally divided into three groups: normal mucosa, OL, and OSCC. Smears were prepared from OECs and stained with MGP. Cytomorphometry was done for 100 cells per subject, and various cell and nuclear parameters were measured and calculated. RESULTS: The Kruskal-Wallis test with post hoc correlation showed significant differences in nucleus and cell diameter (ND, CD), nucleus and cell area (NA, CA), nucleus and cell perimeter (NP, CP), and nucleus to cytoplasmic (N:C) ratio for diameter, perimeter, and area. Spearman's ρ correlation of various N:C ratio methods showed good correlation between N:C perimeter and diameter ratio, N:C diameter and ellipse ratio, and N:C area and ellipse ratio. Additional morphological factors showed significant relations for both cell and nuclear regularity factor, shape factor, and nuclear contour index. DISCUSSION: MGP-based cytomorphometry showed a significant decrease in CD, CA, and CP and increase in ND, NA, NP, and N:C ratio from normal mucosa to OL and OSCC. MGP proved its worth as an effective stain for OECs, despite its strict standardization.