Surface Function Research Articles

SMILE and ICL implantation on the ocular surface and meibomian glands in patients with postoperative myopia

BackgroundObjectively compare the changes in ocular surface parameters in myopic patients who have undergone either SMILE or Implantable Collamer Lens (ICL V4c) surgery.MethodsThis prospective cohort study enrolled 32 patients (32 eyes) undergoing SMILE surgery and 35 patients (35 eyes) receiving ICL V4c intraocular lenses. Examinations were performed at preoperative, 1-week, 1-month, and 3-month postoperative time points. The assessments included Schirmer’s I Test (SIt), First Non-Invasive Break-Up Time (First-NIBUT), Average Non-Invasive Break-Up Time (Average-NIBUT), Tear Meniscus Height (TMH), Ocular Surface Disease Index (OSDI) score, conjunctival congestion score, meibomian gland loss score, lipid layer analysis score, lid margin opening detection score, and corneal fluorescein staining (CFS) score.ResultsRepeated measures ANOVA revealed that SIT, TMH, and First-NIBUT initially decreased and then increased. At three months, SIT levels in the SMILE group were significantly lower than those in the ICL group. From the first month onward, TMH levels in the SMILE group remained significantly lower than those in the ICL group (P < 0.05). OSDI scores initially rose and then fell, with the SMILE group consistently showing higher OSDI levels than the ICL group. Conjunctival congestion scores in the SMILE group fluctuated less, while the ICL group exhibited a clear downward trend, with significant differences starting from the first week (P < 0.05). Over time, scores for meibomian gland loss, lipid layer analysis, and lid margin opening detection were all higher in the SMILE group compared to the ICL group.ConclusionsSMILE surgery has a more pronounced and prolonged impact on the ocular surface and meibomian gland function compared to ICL implantation. Objective dry eye parameters in the ICL group recover more quickly than those in the SMILE group one month post-surgery.

A response surface function method to characterize residual stress and cumulative plastic strain through indentation

Shot peening is a mechanical surface treatment used to improve the material fatigue performance by introducing compressive residual stress and work hardening. In order to characterize the residual stress (RS) and cumulative plastic strain (PS) of the treated surface simultaneously using the instrumented indentation technique, a Response Surface Function method is proposed in this work using a calibration by finite element simulations. The method is first verified numerically, and the results indicate that the determined values can accurately represent the input values, which shows the possibility of experimental application. The proposed method is then applied experimentally on a nickel-based alloy, Inconel 625. For validation, the solved profiles are compared with the profiles measured by X-ray diffraction (XRD). Although there is a difference between the profiles obtained by these two measurement techniques, the proposed method makes an advance on the characterization of residual stress and cumulative plastic strain simultaneously. It has demonstrated its feasibility, indicating its potential for practical application.

Mixed matrix membrane formation with porous metal–organic nanomaterials for CO2 capture and separation: A critical review

Due to the increasingly severe global warming trend, the reduction of CO2 emission and carbon capture have attracted growing interest. The separation of CO2 from gas mixtures, especially from point source and the atmosphere, is considered as one of the most important strategies for mitigating climate change. Porous metal–organic nanomaterials (PNMs), including metal-organic frameworks (MOFs) and metal-organic polyhedra (MOPs) have been extensively investigated in the fields of carbon capture, catalysts, sensors, biomedical imaging and gas storage. Their inherent pores, diverse surface function groups and potential modification possiblities make them competitive carbon capture materials. This review will introduce detailed scientific and technological advancements in PNMs and explain their fitness for carbon capture and separation, followed by the fabrication and application of mixed matrix membranes (MMMs) with PNMs. The current challenges appreared and solutions to improve the MMMs’ CO2 separation performance will also be stressed.

Nanosecond laser ablation of micro-pits in Ni60/WC coatings with coupled thermal-stress simulation and parameter optimization

In order to solve the problem of crack sprouting inside the micro-pit caused by improper design of laser ablation process parameters, this study establishes a thermal-stress coupling simulation model of nanosecond laser ablation of micro-pits on Ni60/WC coated surfaces. It researches the influence of laser process parameters (laser power, scanning speed, processing times) on the index parameters of micro-pit diameter, depth, and maximum residual stress. The weighting of diameter, depth and maximum residual stress sub-objectives in the comprehensive evaluation objectives of micro-pits are calculated. The response surface method of Central Composite Design (CCD) and regression analysis are used to fit the response surface functions of micro-pit diameter, depth, and maximum residual stress as a function of the variation of laser ablation process parameters. The Genetic Algorithm (GA) is used to minimize value of the maximum residual stress sub-objective function and to optimize the process parameters of laser ablation of micro-pits, using the values interval of the micro-pit diameter and depth functions as nonlinear constraints. The results show that the maximum residual stress varies quadratically with laser power and scanning speed, increases linearly with the processing times, and is coupled with the ablation process parameters. A design method for optimizing the parameters of nanosecond laser ablation by maximum residual stress is established, and the process parameters are optimized to meet the design parameters and quality requirements of micro-pits: laser power of 12 W, scanning speed of 276 mm/s, and 4 times of processing. The optimized process is provided for the parameter tuning nanosecond laser ablation of micro-pits on Ni60/WC coating surfaces.

Mixture proportion optimization and durability evaluation of rejuvenating composite seal

Composite seal (CS) is effective in restoring pavement surface function and prolonging the service life, employing a double-layer structure composed of micro-surfacing and chip seal. Rejuvenating composite seal (RCS) applying rejuvenating agents to the chip seal is dedicated to revitalizing the aged asphalt of the original pavement surface. However, limited studies have focused on enhancing interlayer and temperature performance through optimizing mixture ratios. In this study, a modified emulsified asphalt with self-developed rejuvenating agent was applied, and the orthogonal test considering interlayer shear resistance and high temperature stability was conducted for initially mixture proportion of RCS, then the preliminary ratio was optimized. The proportion for micro-surfacing was determined by mixing test, wet track abrasion test, and loaded wheel sticking sand test. Simultaneously, the recommended asphalt spraying amount of rejuvenating chip seal was determined by utilizing accelerated loading abrasion test. In view of the abrasion resistance, interlayer adhesion, and cracking resistance, the coupled effects of aggregate size and paving rate of rejuvenating chip seal were studied. Long-term wet track abrasion test, freeze-thaw cycle test, and UV aging test were conducted to evaluate the durability. The results show that the asphalt-aggregate ratio of micro-surfacing is 7.1 %. For 4.75 mm or 7 mm rejuvenating chip seal, the recommended asphalt spraying amount is 1.5 kg/m2 or 1.6 kg/m2, and the range of paving rate is 60 %–70 %. For 9.5 mm or 13.2 mm rejuvenating chip seal, the results are 1.7 kg/m2 or 1.8 kg/m2, and 80 %–90 %, respectively. Under the conditions, RCS has better interlayer shear resistance and temperature performance, which can improve durability.

Nanomaterials: breaking the bottleneck of breast cancer drug resistance.

Drug resistance poses a significant challenge in the treatment of breast cancer. In recent years, a variety of nanomaterials have been discovered and synthesized that can selectively target tumor cells and play a crucial role in the advancement of breast cancer therapies. As our understanding of tumor heterogeneity deepens, the emerging potential of nanomaterials in addressing drug resistance has garnered considerable attention. These materials not only selectively target tumor cells but also possess unique properties that make them promising options for cancer treatment, including low toxicity, excellent biocompatibility, ease of preparation, the ability to carry antitumor drugs, and customizable surface functions. In this review, we will comprehensively summarize two key developments in breast cancer treatment: the application of antitumor drugs and nanomaterials. We will explore the mechanisms by which nanomaterials improve drug resistance in breast cancer, targeted nanotherapy strategies to mitigate this resistance, and recent research advancements in anticancer nanomaterials. This overview aims to highlight the significant role of nanomaterials in breast cancer treatment and provide a theoretical framework for identifying optimal treatment strategies in the future.

Why Nature Evolved GPI-anchored proteins: Unique Structure Characteristics Enable Versatile Cell Surface Functions.

It remains a mystery why nature evolved the unique structural characteristics of GPI-anchored proteins (GPI-APs) and continues to sustain the complex, energy-intensive process of synthesizing these proteins. GPI-APs, despite their small size, rely on the coordinated activity of nearly 30 genes for their synthesis and remodeling, raising important evolutionary questions. The biological advantages of GPI-APs lie in their ability to rapidly redistribute across the cell membrane, localize within lipid rafts, utilize unique intracellular trafficking pathways, and function as both membrane-bound and soluble proteins. These properties allow GPI-APs to participate in diverse cellular processes such as synaptic plasticity, immune regulation, and signal transduction, highlighting their indispensable roles. Additionally, the shedding capability of GPI-APs extends their functional reach, adding further versatility to their biological roles. This review not only summarizes these key insights but also explores the broader implications of GPI-APs in cell signaling and disease. By understanding the evolutionary necessity of GPI-APs, we can better appreciate their complexity and potential as therapeutic targets.

USLC: Universal self‐learning control via physical performance policy‐optimization neural network

AbstractThis article proposes an online universal self‐learning control (USLC) algorithm based on a physical performance policy‐optimization neural network, which aims to solve the problem of universal self‐learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline‐trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law‐performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three‐dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low‐order circuit system and a high‐order variable‐span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed.

Efficiency of the TEMJOUHERT date palm kernel as activated carbon in wastewater treatment for the Ghardaia region

This study aims to determine the potential for using activated carbon prepared by local agricultural waste for the treatment of wastewater loaded with organic pollutants. The activated carbon was prepared by chemical activation using phosphoric acid H3PO4 at an “85%” concentration, from local date kernels TEMJOUHERT (TMG) from the Ghardaïa region, Algeria. The activated carbon synthesis yield is equal to 28.56%. The adsorbent prepared was characterized by determining its physical and chemical characteristics, in particular the bulk density, the ash content, the iodine number, the surface functions by the Boehm method and the pH at the point of zero charge. Thus the textural properties are determined by scanning electron microscopy SEM and the Burnauer-emmett-Teller (BET). The surface area and micropore volumes of activated carbon TMG; were found to be 639.9190 m2/g and 0.420779 cm3/g respectively. All the results obtained from the treatment of wastewater from the Atteuf "kef doukhane area in the Ghardaia region. are clear that: the best pollutant removal rates are 84.75% for PO3-4; 85.21% for COD and 74.55% for NH4+ by activated carbons; using a mass of 1g of adsorbent per 100 ml of water, 90 min contact time and 250rpm stirring speed.

Microfluidic Synthesis of Magnetic Nanoparticles for Biomedical Applications.

Magnetic nanoparticles have attracted great attention and become promising candidates in the biomedicine field due to their special physicochemical properties. They are generally divided into metallic and non-metallic magnetic nanoparticles, according to their compositions. Both of the two types have shown practical values in biomedicine applications, such as drug delivery, biosensing, bioimaging, and so on. Research efforts are devoted to the improvement of synthesis strategies to achieve magnetic nanoparticles with controllable morphology, diverse composition, active surface, or multiple functions. Taking high repeatability, programmable operation, precise fluid control, and simple device into account, the microfluidics system can expand the production scale and develop magnetic nanoparticles with desired features. This review will first describe different classifications of promising magnetic nanoparticles, followed by the advancements in microfluidic synthesis and the latest biomedical applications of these magnetic nanoparticles. In addition, the challenges and prospects of magnetic nanoparticles in the biomedical field are also discussed.

Surface Profile Recovery from Electromagnetic Fields with Physics-Informed Neural Networks

Physics-informed neural networks (PINN) have shown their potential in solving both direct and inverse problems of partial differential equations. In this paper, we introduce a PINN-based deep learning approach to reconstruct one-dimensional rough surfaces from field data illuminated by an electromagnetic incident wave. In the proposed algorithm, the rough surface is approximated by a neural network, with which the spatial derivatives of surface function can be obtained via automatic differentiation, and then the scattered field can be calculated using the method of moments. The neural network is trained by minimizing the loss between the calculated and the observed field data. Furthermore, the proposed method is an unsupervised approach, independent of any surface data, where only the field data are used. Both transverse electric (TE) field (Dirichlet boundary condition) and transverse magnetic (TM) field (Neumann boundary condition) are considered. Two types of field data are used here: full-scattered field data and phaseless total field data. The performance of the method is verified by testing with Gaussian-correlated random rough surfaces. Numerical results demonstrate that the PINN-based method can recover rough surfaces with great accuracy and is robust with respect to a wide range of problem regimes.

Optimized design of energy absorption of aluminum foam filled CFRP thin-walled square tube based on agent model

PurposeAluminum foam-filled thin-walled unit structures have received much attention for their excellent energy absorption properties. To improve the energy absorption effect of car energy absorption box under axial compression, this paper optimizes the fiber lay-up sequence, fiber angle and aluminum foam density of aluminum foam filled carbon fiber reinforced plastic (CFRP) thin-walled square tubes.Design/methodology/approachDesign of sample points required to construct the proxy model using design of experiments (DOE) method, and the data sample points of different models are obtained through Abaqus simulation and test. A double high-precision proxy model with the maximum specific energy absorption (SEA) and the minimum initial peak crash force (PCF) as the evaluation index is constructed based on the response surface function method. The NSGA-II multi-objective genetic algorithm was used to optimize the design parameters and obtain the optimal solution for the Pareto front, and the results were verified by using the multi-objective optimization toolbox in design-expert.FindingsThe results show that the optimal solution to the multi-objective optimization problem with the inclusion of the lay-up sequence is ρ = 0.5g/cm3 for a fiber lay-up angle varying in the range ±15–90° and an aluminum foam density varying in the range 0.2g/cm3-0.5g/cm3, with a lay-up method of [±87°/±16°/±15°/±89°]. The two optimization methods correspond to SEA and PCF errors of 2.109% and 4.1828%, respectively. The optimized SEA value is 18.2 J/g and the PCF value is 18,230 N. The optimized design reduces the peak impact force and increases the specific energy absorption, which improves the energy absorption effect of thin-walled energy-absorbing boxes for automobiles.Originality/valueIn this paper, the impact resistance of CFRP thin-walled square tubes filled with aluminum foam is optimized. Based on numerical simulations and experiments to obtain the sample point data for constructing the dual-agent model, we investigate the effect of filling with different densities of aluminum foam under the simultaneous change of fiber lay-up angle and order on its mechanical properties in this process, and carry out the multi-objective optimization design with NSGA-II algorithm.

Optimization of a novel lateral energy dissipation system for cable-stayed bridge with short piers based on seismic vulnerability analysis

Cable-stayed bridges with short piers are susceptible to becoming the structural weak link in seismic resistance during strong earthquakes. Traditional lateral restraint systems, such as fixed and sliding bearing systems, may impose substantial lateral forces and displacement demands, affecting the entire bridge's seismic safety. A butterfly-shaped steel plate damper has been developed and integrated into a lateral energy dissipation system along with elastoplastic cables to address this. Given the significant computational challenges and the complexity of identifying the optimal parameters for the energy dissipation system, this paper combines the probabilistic seismic demand model (PSDM) with the response surface method (RSM) to propose the seismic vulnerability fitting optimization method (SVFOM). Based on SVFOM, utilizing a central composite design (CCD), 17 analysis scenarios that account for the parameter variations of the lateral energy dissipation system are established. By developing response surface functions for bearing damage (ys) and pier damage (yp) through the seismic response fitting of the structure across all scenarios, we determined the optimal parameters for the lateral energy dissipation system to minimize damage to both bearings and piers. The finite element model validation analysis, which demonstrated significant reductions in pier displacement and the probability of bearing damage, confirms the effectiveness of the SVFOM. Optimized parameters showed that the new lateral energy dissipation system can significantly enhance the lateral seismic performance of cable-stayed bridges with short piers. Compared to scenarios without energy dissipation and with lateral sliding bearings, they demonstrated that this optimized system reduces the relative displacement of the piers and girders by 56 % and the probability of complete bearing failure by 87 % while maintaining pier damage within acceptable limits. This optimized system offers a valuable reference for the seismic design of similar bridge structures.

Modulating the intrinsic fluorescence properties of amoxicillin by sulfur nanodot-based nanoreactor for its direct fluorescence detection

Direct fluorescence detection of amoxicillin (AMX) is highly desired for food and environment safety but remains a formidable challenge because of its low conjugation degree and stabilization effects on excitons. In this work, we proposed a direct fluorometry for AMX through modulating its fluorescence properties by utilizing sulfur nanodots (S-dots) as nanoreactors. S-dots provided both confined carrier and reactants (polyethylene glycol (PEG) and sulfite ions) to modulate the fluorescence properties of AMX, achieving a 277-fold increase on the fluorescence intensities. PEG and SO32- interacted with AMX through both covalent and hydrogen bounds, which stabilized the excited states, decreased the chances of nonradiative path and activated the radiative decay. The ultrasmall sized S-dots also provided confinement carries for promoting the reactivity between AMX and surface function groups. Quantitative detection of AMX in commercial milk samples was realized through direct recording the fluorescence signals, achieving a limit of detection of 159 nM. The reported results provided a new idea to design fluorescence assays for extremely weak emissive molecules, showing great potential in food safety analysis, environmental and healthy monitoring.

MISNeR: Medical Implicit Shape Neural Representation for Image Volume Visualisation

AbstractThree‐dimensional visualisation of mesh reconstruction of the medical images is commonly used for various clinical applications including pre / post‐surgical planning. Such meshes are conventionally generated by extracting the surface from volumetric segmentation masks. Therefore, they have inherent limitations of staircase artefacts due to their anisotropic voxel dimensions. The time‐consuming process for manual refinement to remove artefacts and/or the isolated regions further adds to these limitations. Methods for directly generating meshes from volumetric data by template deformation are often limited to simple topological structures, and methods that use implicit functions for continuous surfaces, do not achieve the level of mesh reconstruction accuracy when compared to segmentation‐based methods. In this study, we address these limitations by combining the implicit function representation with a multi‐level deep learning architecture. We introduce a novel multi‐level local feature sampling component which leverages the spatial features for the implicit function regression to enhance the segmentation result. We further introduce a shape boundary estimator that accelerates the explicit mesh reconstruction by minimising the number of the signed distance queries during model inference. The result is a multi‐level deep learning network that directly regresses the implicit function from medical image volumes to a continuous surface model, which can be used for mesh reconstruction from arbitrary high volume resolution to minimise staircase artefacts. We evaluated our method using pelvic computed tomography (CT) dataset from two public sources with varying z‐axis resolutions. We show that our method minimised the staircase artefacts while achieving comparable results in surface accuracy when compared to the state‐of‐the‐art segmentation algorithms. Furthermore, our method was 9 times faster in volume reconstruction than comparable implicit shape representation networks.

Experimental Investigation on the Effect of Masked Multiple Passes Abrasive Waterjet Machining Surface Texturing on Coefficient of Friction

The surface texturing method is popular because of its ability to improve surface properties, specifically surface functioning, and friction control. It is a common technique used on material surfaces to improve friction performance. Laser surface texturing is the popular choice; however, this method suffers drawbacks such as cracking, heat-affected zone, and generation of hazardous fumes. The Abrasive Water Jet Machining (AWJM) was chosen is that, because the eroded material is carried by the waterjet, the process is clean and does not produce dust, chips, or chemical contaminants. This experiment examines the influence of AWJM and investigates how crater density and a multiple-pass machining path affect the stainless-steel friction characteristics when sliding in dry conditions. The texture was applied on the surface of stainless steel by masked multi-passes AWJM technique. According to the findings, there is less friction in craters with greater densities because there are more craters in the contact zone, which improves the trapping of debris particles and reduces friction, when the crater density rises from 4% to 18%, there is a greater chance of worn debris becoming trapped, which reduces friction. The coefficient of friction and crater roughness of the stainless-steel surface are all proportionally affected by the multiple-passes approach. The coefficient of friction increases with higher machining passes. There is more erosion and greater roughness when there are more jet passes. It is concluded that the masked multiple passes AWJM Surface Texturing technique has a significant impact on the Coefficient of Friction (COF).

Effect of oxygen or copper impurities on the work function of cesiated surfaces under co-existence with hydrogen: A study about negative hydrogen ion sources for neutral beam injection systems

This study investigates the effect of oxygen and copper impurities on the work function of the Cs/H/Mo (001) surface based on the Density Functional Theory (DFT) method. First, the deposition of 2/16 θ impurities on the Cs0.5/H0.5/Mo (001) surface (the subscript 0.5 represents 8/16 θ coverage) is studied. It is found that the deposition of impurities beneath the cesium layer and oxygen impurities as atoms, and in a high-symmetry configuration, both lower the surface system energy. As the impurity coverage increases, it is observed that impurity deposition with a coverage of less than or equal to 8/16 θ effectively reduces the work function of the Cs0.5/H0.5/Mo (001) surface. However, as the cesium and hydrogen coverage increases, the impurities are no longer effective at reducing the work function. Moreover, when the cesium coverage reaches 16/16 θ, the surface work function approaches that of a cesium metal surface. Another important discovery is that when oxygen impurities are deposited above the cesium layer on the Cs0.5/H0.5/Mo (001) surface, a reduction in the surface work function is observed. This anomalous phenomenon is attributed to significant charge polarization caused by the deposition of oxygen impurities. The migration barriers of impurities traversing the dense cesium layer to the Mo (001) substrate are evaluated using the climbing image nudged elastic band (CI-NEB) method. Oxygen impurities tend to traverse the cesium layer in molecular form, facing a minimum barrier of 0.52 eV, while copper atoms encounter a lower barrier of 0.09 eV when traversing the cesium layer.

Research on multibeam line laying scheme based on geometric analysis and optimization modeling

The multibeam full-coverage bathymetry technique has greatly improved the accuracy and efficiency of marine mapping and has been widely used in many fields. In order to measure the seafloor topography in the sea area with known topographic features, this paper designs the optimal survey line arrangement scheme. It is finally concluded that the fitting surface function of the sea depth is first fitted using the least squares method, after which the gradient of the sea depth data at each point is calculated, and the hierarchical clustering method is used to determine the optimal clusters for clustering. The surface formed by the set of points with similar gradients is approximated as a flat slope with a fixed slope angle, and based on the optimization model constructed, survey lines are laid along the isobath direction for each approximated slope after clustering.

Synthesis, characterisation and functionalization of Hercynite nanoparticles for improved antibacterial activity

Hercynite i.e.,FeAl2O4 is an earth-abundant spinel mineral with a cubic crystal structure and belongs to the normal spinel ferrites possessing optical absorption in the visible range as well as superior magnetic and thermal properties. Herein, we synthesized nanosized FeAl2O4 where the citric acid-mediated sol–gel auto-combustion method was employed to achieve its pure phase and studied its physicochemical properties. Furthermore, the superior colloidal dispersion stability of the FeAl2O4 nanoparticles was achieved required for antibacterial activity and standardised via post-synthesis surface functionalisation using amino-propyl-triethoxysilane (APTES). We further characterised the material using states of art characterisation techniques for their structural, morphological, optical and thermal properties. Finally, the antibacterial activity of pure and surface functionalised FeAl2O4 nanoparticles was investigated against the Escherichia coli (E. coli) strain. We observed good penetration of surface functionalised FeAl2O4 nanoparticles into the bacterial membranes due to the high degree of dispersion achieved via cationic surface charge. Conclusively, a key finding of this study is the enhanced antibacterial properties of surface functionalised FeAl2O4 nanoparticles for the concentration of 31 µg/mL compared to pure FeAl2O4 nanoparticles at 62 µg/mL. This study has great relevance in the area of wound healing and tissue regeneration in the future.

Effects of Glaucoma Treatment on Ocular Surface and Tear Functions: Comparison of Trabeculectomy and Antiglaucoma Drops.

To investigate tear function and ocular surface disease (OSD) findings in patients with glaucoma who received antiglaucoma medication in one eye and trabeculectomy surgery in the other eye. The patient group included 38 eyes of 19 patients who had undergone trabeculectomy surgery with mitomycin C (MMC) treatment in one eye at least 6 months prior. These eyes were followed up without medication while the fellow eye continued receiving antiglaucomatous medication. The control group comprised 20 eyes of 20 healthy individuals. Demographic data, follow-up period after trabeculectomy, antiglaucoma medications, number of drops per day, and duration of medication were recorded. Tear break-up time (BUT), corneal and conjunctival fluorescein staining, Schirmer II test, and conjunctival impression cytology were performed. A statistically significant difference was observed in BUT, corneal and conjunctival fluorescein staining, Schirmer II test, and Nelson staging levels in both eyes of patients with glaucoma compared to the control group (p=0.05). Although not statistically significant, BUT, Schirmer II test, punctate staining, and Nelson staging results showed improvement with increasing postoperative time. In our patient group, antiglaucoma medications and trabeculectomy surgery with MMC induced OSD to a similar degree. No superiority was observed between trabeculectomy with MMC and antiglaucoma drops in terms of OSD incidence.