Digital histological staining of tissue slide images from optical coherence microscopy.

The convergence of staining-free optical imaging and digital staining technologies has become a central focus in digital pathology, presenting significant advantages in streamlining specimen preparation and expediting the rapid acquisition of histopathological information. Despite the inherent merits of optical coherence microscopy (OCM) as a staining-free technique, its widespread application in observing histopathological slides has been constrained. This study introduces a novel approach by combining wide-field OCM with digital staining technology for the imaging of histopathological slides. Through the optimization of the histology slide production process satisfying the ground growth for digital staining as well as pronounced contrast for OCM imaging, successful imaging of various mouse tissues was achieved. Comparative analyses with conventional staining-based bright field images were executed to evaluate the proposed methodology's efficacy. Moreover, the study investigates the generalization of digital staining color appearance to ensure consistent histopathology, considering tissue-specific and thickness-dependent variations.

Implementation of a portable diffraction phase microscope for digital histopathology.

Quantitative phase imaging (QPI) has a significant advantage in histopathology as it helps in differentiating biological tissue structures and cells without the need for staining. To make this capability more accessible, it is crucial to develop compact and portable systems. In this study, we introduce a portable diffraction phase microscopy (DPM) system that allows the acquisition of phase map images from various organs in mice using a low-NA objective lens. Our findings indicate that the cell and tissue structures observed in portable DPM images are similar to those seen in conventional histology microscope images. We confirmed that the developed system's performance is comparable to the benchtop DPM system. Additionally, we investigate the potential utility of digital histopathology by applying deep learning technology to create virtual staining of DPM images.

Regulated Self-Folding in Multi-Layered Hydrogels Considered with an Interfacial Layer.

Multi-layered hydrogels consisting of bi- or tri-layers with different swelling ratios are designed to soft hydrogel actuators by self-folding. The successful use of multi-layered hydrogels in this application greatly relies on the precise design and fabrication of the curvature of self-folding. In general, however, the self-folding often results in an undesired mismatch with the expecting value. To address this issue, this study introduces an interfacial layer formed between each layered hydrogel, and this layer is evaluated to enhance the design and fabrication precision. By considering the interfacial layer, which forms through diffusion, as an additional layer in the multi-layered hydrogel, the degree of mismatch in the self-folding is significantly reduced. Experimental results show that as the thickness of the interfacial layer increases, the multi-layered hydrogel exhibits a 3.5-fold increase in its radius of curvature during the self-folding. In addition, the diffusion layer is crucial for creating robust systems by preventing the separation of layers in the muti-layered hydrogel during actuation, thereby ensuring the integrity of the system in operation. This new strategy for designing multi-layered hydrogels including an interfacial layer would greatly serve to fabricate precise and robust soft hydrogel actuators.

A Spike-like Self-Assembly of Polyaspartamide Integrated with Functionalized Nanoparticles.

The integration of nanoparticles (NPs) into molecular self-assemblies has been extensively studied with the aim of building well-defined, ordered structures which exhibit advanced properties and performances. This study demonstrates a novel strategy for the preparation of a spike-like self-assembly designed to enhance UV blocking. Poly(2-hydroxyethyl aspartamide) (PHEA) substituted with octadecyl chains and menthyl anthranilate (C18-M-PHEA) was successfully synthesized by varying the number of grafted groups to control their morphology and UV absorption. The in situ incorporation of polymerized rod-like TiO2 within the C18-M-PHEA self-aggregates generated spike-like self-assemblies (TiO2@C18-M-PHEA) with a chestnut burr structure in aqueous solution. The results showed that the spike-like self-assemblies integrated with TiO2 NPs exhibited a nine-fold increase in UV protection by simultaneous UV absorption and scattering compared with the pure TiO2 NPs formed via a bulk mixing process. This work provides a novel method for UV protection using self-assembling poly(amino acid)s derivatives integrated with functional nanoparticles to tune their morphology and organization.

A novel optical coherence tomography-based in vitro method of anti-aging skin analysis using 3D skin wrinkle mimics.

BACKGROUND: Wrinkles represent a characteristic symptom of skin aging. In recent years, various studies have focused on their prevention and/or cure. However, clinical tests are still the only method available to directly detect and evaluate the anti-wrinkle efficacy of various substances. Moreover, no in vitro strategy for such anti-aging skin analysis has been reported. Therefore, in this study, we aimed to develop a novel technology to overcome these limitations. MATERIALS AND METHODS: Full-thickness (FT) skin wrinkle mimics with various widths and depths were fabricated using a collagen stamping method. These were analyzed and compared using 2D and 3D Swept Source-Optical Coherence Tomography (SS-OCT) imaging technologies. RESULTS: SS-OCT demonstrated superficial and cross-sectional images of the wrinkle mimics, and the size of the wrinkles was validated using image analysis. Retinoic acid treatment significantly decreased both the depth and width of wrinkles formed in the FT skin wrinkle mimics. CONCLUSIONS: Using 3D tissue engineering and SS-OCT imaging technologies, we developed a novel in vitro technique that can directly detect skin wrinkles. This significantly efficient method could lead to an alternative strategy for animal experiments and preclinical anti-aging research on the skin.

Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining.

Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.

Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography.

Since glaucoma is a progressive and irreversible optic neuropathy, accurate screening and/or early diagnosis is critical in preventing permanent vision loss. Recently, optical coherence tomography (OCT) has become an accurate diagnostic tool to observe and extract the thickness of the retinal nerve fiber layer (RNFL), which closely reflects the nerve damage caused by glaucoma. However, OCT is less accessible than fundus photography due to higher cost and expertise required for operation. Though widely used, fundus photography is effective for early glaucoma detection only when used by experts with extensive training. Here, we introduce a deep learning-based approach to predict the RNFL thickness around optic disc regions in fundus photography for glaucoma screening. The proposed deep learning model is based on a convolutional neural network (CNN) and utilizes images taken with fundus photography and with RNFL thickness measured with OCT for model training and validation. Using a dataset acquired from normal tension glaucoma (NTG) patients, the trained model can estimate RNFL thicknesses in 12 optic disc regions from fundus photos. Using intuitive thickness labels to identify localized damage of the optic nerve head and then estimating regional RNFL thicknesses from fundus images, we determine that screening for glaucoma could achieve 92% sensitivity and 86.9% specificity. Receiver operating characteristic (ROC) analysis results for specificity of 80% demonstrate that use of the localized mean over superior and inferior regions reaches 90.7% sensitivity, whereas 71.2% sensitivity is reached using the global RNFL thicknesses for specificity at 80%. This demonstrates that the new approach of using regional RNFL thicknesses in fundus images holds good promise as a potential screening technique for early stage of glaucoma.

Quantification and visualization of metastatic lung tumors in mice.

Histopathological examination is important for the diagnosis of various diseases. Conventional histopathology provides a two-dimensional view of the tissues, and requires the tissue to be extracted, fixed, and processed using histotechnology techniques. However, there is an increasing need for three-dimensional (3D) images of structures in biomedical research. The objective of this study was to develop reliable, objective tools for visualizing and quantifying metastatic tumors in mouse lung using micro-computed tomography (micro-CT), optical coherence tomography (OCT), and field emission-scanning electron microscopy (FE-SEM). Melanoma cells were intravenously injected into the tail vein of 8-week-old C57BL/6 mice. The mice were euthanized at 2 or 4 weeks after injection. Lungs were fixed and examined by micro-CT, OCT, FE-SEM, and histopathological observation. Micro-CT clearly distinguished between tumor and normal cells in surface and deep lesions, thereby allowing 3D quantification of the tumor volume. OCT showed a clear difference between the tumor and surrounding normal tissues. FE-SEM clearly showed round tumor cells, mainly located in the alveolar wall and growing inside the alveoli. Therefore, whole-tumor 3D imaging successfully visualized the metastatic tumor and quantified its volume. This promising approach will allow for fast and label-free 3D phenotyping of diverse tissue structures.

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images.

Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy.

Highly sensitive pregnancy test kit via oriented antibody conjugation on brush-type ligand-coated quantum beads.

Lateral flow assays (LFA) enable development of portable and rapid diagnostic kits; however, their capacity to detect low levels of disease markers remains poor. Here, we report a highly sensitive pregnancy test kit as a proof of concept, by combining brush-type ligand-coated quantum beads (B-type QBs) and nanobody, which can control the antibody orientation and enhance sensitivity. The brush-type ligand provided excellent dispersion stability and high-binding capacity toward antibody. Fc-binding nanobody increased the antigen-binding capacity of conjugated antibodies on the B-type QBs. To facilitate convenient acquisition of the LFA results, we developed a smartphone-based reader with a 3D-printed optical imaging module, and validated the diagnostic performance of the sensing platform. The pregnancy test kit achieved a 5.1 pg mL-1 limit of detection, corresponding to the levels for early-stage detection of heart disease and malaria. Our LFA application can potentially be expanded to diagnosis other diseases by simply changing the antibody pair in the kit.

Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing.

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR.

The synergistic effect of biomimetic electrical stimulation and extracellular-matrix-mimetic nanopattern for upregulating cell activities.

Endogenous bioelectric signaling and the extracellular matrix (ECM) are factors that have a great effect on the performance of cellular functions. Presenting an experimental platform to confirm the synergy effects of an electrical stimulation, which simulates endogenous bioelectricity, and nanopatterns that can be precisely fabricated in various patterns sizes makes it possible to consider those factors effectively. Herein, we have performed a comparison of cellular response to each of general electrical stimulation and biomimetic electrical stimulation (BES) and demonstrated the synergy effects of electrical stimulation and ECM-mimetic nanopatterns. BES has provided the most remarkable proliferation among different types of electrical stimulation and upregulated the behavior of cells through synergy effects with ECM-mimetic nanopatterns. Thus, it is believed that using the synergy effects of BES and ECM-like nanopatterns has broad applications in the biomedical field, such as cell culture with electrical stimulation, induction of cell growth, tissue repair, etc.

Synthetic Retinoid Seletinoid G Improves Skin Barrier Function through Wound Healing and Collagen Realignment in Human Skin Equivalents.

The outer epidermal skin is a primary barrier that protects the body from extrinsic factors, such as ultraviolet (UV) radiation, chemicals and pollutants. The complete epithelialization of a wound by keratinocytes is essential for restoring the barrier function of the skin. However, age-related alterations predispose the elderly to impaired wound healing. Therefore, wound-healing efficacy could be also considered as a potent function of an anti-aging reagent. Here, we examine the epidermal wound-healing efficacy of the fourth-generation retinoid, seletinoid G, using HaCaT keratinocytes and skin tissues. We found that seletinoid G promoted the proliferation and migration of keratinocytes in scratch assays and time-lapse imaging. It also increased the gene expression levels of several keratinocyte proliferation-regulating factors. In human skin equivalents, seletinoid G accelerated epidermal wound closure, as assessed using optical coherence tomography (OCT) imaging. Moreover, second harmonic generation (SHG) imaging revealed that seletinoid G recovered the reduced dermal collagen deposition seen in ultraviolet B (UVB)-irradiated human skin equivalents. Taken together, these results indicate that seletinoid G protects the skin barrier by accelerating wound healing in the epidermis and by repairing collagen deficiency in the dermis. Thus, seletinoid G could be a potent anti-aging agent for protecting the skin barrier.

Compartmentalized Arrays of Matrix Droplets for Quantitative Mass Spectrometry Imaging of Adsorbed Peptides.

Mass spectrometry imaging (MSI) based on matrix-assisted laser desorption/ionization (MALDI) provides information on the identification and spatial distribution of biomolecules. Quantitative analysis, however, has been challenging largely due to heterogeneity in both the size of the matrix crystals and the extraction area. In this work, we present a compartmentalized elastomeric stamp for quantitative MALDI-MSI of adsorbed peptides. Filling the compartments with matrix solution and stamping onto a planar substrate extract and concentrate analytes adsorbed in each compartment into a single analyte-matrix cocrystal over the entire stamped area. Walls between compartments help preserve spatial information on the adsorbates. The mass intensity of the cocrystals directly correlates with the surface coverage of analytes, which enables not only quantitative analysis but estimation of an equilibrium constant for the adsorption. We demonstrate via MALDI-MSI relative quantitation of peptides adsorbed along a microchannel with varying surface coverages.

Serial optical coherence microscopy for label-free volumetric histopathology.

The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents.

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques.

BACKGROUND: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations. OBJECTIVE: In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians' interpretation. METHODS: A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA. RESULTS: An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA. CONCLUSIONS: We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.

In situ facile-forming chitosan hydrogels with tunable physicomechanical and tissue adhesive properties by polymer graft architecture.

In situ forming hydrogels generated upon spontaneous and biocompatible reaction under physiological conditions are widely investigated as injectable and implantable biomaterials. However, it is still a significant challenge to control their mechanics while maintaining their gelation behavior, due to the interdependency between gelation kinetics and mechanics. Herein, physicomechanical properties of in situ forming chitosan hydrogels via Schiff base formation are tuned in a wide range, while maintaining gelation kinetics, via polymer graft architecture. By introducing poly(ethylene glycol) (PEG) grafts with varying lengths and densities, the resulting PEG-grafted chitosan ('PEG-g-Cs') not only dissolve readily in neutral aqueous media, but also effectively control the mechanical properties of hydrogels, while maintaining facile gelation kinetics. These properties are further controlled by the chain length of polymeric crosslinker, PEG-dialdehyde. In addition, tissue adhesive properties of the hydrogels are further examined using ex vivo and in vivo models for their potential applications as tissue sealants.

Snake fang-inspired stamping patch for transdermal delivery of liquid formulations.

A flexible microneedle patch that can transdermally deliver liquid-phase therapeutics would enable direct use of existing, approved drugs and vaccines, which are mostly in liquid form, without the need for additional drug solidification, efficacy verification, and subsequent approval. Specialized dissolving or coated microneedle patches that deliver reformulated, solidified therapeutics have made considerable advances; however, microneedles that can deliver liquid drugs and vaccines still remain elusive because of technical limitations. Here, we present a snake fang-inspired microneedle patch that can administer existing liquid formulations to patients in an ultrafast manner (<15 s). Rear-fanged snakes have an intriguing molar with a groove on the surface, which enables rapid and efficient infusion of venom or saliva into prey. Liquid delivery is based on surface tension and capillary action. The microneedle patch uses multiple open groove architectures that emulate the grooved fangs of rear-fanged snakes: Similar to snake fangs, the microneedles can rapidly and efficiently deliver diverse liquid-phase drugs and vaccines in seconds under capillary action with only gentle thumb pressure, without requiring a complex pumping system. Hydrodynamic simulations show that the snake fang-inspired open groove architectures enable rapid capillary force-driven delivery of liquid formulations with varied surface tensions and viscosities. We demonstrate that administration of ovalbumin and influenza virus with the snake fang-inspired microneedle patch induces robust antibody production and protective immune response in guinea pigs and mice.

Quantitative assessment of regional variation in tissue clearing efficiency using optical coherence tomography (OCT) and magnetic resonance imaging (MRI): A feasibility study.

Tissue clearing has gained attention as a pioneering research tool for imaging of large tissue samples. This technique improves light transmission by reducing light scattering within tissues, either by removing lipids or by replacing water with a high refractive index solution. Although various clearing techniques have been developed, quantitative assessments on clearing efficacy depending on tissue properties are rare. In this study, we developed the quantitative mapping of regional clearing efficacy using mean free path in optical coherence tomography (OCT) and proton density in magnetic resonance imaging (MRI), and demonstrated its feasibility in the brain sample with four representative clearing techniques (benzyl alcohol and benzyl benzoate [BABB], ClearT, Scale, and passive CLARITY technique [PACT]). BABB (solvent-based clearing), involving both refractive index matching and lipid removal, exhibited best optical clearing performance with the highest proton density reduction both in gray and white matter. Lipid-removing techniques such as Scale (aqueous hyperhydration) and PACT (hydrogel embedding) showed higher clearing efficiency in white matter than gray matter in accordance with larger proton density increase in white matter. For ClearT (aqueous-based simple immersion), we observed lowest clearing efficiency in the white matter as well as poor lipid removal reflected in low proton density reduction. Our results showed the feasibility of the regional mapping of clearing efficacy and correlating optical transparency and proton density changes using OCT and MRI from existing tissue clearing techniques. This novel quantitative mapping of clearing efficacy depending on tissue types and clearing methods may be helpful in the development of optimized clearing methods for different biological samples.

Effect of Air Injection Depth on Big-bubble Formation in Lamellar Keratoplasty: an Ex Vivo Study.

This study evaluated the effect of air injection depth in the big-bubble (BB) technique, which is used for corneal tissue preparation in lamellar keratoplasty. The BB technique was performed on ex vivo human corneoscleral buttons using a depth-sensing needle, based on optical coherence tomography (OCT) imaging technology. The needle tip, equipped with a miniaturized OCT depth-sensing probe, was inserted for air injection at a specified depth. Inside the corneal tissue, our needle obtained OCT line profiles, from which residual thickness below the needle tip was measured. Subjects were classified into Groups I, II, III, and IV based on injection depths of 75-80%, 80-85%, 85-90%, and >90% of the full corneal thickness, respectively. Both Type I and II BBs were produced when the mean residual thicknesses of air injection were 109.7 ± 38.0 µm and 52.4 ± 19.2 µm, respectively. Type II BB (4/5) was dominant in group IV. Bubble burst occurred in 1/16 cases of type I BB and 3/16 cases of type II BB, respectively. Injection depth was an important factor in determining the types of BBs produced. Deeper air injection could facilitate formation of Type II BBs, with an increased risk of bubble bursts.