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Zeng S, Wu Y, Wang L, Huang Y, Huang W, Li Z, Gao W, Jiang S, Ge L, Zhang J. In vivo real-time assessment of developmental defects in enamel of anti-Act1 mice using optical coherence tomography. Heliyon 2023; 9:e16545. [PMID: 37274657 PMCID: PMC10238730 DOI: 10.1016/j.heliyon.2023.e16545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 05/10/2023] [Accepted: 05/18/2023] [Indexed: 06/06/2023] Open
Abstract
The purpose of this study was to explore the feasibility of using optical coherence tomography (OCT) for real-time and quantitative monitoring of enamel development in gene-edited enamel defect mice. NF-κB activator 1, known as Act1, is associated with many inflammatory diseases. The antisense oligonucleotide of Act1 was inserted after the CD68 gene promoter, which would cover the start region of the Act1 gene and inhibit its transcription. Anti-Act1 mice, gene-edited mice, were successfully constructed and demonstrated amelogenesis imperfecta by scanning electron microscope (SEM) and energy dispersive X-ray (EDX) spectroscopy. Wild-type (WT) mice were used as the control group in this study. WT mice and anti-Act1 mice at 3 weeks old were examined by OCT every week and killed at eight weeks old. Their mandibular bones were dissected and examined by OCT, micro-computed tomography (micro-CT), and SEM. OCT images showed that the outer layer of enamel of anti-Act1 mice was obviously thinner than that of WT mice but no difference in total thickness. When assessing enamel thickness, there was a significant normal linear correlation between these methods. OCT could scan the imperfect developed enamel noninvasively and quickly, providing images of the enamel layers of mouse incisors.
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Affiliation(s)
- Sujuan Zeng
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
| | - Yuejun Wu
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
| | - Lijing Wang
- Vascular Biology Research Institute, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Yuhang Huang
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
| | - Wenyan Huang
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
| | - Ziling Li
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
| | - Weijian Gao
- School of Biomedical Engineering, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Guangzhou Medical University, Guangzhou, 511436, China
| | - Siqing Jiang
- Department of Temporomandibular Joint, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
| | - Lihong Ge
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
- Department of Pediatric Dentistry, Stomatology Hospital of Peking University, Beijing, 100081, China
| | - Jian Zhang
- Department of Pedodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, 510182, China
- School of Biomedical Engineering, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Guangzhou Medical University, Guangzhou, 511436, China
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Massalimova A, Timmermans M, Esfandiari H, Carrillo F, Laux CJ, Farshad M, Denis K, Fürnstahl P. Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review. Front Surg 2022; 9:952539. [PMID: 35990097 PMCID: PMC9381957 DOI: 10.3389/fsurg.2022.952539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.
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Affiliation(s)
- Aidana Massalimova
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
- Correspondence: Aidana Massalimova
| | - Maikel Timmermans
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Hooman Esfandiari
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Fabio Carrillo
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
| | - Christoph J. Laux
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Kathleen Denis
- KU Leuven, Campus Group T, BioMechanics (BMe), Smart Instrumentation Group, Leuven, Belgium
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), Balgrist Campus, University of Zurich, Zurich, Switzerland
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Lautman Z, Winetraub Y, Blacher E, Yu C, Terem I, Chibukhchyan A, Marshel JH, de la Zerda A. Intravital 3D visualization and segmentation of murine neural networks at micron resolution. Sci Rep 2022; 12:13130. [PMID: 35907928 PMCID: PMC9338956 DOI: 10.1038/s41598-022-14450-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 06/06/2022] [Indexed: 12/03/2022] Open
Abstract
Optical coherence tomography (OCT) allows label-free, micron-scale 3D imaging of biological tissues’ fine structures with significant depth and large field-of-view. Here we introduce a novel OCT-based neuroimaging setting, accompanied by a feature segmentation algorithm, which enables rapid, accurate, and high-resolution in vivo imaging of 700 μm depth across the mouse cortex. Using a commercial OCT device, we demonstrate 3D reconstruction of microarchitectural elements through a cortical column. Our system is sensitive to structural and cellular changes at micron-scale resolution in vivo, such as those from injury or disease. Therefore, it can serve as a tool to visualize and quantify spatiotemporal brain elasticity patterns. This highly transformative and versatile platform allows accurate investigation of brain cellular architectural changes by quantifying features such as brain cell bodies’ density, volume, and average distance to the nearest cell. Hence, it may assist in longitudinal studies of microstructural tissue alteration in aging, injury, or disease in a living rodent brain.
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Affiliation(s)
- Ziv Lautman
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.,Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA
| | - Yonatan Winetraub
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA.,Biophysics Program at Stanford, Stanford, CA, 94305, USA.,The Bio-X Program, Stanford, CA, 94305, USA
| | - Eran Blacher
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, 94305, USA.,Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus Givat-Ram, 9190401, Jerusalem, Israel
| | - Caroline Yu
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA
| | - Itamar Terem
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.,Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | | | - James H Marshel
- CNC Department, Stanford University, Stanford, CA, 94305, USA
| | - Adam de la Zerda
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA. .,Molecular Imaging Program at Stanford, Stanford, CA, 94305, USA. .,Biophysics Program at Stanford, Stanford, CA, 94305, USA. .,The Bio-X Program, Stanford, CA, 94305, USA. .,Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA. .,The Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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Abstract
Infertility affects 1 in 6 couples, and male factor infertility has been implicated as a cause in 50% of cases. Azoospermia is defined as the absence of spermatozoa in the ejaculate and is considered the most extreme form of male factor infertility. Historically, these men were considered sterile but, with the advent of testicular sperm extraction and assisted reproductive technologies, men with azoospermia are able to biologically father their own children. Non-obstructive azoospermia (NOA) occurs when there is an impairment to spermatogenesis. This review describes the contemporary management of NOA and discusses the role of hormone stimulation therapy, surgical and embryological factors, and novel technologies such as proteomics, genomics, and artificial intelligence systems in the diagnosis and treatment of men with NOA. Moreover, we highlight that men with NOA represent a vulnerable population with an increased risk of developing cancer and cardiovascular comorbodities.
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Affiliation(s)
- Tharu Tharakan
- Section of Investigative Medicine, Imperial College London, Hammersmith Hospital, London, United Kingdom
- Department of Urology, Imperial Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road, London, United Kingdom
| | - Rong Luo
- Section of Investigative Medicine, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Channa N Jayasena
- Section of Investigative Medicine, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Suks Minhas
- Department of Urology, Imperial Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road, London, United Kingdom
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Zeng Y, Chapman WC, Lin Y, Li S, Mutch M, Zhu Q. Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography. J Biophotonics 2021; 14:e202000276. [PMID: 33064368 PMCID: PMC8196414 DOI: 10.1002/jbio.202000276] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/08/2020] [Accepted: 10/11/2020] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.
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Affiliation(s)
- Yifeng Zeng
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
| | - William C Chapman
- Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Yixiao Lin
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
| | - Matthew Mutch
- Department of Surgery, Section of Colon and Rectal Surgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
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Dolganova IN, Aleksandrova PV, Nikitin PV, Alekseeva AI, Chernomyrdin NV, Musina GR, Beshplav ST, Reshetov IV, Potapov AA, Kurlov VN, Tuchin VV, Zaytsev KI. Capability of physically reasonable OCT-based differentiation between intact brain tissues, human brain gliomas of different WHO grades, and glioma model 101.8 from rats. Biomed Opt Express 2020; 11:6780-6798. [PMID: 33282523 PMCID: PMC7687948 DOI: 10.1364/boe.409692] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 05/17/2023]
Abstract
Optical coherence tomography (OCT) of the ex vivo rat and human brain tissue samples is performed. The set of samples comprises intact white and gray matter, as well as human brain gliomas of the World Health Organization (WHO) Grades I-IV and glioma model 101.8 from rats. Analysis of OCT signals is aimed at comparing the physically reasonable properties of tissues, and determining the attenuation coefficient, parameter related to effective refractive index, and their standard deviations. Data analysis is based on the linear discriminant analysis and estimation of their dispersion in a four-dimensional principal component space. The results demonstrate the distinct contrast between intact tissues and low-grade gliomas and moderate contrast between intact tissues and high-grade gliomas. Particularly, the mean values of attenuation coefficient are 7.56±0.91, 3.96±0.98, and 5.71±1.49 mm-1 for human white matter, glioma Grade I, and glioblastoma, respectively. The significant variability of optical properties of high Grades and essential differences between rat and human brain tissues are observed. The dispersion of properties enlarges with increase of the glioma WHO Grade, which can be attributed to the growing heterogeneity of pathological brain tissues. The results of this study reveal the advantages and drawbacks of OCT for the intraoperative diagnosis of brain gliomas and compare its abilities separately for different grades of malignancy. The perspective of OCT to differentiate low-grade gliomas is highlighted by the low performance of the existing intraoperational methods and instruments.
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Affiliation(s)
- I. N. Dolganova
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka 142432, Russia
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia
| | - P. V. Aleksandrova
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka 142432, Russia
| | - P. V. Nikitin
- Burdenko Neurosurgery Institute, Moscow 125047, Russia
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow 119991, Russia
| | - A. I. Alekseeva
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka 142432, Russia
- Research Institute of Human Morphology, Moscow 117418, Russia
| | - N. V. Chernomyrdin
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow 119991, Russia
| | - G. R. Musina
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow 119991, Russia
| | - S. T. Beshplav
- Burdenko Neurosurgery Institute, Moscow 125047, Russia
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow 119991, Russia
| | - I. V. Reshetov
- Institute for Cluster Oncology, Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia
- Academy of Postgraduate Education FSCC FMBA, Moscow 125310, Russia
| | - A. A. Potapov
- Burdenko Neurosurgery Institute, Moscow 125047, Russia
| | - V. N. Kurlov
- Institute of Solid State Physics of the Russian Academy of Sciences, Chernogolovka 142432, Russia
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia
| | - V. V. Tuchin
- Saratov State University, Saratov 410012, Russia
- Institute of Precision Mechanics and Control of the Russian Academy of Sciences, Saratov 410028, Russia
- Tomsk State University, Tomsk 634050, Russia
| | - K. I. Zaytsev
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow 119991, Russia
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Affiliation(s)
- Shuliang Jiao
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA
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