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Mone NS, Syed S, Ravichandiran P, Kamble EE, Pardesi KR, Salunke-Gawali S, Rai M, Vikram Singh A, Prasad Dakua S, Park BH, Yoo DJ, Satpute SK. Synergistic and Additive Effects of Menadione in Combination with Antibiotics on Multidrug-Resistant Staphylococcus aureus: Insights from Structure-Function Analysis of Naphthoquinones. ChemMedChem 2023; 18:e202300328. [PMID: 37874976 DOI: 10.1002/cmdc.202300328] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/24/2023] [Accepted: 10/24/2023] [Indexed: 10/26/2023]
Abstract
Antimicrobial resistance (AMR) interferes with the effective treatment of infections and increases the risk of microbial spread and infection-related illness and death. The synergistic activities of combinations of antimicrobial compounds offer satisfactory approaches to some extent. Structurally diverse naphthoquinones (NQs) including menadione (-CH3 group at C2) exhibit substantial antimicrobial activities against multidrug-resistant (MDR) pathogens. We explored the combinations of menadione with antibiotic ciprofloxacin or ampicillin against Staphylococcus aureus and its biofilms. We found an additive (0.590 %) were also observed. However, preformed biofilms were not affected. Dent formation was also evident in S. aureus treated with the test compounds. The structure-function relationship (SFR) of NQs was used to determine and predict their activity pattern against pathogens. Analysis of 10 structurally distinct NQs revealed that the compounds with -Cl, -Br, -CH3 , or -OH groups displayed the lowest MICs (32-256 μg/mL). Furthermore, 1,4-NQs possessing a halogen or -CH3 moiety showed elevated ROS activity, whereas molecules with an -OH group affected cell integrity. Improved activity of antimicrobial combinations and SFR approaches are significant in antimicrobial therapies.
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Affiliation(s)
- Nishigandha S Mone
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra, 411007, India
| | - Sahil Syed
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra, 411007, India
| | - Palanisamy Ravichandiran
- Department of Energy Storage/Conversion Engineering (BK21 FOUR) of Graduate School, Hydrogen and Fuel Cell Research Center, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
- Department of Life Science, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
| | - Ekta E Kamble
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra, 411007, India
| | - Karishma R Pardesi
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra, 411007, India
| | - Sunita Salunke-Gawali
- Department of Chemistry, Savitribai Phule Pune University, Pune, Maharashtra m, 411007, India
| | - Mansi Rai
- Department of Microbiology, Central University of Rajasthan Ajmer, Rajasthan, 305817, India
| | - Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute (BfR), Max-Dohrn-Str. 8-10, 10589, Berlin, Germany
| | | | - Byung-Hyun Park
- Department of Biochemistry, Jeonbuk National University Medical School, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54896, Republic of Korea
| | - Dong Jin Yoo
- Department of Energy Storage/Conversion Engineering (BK21 FOUR) of Graduate School, Hydrogen and Fuel Cell Research Center, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
- Department of Life Science, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
| | - Surekha K Satpute
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra, 411007, India
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Rai P, Ansari MY, Warfa M, Al-Hamar H, Abinahed J, Barah A, Dakua SP, Balakrishnan S. Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review. Cancer Med 2023. [PMID: 37191030 DOI: 10.1002/cam4.6089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Percutaneous thermal ablation has become the preferred therapeutic treatment option for liver cancers that cannot be resected. Since ablative zone tissue changes over time, it becomes challenging to determine therapy effectiveness over an extended period. Thus, an immediate post-procedural evaluation of the ablation zone is crucial, as it could influence the need for a second-look treatment or follow-up plan. Assessing treatment response immediately after ablation is essential to attain favorable outcomes. This study examines the efficacy of image fusion strategies immediately post-ablation in liver neoplasms to determine therapeutic response. METHODOLOGY A comprehensive systematic search using PRISMA methodology was conducted using EMBASE, MEDLINE (via PUBMED), and Cochrane Library Central Registry electronic databases to identify articles that assessed the immediate post-ablation response in malignant hepatic tumors with fusion imaging (FI) systems. The data were retrieved on relevant clinical characteristics, including population demographics, pre-intervention clinical history, lesion characteristics, and intervention type. For the outcome metrics, variables such as average fusion time, intervention metrics, technical success rate, ablative safety margin, supplementary ablation rate, technical efficacy rate, LTP rates, and reported complications were extracted. RESULTS Twenty-two studies were included for review after fulfilling the study eligibility criteria. FI's immediate technical success rate ranged from 81.3% to 100% in 17/22 studies. In 16/22 studies, the ablative safety margin was assessed immediately after ablation. Supplementary ablation was performed in 9 studies following immediate evaluation by FI. In 15/22 studies, the technical effectiveness rates during the first follow-up varied from 89.3% to 100%. CONCLUSION Based on the studies included, we found that FI can accurately determine the immediate therapeutic response in liver cancer ablation image fusion and could be a feasible intraprocedural tool for determining short-term post-ablation outcomes in unresectable liver neoplasms. There are some technical challenges that limit the widespread adoption of FI techniques. Large-scale randomized trials are warranted to improve on existing protocols. Future research should emphasize improving FI's technological capabilities and clinical applicability to a broader range of tumor types and ablation procedures.
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Affiliation(s)
- Pragati Rai
- Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | | | - Mohammed Warfa
- Department of Clinical Imaging, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA
| | - Hammad Al-Hamar
- Department of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar
| | - Julien Abinahed
- Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | - Ali Barah
- Department of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar
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Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, Tissato V, Prabhu KS, Uddin S, Dakua SP. Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 2023; 163:114784. [PMID: 37121152 DOI: 10.1016/j.biopha.2023.114784] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023] Open
Abstract
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | | | - Namuna Paudel
- Department of Chemistry, Amrit Campus, Institute of Science and Technology, Tribhuvan University, Lainchaur, Kathmandu 44600 Nepal
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Veronica Tissato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Kirti S Prabhu
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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Ansari MY, Yang Y, Meher PK, Dakua SP. Dense-PSP-UNet: A neural network for fast inference liver ultrasound segmentation. Comput Biol Med 2023; 153:106478. [PMID: 36603437 DOI: 10.1016/j.compbiomed.2022.106478] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/29/2022] [Accepted: 12/21/2022] [Indexed: 01/02/2023]
Abstract
Liver Ultrasound (US) or sonography is popularly used because of its real-time output, low-cost, ease-of-use, portability, and non-invasive nature. Segmentation of real-time liver US is essential for diagnosing and analyzing liver conditions (e.g., hepatocellular carcinoma (HCC)), assisting the surgeons/radiologists in therapeutic procedures. In this paper, we propose a method using a modified Pyramid Scene Parsing (PSP) module in tuned neural network backbones to achieve real-time segmentation without compromising the segmentation accuracy. Considering widespread noise in US data and its impact on outcomes, we study the impact of pre-processing and the influence of loss functions on segmentation performance. We have tested our method after annotating a publicly available US dataset containing 2400 images of 8 healthy volunteers (link to the annotated dataset is provided); the results show that the Dense-PSP-UNet model achieves a high Dice coefficient of 0.913±0.024 while delivering a real-time performance of 37 frames per second (FPS).
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Affiliation(s)
| | - Yin Yang
- Hamad Bin Khalifa Uinversity, Doha, Qatar
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5
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Chandrasekar V, Ansari MY, Singh AV, Uddin S, Prabhu KS, Dash S, Khodor SA, Terranegra A, Avella M, Dakua SP. Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta. IEEE Access 2023; 11:52726-52739. [DOI: 10.1109/access.2023.3272987] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2023]
Affiliation(s)
| | | | | | - Shahab Uddin
- Hamad Medical Corporation, Translational Research Institute, Academic Health System, Doha, Qatar
| | - Kirthi S. Prabhu
- Hamad Medical Corporation, Translational Research Institute, Academic Health System, Doha, Qatar
| | - Sagnika Dash
- Department of Obstetrics and Gynecology, Apollo Clinic, Doha, Qatar
| | - Souhaila Al Khodor
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | - Annalisa Terranegra
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
| | - Matteo Avella
- Maternal and Child Health Department, Research Branch, Sidra Medicine, Ar-Rayyan, Doha, Qatar
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Singh AV, Chandrasekar V, Laux P, Luch A, Dakua SP, Zamboni P, Shelar A, Yang Y, Pandit V, Tisato V, Gemmati D. Micropatterned Neurovascular Interface to Mimic the Blood–Brain Barrier’s Neurophysiology and Micromechanical Function: A BBB-on-CHIP Model. Cells 2022; 11:cells11182801. [PMID: 36139383 PMCID: PMC9497163 DOI: 10.3390/cells11182801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 08/24/2022] [Accepted: 09/01/2022] [Indexed: 12/25/2022] Open
Abstract
A hybrid blood–brain barrier (BBB)-on-chip cell culture device is proposed in this study by integrating microcontact printing and perfusion co-culture to facilitate the study of BBB function under high biological fidelity. This is achieved by crosslinking brain extracellular matrix (ECM) proteins to the transwell membrane at the luminal surface and adapting inlet–outlet perfusion on the porous transwell wall. While investigating the anatomical hallmarks of the BBB, tight junction proteins revealed tortuous zonula occludens (ZO-1), and claudin expressions with increased interdigitation in the presence of astrocytes were recorded. Enhanced adherent junctions were also observed. This junctional phenotype reflects in-vivo-like features related to the jamming of cell borders to prevent paracellular transport. Biochemical regulation of BBB function by astrocytes was noted by the transient intracellular calcium effluxes induced into endothelial cells. Geometry-force control of astrocyte–endothelial cell interactions was studied utilizing traction force microscopy (TFM) with fluorescent beads incorporated into a micropatterned polyacrylamide gel (PAG). We observed the directionality and enhanced magnitude in the traction forces in the presence of astrocytes. In the future, we envisage studying transendothelial electrical resistance (TEER) and the effect of chemomechanical stimulations on drug/ligand permeability and transport. The BBB-on-chip model presented in this proposal should serve as an in vitro surrogate to recapitulate the complexities of the native BBB cellular milieus.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
- Correspondence: (A.V.S.); (S.P.D.)
| | | | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Sarada Prasad Dakua
- Department of Surgery, Hamad Medical Corporation (HMC), Doha 3050, Qatar
- Correspondence: (A.V.S.); (S.P.D.)
| | - Paolo Zamboni
- Department of Vascular Surgery, University of Ferrara, 44121 Ferrara, Italy
| | - Amruta Shelar
- Department of Technology, Savitribai Phule Pune University, Pune 411007, India
| | - Yin Yang
- College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha 24404, Qatar
| | - Vaibhav Pandit
- Dynex Technologies, 14340 Sullyfield Circle, Chantilly, VA 20151, USA
| | - Veronica Tisato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
- Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
- Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy
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Ansari MY, Yang Y, Balakrishnan S, Abinahed J, Al-Ansari A, Warfa M, Almokdad O, Barah A, Omer A, Singh AV, Meher PK, Bhadra J, Halabi O, Azampour MF, Navab N, Wendler T, Dakua SP. A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci Rep 2022; 12:14153. [PMID: 35986015 PMCID: PMC9391485 DOI: 10.1038/s41598-022-16828-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.
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8
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Ansari MY, Abdalla A, Ansari MY, Ansari MI, Malluhi B, Mohanty S, Mishra S, Singh SS, Abinahed J, Al-Ansari A, Balakrishnan S, Dakua SP. Correction: Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med Imaging 2022; 22:141. [PMID: 35941585 PMCID: PMC9361601 DOI: 10.1186/s12880-022-00869-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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9
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Chandrasekar V, Singh AV, Maharjan RS, Dakua SP, Balakrishnan S, Dash S, Laux P, Luch A, Singh S, Pradhan M. Perspectives on the Technological Aspects and Biomedical Applications of Virus‐Like Particles/Nanoparticles in Reproductive Biology: Insights on the Medicinal and Toxicological Outlook. Advanced NanoBiomed Research 2022. [DOI: 10.1002/anbr.202200010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
| | - Ajay Vikram Singh
- German Federal Institute for Risk Assessment (BfR) Department of Chemical and Product Safety Max-Dohrn-Straße 8-10 10589 Berlin Germany
| | - Romi Singh Maharjan
- German Federal Institute for Risk Assessment (BfR) Department of Chemical and Product Safety Max-Dohrn-Straße 8-10 10589 Berlin Germany
| | | | | | - Sagnika Dash
- Obstetrics and Gynecology Apollo Clinic Qatar 23656 Doha Qatar
| | - Peter Laux
- German Federal Institute for Risk Assessment (BfR) Department of Chemical and Product Safety Max-Dohrn-Straße 8-10 10589 Berlin Germany
| | - Andreas Luch
- German Federal Institute for Risk Assessment (BfR) Department of Chemical and Product Safety Max-Dohrn-Straße 8-10 10589 Berlin Germany
| | - Suyash Singh
- Department of Neurosurgery All India Institute of Medical Sciences Raebareli UP 226001 India
| | - Mandakini Pradhan
- Department of Fetal Medicine Sanjay Gandhi Post Graduate Institute of Medical Sciences Reabareli Road Lucknow UP 226014 India
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10
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Ansari MY, Abdalla A, Ansari MY, Ansari MI, Malluhi B, Mohanty S, Mishra S, Singh SS, Abinahed J, Al-Ansari A, Balakrishnan S, Dakua SP. Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med Imaging 2022; 22:97. [PMID: 35610600 PMCID: PMC9128093 DOI: 10.1186/s12880-022-00825-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 05/09/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012–2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.
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11
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Dakua SP, Nayak A. A review on treatments of hepatocellular carcinoma—role of radio wave ablation and possible improvements. Egypt Liver Journal 2022. [DOI: 10.1186/s43066-022-00191-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Currently, several treatment options are available for liver cancer depending on various factors such as location, size, shape, and liver function. Image fusion is required for the diagnosis, intervention, and follow-up of certain HCCs. Presently, mental fusion is the only way while diagnosing liver lesions by comparing the ultrasound (US) image with the computed tomography (CT) image. Nevertheless, mental fusion is bound to have errors. The objective of this paper is to study the present treatment options for hepatocellular carcinoma and review the present treatment options, list out their potential limitations, and present a possible alternative solution based on the findings to reduce errors and mistargeting.
Methods
This is a systematic review on the present treatment options for hepatocellular carcinoma, especially radio wave ablation.
Results
It is found that computer fusion is the possible alternative to the present mental registration.
Conclusions
Although computer fusion is the best alternative to use radio wave ablation, there have been a few open-ended questions to further explore.
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Singh AV, Romeo A, Scott K, Wagener S, Leibrock L, Laux P, Luch A, Kerkar P, Balakrishnan S, Dakua SP, Park B. Emerging Technologies for In Vitro Inhalation Toxicology (Adv. Healthcare Mater. 18/2021). Adv Healthc Mater 2021. [DOI: 10.1002/adhm.202170082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Singh AV, Romeo A, Scott K, Wagener S, Leibrock L, Laux P, Luch A, Kerkar P, Balakrishnan S, Dakua SP, Park B. Emerging Technologies for In Vitro Inhalation Toxicology. Adv Healthc Mater 2021; 10:e2100633. [PMID: 34292676 DOI: 10.1002/adhm.202100633] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/04/2021] [Indexed: 12/20/2022]
Abstract
Respiratory toxicology remains a major research area in the 21st century since current scenario of airborne viral infection transmission and pollutant inhalation is expected to raise the annual morbidity beyond 2 million. Clinical and epidemiological research connecting human exposure to air contaminants to understand adverse pulmonary health outcomes is, therefore, an immediate subject of human health assessment. Important observations in defining systemic effects of environmental contaminants on inhalation metabolic dysfunction, liver health, and gastrointestinal tract have been well explored with in vivo models. In this review, a framework is provided, a paradigm is established about inhalation toxicity testing in vitro, and a brief overview of breathing Lungs-on-Chip (LoC) as design concepts is given. The optimized bioengineering approaches and microfluidics with their fundamental pros, and cons are presented. There are different strategies that researchers apply to inhalation toxicity studies to assess a variety of inhalable substances and relevant LoC approaches. A case study from published literature and frame arguments about reproducibility as well as in vitro/in vivo correlations are discussed. Finally, the opportunities and challenges in soft robotics, systems inhalation toxicology approach integrating bioengineering, machine learning, and artificial intelligence to address a multitude model for future toxicology are discussed.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Max‐Dohrn‐Strasse 8‐10 Berlin 10589 Germany
| | - Anthony Romeo
- Department of Chemical Engineering Rayen School of Engineering Youngstown State University Youngstown OH 44555 USA
| | - Kassandra Scott
- Department of Chemical Engineering Rayen School of Engineering Youngstown State University Youngstown OH 44555 USA
| | - Sandra Wagener
- Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Max‐Dohrn‐Strasse 8‐10 Berlin 10589 Germany
| | - Lars Leibrock
- Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Max‐Dohrn‐Strasse 8‐10 Berlin 10589 Germany
| | - Peter Laux
- Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Max‐Dohrn‐Strasse 8‐10 Berlin 10589 Germany
| | - Andreas Luch
- Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Max‐Dohrn‐Strasse 8‐10 Berlin 10589 Germany
| | - Pranali Kerkar
- ICMR – National AIDS Research Institute (NARI) Pune Maharashtra 411026 India
| | - Shidin Balakrishnan
- Department of Surgery Hamad Medical Corporation (HMC) PO Box 3050 Doha Qatar
| | - Sarada Prasad Dakua
- Department of Surgery Hamad Medical Corporation (HMC) PO Box 3050 Doha Qatar
| | - Byung‐Wook Park
- Department of Chemical Engineering Rayen School of Engineering Youngstown State University Youngstown OH 44555 USA
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14
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Singh AV, Maharjan RS, Kromer C, Laux P, Luch A, Vats T, Chandrasekar V, Dakua SP, Park BW. Advances in Smoking Related In Vitro Inhalation Toxicology: A Perspective Case of Challenges and Opportunities from Progresses in Lung-on-Chip Technologies. Chem Res Toxicol 2021; 34:1984-2002. [PMID: 34397218 DOI: 10.1021/acs.chemrestox.1c00219] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The inhalation toxicology of multifaceted particulate matter from the environment, cigarette smoke, and e-cigarette liquid vapes is a major research topic concerning the adverse effect of these items on lung tissue. In vitro air-liquid interface (ALI) culture models hold more potential in an inhalation toxicity assessment. Apropos to e-cigarette toxicity, the multiflavor components of the vapes pose a complex experimental bottleneck. While an appropriate ALI setup has been one part of the focus to overcome this, parallel attention towards the development of an ideal exposure system has pushed the field forward. With the advent of microfluidic devices, lung-on-chip (LOC) technologies show enormous opportunities in in vitro smoke-related inhalation toxicity. In this review, we provide a framework, establish a paradigm about smoke-related inhalation toxicity testing in vitro, and give a brief overview of breathing LOC experimental design concepts. The capabilities with optimized bioengineering approaches and microfluidics and their fundamental pros and cons are presented with specific case studies. The LOC model can imitate the structural, functional, and mechanical properties of human alveolar-capillary interface and are more reliable than conventional in vitro models. Finally, we outline current perspective challenges as well as opportunities of future development to smoking lungs-on-chip technologies based on advances in soft robotics, machine learning, and bioengineering.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Strasse 8-10, Berlin 10589, Germany
| | - Romi Singh Maharjan
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Strasse 8-10, Berlin 10589, Germany
| | - Charlotte Kromer
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Strasse 8-10, Berlin 10589, Germany
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Strasse 8-10, Berlin 10589, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Strasse 8-10, Berlin 10589, Germany
| | - Tanusri Vats
- KNIPSS Management Institute, Faridipur Campus, NH 96, Faizabad-Allahabad Road, Sultanpur 228119, Uttar Pradesh, India
| | | | | | - Byung-Wook Park
- Department of Chemical Engineering, Rayen School of Engineering, Youngstown State University, Youngstown 44555, Ohio, United States
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Singh AV, Chandrasekar V, Janapareddy P, Mathews DE, Laux P, Luch A, Yang Y, Garcia-Canibano B, Balakrishnan S, Abinahed J, Al Ansari A, Dakua SP. Emerging Application of Nanorobotics and Artificial Intelligence To Cross the BBB: Advances in Design, Controlled Maneuvering, and Targeting of the Barriers. ACS Chem Neurosci 2021; 12:1835-1853. [PMID: 34008957 DOI: 10.1021/acschemneuro.1c00087] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The blood-brain barrier (BBB) is a prime focus for clinicians to maintain the homeostatic function in health and deliver the theranostics in brain cancer and number of neurological diseases. The structural hierarchy and in situ biochemical signaling of BBB neurovascular unit have been primary targets to recapitulate into the in vitro modules. The microengineered perfusion systems and development in 3D cellular and organoid culture have given a major thrust to BBB research for neuropharmacology. In this review, we focus on revisiting the nanoparticles based bimolecular engineering to enable them to maneuver, control, target, and deliver the theranostic payloads across cellular BBB as nanorobots or nanobots. Subsequently we provide a brief outline of specific case studies addressing the payload delivery in brain tumor and neurological disorders (e.g., Alzheimer's disease, Parkinson's disease, multiple sclerosis, etc.). In addition, we also address the opportunities and challenges across the nanorobots' development and design. Finally, we address how computationally powered machine learning (ML) tools and artificial intelligence (AI) can be partnered with robotics to predict and design the next generation nanorobots to interact and deliver across the BBB without causing damage, toxicity, or malfunctions. The content of this review could be references to multidisciplinary science to clinicians, roboticists, chemists, and bioengineers involved in cutting-edge pharmaceutical design and BBB research.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | | | - Poonam Janapareddy
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
| | - Divya Elsa Mathews
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Straße 8-10, 10589 Berlin, Germany
| | - Yin Yang
- College of Science and Engineering, Hamad Bin Khalifa University (HBKU), 24404 Doha, Qatar
| | | | | | - Julien Abinahed
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
| | - Abdulla Al Ansari
- Department of Surgery, Hamad Medical Corporation (HMC), 3050 Doha, Qatar
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Akhtar Y, Dakua SP, Abdalla A, Aboumarzouk OM, Ansari MY, Abinahed J, Elakkad MSM, Al-Ansari A. Risk Assessment of Computer-aided Diagnostic Software for Hepatic Resection. IEEE Trans Radiat Plasma Med Sci 2021. [DOI: 10.1109/trpms.2021.3071148] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yusuf Akhtar
- Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India
| | | | | | | | | | - Julien Abinahed
- Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
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Vikram Singh A, Laux P, Luch A, Balkrishnan S, Prasad Dakua S. Bottom-UP assembly of nanorobots: extending synthetic biology to complex material design. ACTA ACUST UNITED AC 2019. [DOI: 10.15761/fnn.1000s2005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Abstract
High-level noise and low contrast characteristics in medical images continue to present major bottlenecks in their segmentation despite increased imaging modalities. This paper presents a semi-automatic algorithm that utilizes the noise for enhancing the contrast of low contrast input magnetic resonance images followed by a new graph cut method to reconstruct the surface of left ventricle. The main contribution in this work is a new formulation for preventing the conventional cellular automata method to leak into surrounding regions of similar intensity. Instead of segmenting each slice of a subject sequence individually, we empirically select a few slices, segment them, and reconstruct the left ventricular surface. During the course of surface reconstruction, we use level sets to segment the rest of the slices automatically. We have throughly evaluated the method on both York and MICCAI Grand Challenge workshop databases. The average Dice coefficient (in %) is found to be 92.4 ± 1.3 (value indicates the mean and standard deviation) whereas false positive ratio, false negative ratio, and specificity are found to be 0.019, 7.62 × 10-3, and 0.75, respectively. Average Hausdorff distance between segmented contour and ground truth is determined to be 2.94 mm. The encouraging quantitative and qualitative results reflect the potential of the proposed method.
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Dakua SP, Abinahed J, Al-Ansari A. Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets. J Med Imaging (Bellingham) 2015; 2:024006. [PMID: 26158101 PMCID: PMC4478775 DOI: 10.1117/1.jmi.2.2.024006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Accepted: 04/22/2015] [Indexed: 11/14/2022] Open
Abstract
Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.
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Affiliation(s)
- Sarada Prasad Dakua
- Qatar Science & Technology Park, Qatar Robotic Surgery Centre, Al Gharrafa Street, Al Rayyan, Education City, PO Box 210000, Doha, Qatar
| | - Julien Abinahed
- Qatar Science & Technology Park, Qatar Robotic Surgery Centre, Al Gharrafa Street, Al Rayyan, Education City, PO Box 210000, Doha, Qatar
| | - Abdulla Al-Ansari
- Hamad General Hospital, Department of Urology, Hamad Medical City, PO Box 3050, Doha, Qatar
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Dakua SP. Use of chaos concept in medical image segmentation. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2013. [DOI: 10.1080/21681163.2013.765709] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Abstract
Quantitative evaluation of cardiac function from cardiac magnetic resonance (CMR) images requires the identification of the myocardial walls. This generally requires the clinician to view the image and interactively trace the contours. Especially, detection of myocardial walls of left ventricle is a difficult task in CMR images that are obtained from subjects having serious diseases. An approach to automated outlining the left ventricular contour is proposed. In order to segment the left ventricle, in this paper, a combination of two approaches is suggested. Difference of Gaussian weighting function (DoG) is newly introduced in random walk approach for blood pool (inner contour) extraction. The myocardial wall (outer contour) is segmented out by a modified active contour method that takes blood pool boundary as the initial contour. Promising experimental results in CMR images demonstrate the potentials of our approach.
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Affiliation(s)
- Sarada Prasad Dakua
- Department of Electronics and Communication Engineering, Indian Institute of Technology, Guwahati, India.
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Dakua SP, Sahambi JS. Automatic left ventricular contour extraction from cardiac magnetic resonance images using cantilever beam and random walk approach. ACTA ACUST UNITED AC 2010; 10:30-43. [PMID: 20082140 DOI: 10.1007/s10558-009-9091-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Heart failure is a well-known debilitating disease. From clinical point of view, segmentation of left ventricle (LV) is important in a cardiac magnetic resonance (CMR) image. Accurate parameters are desired for better diagnosis. Proper and fast image segmentation of LV is of paramount importance prior to estimation of these parameters. We prefer random walk approach over other existing techniques due to two of its advantages: (1) robustness to noise and, (2) it does not require any special condition to work. Performance of the method solely depends on the selection of initial seed and parameter β. Problems arise while applying this method to different kind of CMR images bearing different ischemia. It is due due to their implicit geometry definitions unlike general images, where the boundary of LV in the image is not available in an explicit form. This type of images bear multi-labeled LV and the manual seed selection in these images introduces variability in the results. In view of this, the paper presents two modifications in the algorithm: (1) automatic seed selection and, (2) automatic estimation of β from the image. The highlight of our method is its ability to succeed with minimum number of initial seeds.
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Affiliation(s)
- Sarada Prasad Dakua
- Department of Electronics and Communication Engineering,Indian Institute of Technology, Guwahati, India.
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