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dc.contributor.advisorBasnet, Purusotam
dc.contributor.authorPopova, Daria
dc.date.accessioned2021-09-22T08:40:54Z
dc.date.available2021-09-22T08:40:54Z
dc.date.issued2021-10-22
dc.description.abstractDeclined fertility rate and population is a matter of serious concern, especially in the developed nations. Assisted Reproductive Technologies (ART), including in vitro fertilization (IVF), have provided great hope for infertility treatment and maintaining population growth and social structure. With the help of ART, more than 8 million babies have already been born so far. Despite the worldwide expansion of ART, there is a number of open questions on the IVF success rates. Male factors for infertility contribute equally as female factors, however, male infertility is primarily focused on the “semen quality”. Therefore, the search of new semen parameters for male fertility evaluation and the exploration of the optimal method of sperm selection in IVF have been included among the top 10 research priorities for male infertility and medically assisted reproduction. The development of imaging systems coupled with image processing by Artificial Intelligence (AI) could be the revolutionary step for semen quality analysis and sperm cell selection in IVF procedures. For this work, we applied optical nanoscopy technology for the analysis of human spermatozoa, i.e., label-based Structured Illumination Microscopy (SIM) and non-invasive Quantitative Phase Microscopy (QPM). The SIM results demonstrated a prominent contrast and resolution enhancement for subcellular structures of living sperm cells, especially for mitochondria-containing midpiece, where features around 100 nm length-scale were resolved. Further, non-labeled QPM combined with machine learning technique revealed the association between gradual progressive motility loss and the morphology changes of the sperm head after external exposure to various concentrations of hydrogen peroxide. Moreover, to recognize healthy and stress-affected sperm cells, we applied Deep Neural Networks (DNNs) to QPM images achieving an accuracy of 85.6% on a dataset of 10,163 interferometric images of sperm cells. Additionally, we summarized the evidence from published literature regarding the association between mitochondrial DNA copy numbers (mtDNAcn) and semen quality. To conclude, we set up the high-resolution imaging of living human sperm cells with a remarkable level of subcellular structural details provided by SIM. Next, the morphological changes of sperm heads resulting from peroxidation have been revealed by QPM, which may not be explored by microscopy currently used in IVF settings. Besides, the implementation of DNNs for QPM image processing appears to be a promising tool in the automated classification and selection of sperm cells during IVF procedures. Moreover, the results of our meta-analysis showed an association of mtDNAcn in human sperm cells and semen quality, which seems to be a relevant sperm parameter for routine clinical practice in male fertility assessment.en_US
dc.description.doctoraltypeph.d.en_US
dc.description.popularabstractMale infertility is a significant reproductive health concern. Assisted Reproductive Technologies (ART) have provided great hope for male infertility treatment, but the success rate is still relatively low. The search of new semen parameters for male fertility evaluation and the exploration of the optimal method of sperm selection is one of the research priorities to improve ART outcomes. The label-free Quantitative Phase Microscopy (QPM) coupled with image processing by Artificial Intelligence (AI) enabled us to detect tiny morphologic changes of spermatozoa under oxidative stress and recognize between healthy and stress-affected sperm cells. QPM-AI framework appears to be a promising tool in the automated classification and selection of sperm cells during ART procedures. Moreover, we set up the high-resolution imaging of living spermatozoa using label-based Structured Illumination Microscopy. This technique shows great promise for shedding new light on the detailed understanding of sperm cell functioning. In addition, we summarized the evidence from published literature regarding the association between semen quality and mitochondrial DNA copy numbers, which seems to be a relevant parameter in male fertility assessment.en_US
dc.description.sponsorshipThe UiT Tematiske satsinger programen_US
dc.identifier.urihttps://hdl.handle.net/10037/22598
dc.language.isoengen_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.relation.haspart<p>Paper I: Opstad, I.S., Popova, D.A., Acharya, G., Basnet, P. & Ahluwalia, B.S. (2018). Live-cell imaging of human spermatozoa using structured illumination microscopy. <i>Biomedical Optics Express, 9</i>(12), 5939-5945. Also available in Munin at <a href=https://hdl.handle.net/10037/15036>https://hdl.handle.net/10037/15036</a>. <p>Paper II: Dubey, V., Popova, D., Ahmad, A., Acharya, G., Basnet, P., Mehta, D.S.I. & Ahluwalia, B.S. (2019). Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition. <i>Scientific Reports, 9</i>, 3564. Also available in Munin at <a href=https://hdl.handle.net/10037/17194>https://hdl.handle.net/10037/17194</a>. <p>Paper III: Butola, A., Popova, D., Prasad, D.K., Ahmad, A., Habib, A., Tinguely, J.C., … Ahluwalia, B.S. (2020). High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition. <i>Scientific Reports, 10</i>, 13118. Also available in Munin at <a href=https://hdl.handle.net/10037/20415>https://hdl.handle.net/10037/20415</a>. <p>Paper IV: Popova, D., Bhide, P., D’Antonio, F., Basnet, P. & Acharya, G. (2021). Sperm mitochondrial DNA copy numbers in normal and abnormal semen analysis: a systematic review and meta-analysis. (Submitted manuscript).en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)
dc.subject.courseIDDOKTOR-003
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Gynecology and obstetrics: 756en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Gynekologi og obstetrikk: 756en_US
dc.titleAdvanced methods in reproductive medicine: Application of optical nanoscopy, artificial intelligence-assisted quantitative phase microscopy and mitochondrial DNA copy numbers to assess human sperm cellsen_US
dc.typeDoctoral thesisen_US
dc.typeDoktorgradsavhandlingen_US


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