15 research outputs found
Medical image registration using unsupervised deep neural network: A scoping literature review
In medicine, image registration is vital in image-guided interventions and
other clinical applications. However, it is a difficult subject to be addressed
which by the advent of machine learning, there have been considerable progress
in algorithmic performance has recently been achieved for medical image
registration in this area. The implementation of deep neural networks provides
an opportunity for some medical applications such as conducting image
registration in less time with high accuracy, playing a key role in countering
tumors during the operation. The current study presents a comprehensive scoping
review on the state-of-the-art literature of medical image registration studies
based on unsupervised deep neural networks is conducted, encompassing all the
related studies published in this field to this date. Here, we have tried to
summarize the latest developments and applications of unsupervised deep
learning-based registration methods in the medical field. Fundamental and main
concepts, techniques, statistical analysis from different viewpoints,
novelties, and future directions are elaborately discussed and conveyed in the
current comprehensive scoping review. Besides, this review hopes to help those
active readers, who are riveted by this field, achieve deep insight into this
exciting field
Genetic determinants of premature menopause in A Mashhad population cohort
Funding Information: This work was supported by a grant from the Vice Chancellor for Research at Mashhad University of Medical Sciences and this was a part of the Ph.D. student dissertation (no. 971084). The authors have no conflict of interest to disclose.Peer reviewedPublisher PD
The Dilemma of TP53 Codon 72 Polymorphism (rs1042522) and Breast Cancer Risk : A Case-Control Study and Meta-Analysis in The Iranian Population
The authors would like to thank all participants in this research. We would also like to thank Mashhad University of Medical Sciences and Omid Hospital (Mashhad, Iran) for their support to the project. This work was financially supported by Mashhad University of Medical Sciences under Grant No. 930891. No potential conflict of interest was reported by the authors.Peer reviewedPublisher PD
In silico analysis of the substitution mutations and evolutionary trends of the SARS-CoV-2 structural proteins in Asia
OBJECTIVES: To address a highly mutable pathogen, mutations must be evaluated. SARS-CoV-2 involves changing infectivity, mortality, and treatment and vaccination susceptibility resulting from mutations. MATERIALS AND METHODS: We investigated the Asian and worldwide samples of amino-acid sequences (AASs) for envelope (E), membrane (M), nucleocapsid (N), and spike (S) proteins from the announcement of the new coronavirus 2019 (COVID-19) up to January 2022. Sequence alignment to the Wuhan-2019 virus permits tracking mutations in Asian and global samples. Furthermore, we explored the evolutionary tendencies of structural protein mutations and compared the results between Asia and the globe. RESULTS: The mutation analyses indicated that 5.81%, 70.63%, 26.59%, and 3.36% of Asian S, E, M, and N samples did not display any mutation. Additionally, the most relative mutations among the S, E, M, and N AASs occurred in the regions of 508 to 635 AA, 7 to 14 AA, 66 to 88 AA, and 164 to 205 AA in both Asian and total samples. D614G, T9I, I82T, and R203M were inferred as the most frequent mutations in S, E, M, and N AASs. Timeline research showed that substitution mutation in the location of 614 among Asian and total S AASs was detected from January 2020. CONCLUSION: N protein was the most non-conserved protein, and the most prevalent mutations in S, E, M, and N AASs were D614G, T9I, I82T, and R203M. Screening structural protein mutations is a robust approach for developing drugs, vaccines, and more specific diagnostic tools
Role of aldoāketo reductase family 1 member B1 (AKR1B1) in the cancer process and its therapeutic potential
Treatment of non-healing sternum wound after open-heart surgery with allogenic platelet-rich plasma and fibrin glue-preliminary outcomes
Introduction: Non-healing wound in the sternal region after coronary arteries bypass graft surgery is a serious complication. For healing a chronic wound, several novel approaches have been proposed recently such as using bone marrow stem cells, platelets and fibrin glue (PFG); but a non-invasive method is highly desirable in the first approach for treatment. The current study was undertaken to evaluate the effect of the combination of PFG in one treatment. Materials and Methods: We report on the treatment of six patients with life-threatening chronic sternum wounds, which caused septicemia with multi-drug resistant pathogens. The ulcers were extensively debrided initially and were measured and photographed at weekly intervals. The combination of PFG was applied topically on the wound after every 2 days. Results: The wounds were completely closed in five patients and significantly reduced in size in one. There was no evidence of local or systemic complications and any abnormal tissue formation, keloid or hypertrophic scarring. Conclusions: Our study suggests, in the first approach, PFG can be used safely in order to heal a non healing sternum wound following coronary artery bypass surgery