Functorial relationships between $QH* (G/B)$ and $QH* (G/P)$, II

Objective: Infertility is defined as absence of a healthy baby or pregnancy despite a one-year regular unprotected sexual intercourse. While primary infertility is called the absence of pregnancy at all, secondary infertility is called inability of couples who have had at least one healthy child to become pregnant. Chromosome anomalies are an important cause of both male and female infertility. In this study, we aimed to discuss the results of chromosome analysis of couples with primary infertility. Material and Methods: A total of 535 people, 262 women and 273 spouses with primary infertility, were included in the study. Chromosome analysis was performed using standard cytogenetic GTG banding technique using peripheral blood lymphocytes.Results: Normal chromosome establishment was detected in 513 people, 46,XX in 254 people, 46,XY in 259 people (95.8%). Eight patients had a normal chromosome variant (1.4%). In addition, translocation was detected in 5 patients (0.8%); in 4 patients, balanced reciprocal translocation and in 1 patient, Robertsonian translocation. Klinefelter syndrome was detected in 3 patients (1% of male infertility). In addition, we detected Turner syndrome variants, mos 45,X[11]/46,XX,i (X)(q10)[29] and mos 45,X[10]/46,X,i(X)(q10)[10] (0.7% of female infertility) in two cases. If normal chromosome variants are excluded, the frequency of the remaining changes in the patient population is 2.6%. Conclusion: Structural chromosomal anomalies such as balanced reciprocal and Robertsonian translocations cause both female and male infertility. 47,XXY should be kept in mind for male infertility and 45,X/46,X,i(X)(q10) Turner chromosome variants should be kept in mind in female infertility. In summary, chromosome analysis is one of the important tests that should be done to explain the etiology of both male and female infertility.

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“…Klinefelter syndrome was the most common reason of male infertility (1%). This result was in parallel with the literature (15,16).…”

Section: Discussionsupporting

confidence: 90%

The Role of Chromosome Analysis in Male and Female Infertility

Balta

Erdoğan

Kiraz

et al. 2021

Kırıkkale Üniversitesi Tıp Fakültesi Dergisi

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“…The synthesized CHA nanorods were characterized by various analytical measurements to investigate how changes of carbonate levels affect the crystal morphology and structure of CHA nanorods. The effects of synthesized samples on the viability and osteogenic differentiation of the human osteosarcoma MG-63 cells have been measured by an MTT method and alkaline phosphate activity assay [18,19]. …”

mentioning

confidence: 99%

Hydrothermal Synthesis and Biocompatibility Study of Highly Crystalline Carbonated Hydroxyapatite Nanorods

Xue¹,

Chen²,

Huang³

et al. 2016

Nano Online

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(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Highly crystalline carbonated hydroxyapatite (CHA) nanorods with different carbonate contents were synthesized by a novel hydrothermal method. The crystallinity and chemical structure of synthesized nanorods were studied by Fourier transform infrared spectroscopy (FTIR), X-ray photo-electronic spectroscopy (XPS), X-ray diffraction (XRD), Raman spectroscopy, and transmission electron microscopy (TEM). The biocompatibility of synthesized CHA nanorods was evaluated by cell viability and alkaline phosphatase (ALP) activity of MG-63 cell line. The biocompatibility evaluation results show that these CHA nanorods are biologically active apatites and potentially promising bone-substitute biomaterials for orthopedic application.Keywords: Hydroxyapatite; Carbonated hydroxyapatite; Nanorods; Cell viability; Alkaline phosphatase activity; MG-63 cell BackgroundHydroxyapatite (HA) is the main inorganic component of human bone mineral, and the content of carbonate in human bone mineral is about 5-8 wt % [1, 2]. Carbonate ions in carbonated hydroxyapatites (CHA) substitute both the phosphate and hydroxyl sites of the HA structure and each is called A-type CHA and B-type CHA, respectively. Predominantly, the carbonate ions are present as B-type carbonates in natural bone minerals [3]. Synthetic CHAs have been widely used in a variety of biomedical applications including osteoconductive coatings [4][5][6], bone-substitute biomaterials [7], and vehicles for drug delivery [ 8]. Recently, hydoxyapatite nanorods have been prepared by an ethanol-induced method [9], liquid crystals [10], sonochemical synthesis [11], sol-gel method [12], and hydrothermal reaction [13,14]. However, few methods have been reported for the preparation of carbonated hydroxyapatite nanorods with different carbonate contents. Since carbonate ion substitution in the apaptite lattice plays a major role in the biochemistry and physical properties of biological apatites, it is important to develop convenient ways for the synthesis of CHA nanorods with different carbonate contents and understand how various carbonate contents affect the crystal structure and biocompatibility of CHA nanorods. Table 1 Synthesizing materials for preparing HA and CHA nanorods SamplesCaThe hydrothermal method is a typical process which has been widely used in synthesis of inorganic materials for its good repeatability and crystallinity control [15][16][17]. In this study, we developed a hydrothermal process to synthesize carbonated hydroxyapatite nanorods with different carbonate contents, using ethylene diamine tetraacetic aci...

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“…57 In addition, we are interested in comparing the predictive power of our AGL-Score with various state-of-the-art scoring functions introduced in the literature. 36,53,58,63,[76][77][78] Fig. 3a illustrates such a comparison.…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

“…The proposed algebraic graph learning based scoring function, AGL-Score, is plotted in the red color. The results of other methods, taken from Refs., 36,53,58,63,[76][77][78] are in the blue color. Performance comparison of different scoring functions on CASF-2013 benchmark, a) the scoring power measured by Pearson correlation coefficient; b) the ranking power evaluated by the high-level success measurement, and c) the docking power in term of the rate of successfully identifying the "native" pose from 100 poses for each ligand.…”

Section: Supplementary Materialsmentioning

confidence: 99%

AGL-Score: Algebraic Graph Learning Score for Protein–Ligand Binding Scoring, Ranking, Docking, and Screening

Nguyen

Wei

2019

J. Chem. Inf. Model.

186

165

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Although algebraic graph theory based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties, confirming the common belief that “one cannot hear the shape of a drum”. A new development on this century old issue about spectrum-geometry relationship is provided. Novel algebraic graph learning score (AGL-Score) models are proposed to encode high-dimensional physical and biological information into intrinsically low-dimensional representations. The proposed AGL-Score models employ multiscale weighted colored subgraphs to describe crucial molecular and biomolecular interactions in terms of graph invariants derived from graph Laplacian, its pseudo-inverse, and adjacency matrices. Additionally, AGL-Score models are integrated with an advanced machine learning algorithm to predict biomolecular macroscopic properties from the low-dimensional graph representation of biomolecular structures. The proposed AGL-Score models are extensively validated for their scoring power, ranking power, docking power, and screening power via a number of benchmark datasets, namely CASF-2007, CASF-2013, and CASF-2016. Numerical results indicate that the proposed AGL-Score models are able to outperform other state-of-the-art scoring functions in protein-ligand binding scoring, ranking, docking, and screening. This study indicates that machine learning methods are powerful tools for molecular docking and virtual screening. It also indicates that spectral geometry or spectral graph theory has the ability to infer geometric properties.

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Functorial relationships between $QH* (G/B)$ and $QH* (G/P)$, II

Cited by 7 publications

References 20 publications

The Role of Chromosome Analysis in Male and Female Infertility

The Role of Chromosome Analysis in Male and Female Infertility

Hydrothermal Synthesis and Biocompatibility Study of Highly Crystalline Carbonated Hydroxyapatite Nanorods

AGL-Score: Algebraic Graph Learning Score for Protein–Ligand Binding Scoring, Ranking, Docking, and Screening

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