• Animal Bioscience
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• v.34 no.8
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• pp.1253-1270
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• 2021

Assessment of male fertility is based on the evaluation of sperm. Semen evaluation measures various sperm quality parameters as fertility indicators. However, semen evaluation has limitations, and it requires the advancement and application of strict quality control methods to interpret the results. This article reviews the recent advances in evaluating various sperm-specific quality characteristics and methodologies, with the help of different assays to assess sperm-fertility status. Sperm evaluation methods that include conventional microscopic methods, computer-assisted sperm analyzers (CASA), and flow cytometric analysis, provide precise information related to sperm morphology and function. Moreover, profiling fertility-related biomarkers in sperm or seminal plasma can be helpful in predicting fertility. Identification of different sperm proteins and diagnosis of DNA damage has positively contributed to the existing pool of knowledge about sperm physiology and molecular anomalies associated with different infertility issues in males. Advances in methods and sperm-specific evaluation has subsequently resulted in a better understanding of sperm biology that has improved the diagnosis and clinical management of male factor infertility. Accurate sperm evaluation is of paramount importance in the application of artificial insemination and assisted reproductive technology. However, no single test can precisely determine fertility; the selection of an appropriate test or a set of tests and parameters is required to accurately determine the fertility of specific animal species. Therefore, a need to further calibrate the CASA and advance the gene expression tests is recommended for faster and field-level applications.

• Clinical and Experimental Reproductive Medicine
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• v.45 no.2
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• pp.88-93
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• 2018

Objective: The aim of this study was to measure reactive oxygen species (ROS) production and total antioxidant capacity (TAC) in the seminal fluid of the male partners in couples undergoing intrauterine insemination and to evaluate correlations between these values and their semen parameters. Methods: The study was conducted at Vamsam Fertility Center, Coimbatore, India and enrolled 110 male patients from whom semen samples were collected. ROS production was measured by a thiobarbituric acid reactive species assay, and TAC was measured by a 2,2-diphenyl-2-picrylhydrazyl free radical assay. The differences in the TAC and malondialdehyde (MDA) levels between the subfertile and fertile groups were analysed. Correlations between sperm parameters and TAC and MDA levels were statistically analysed, and cutoff values with respect to the controls were determined. All hypothesis tests used were two-tailed, with statistical significance assessed at the level of p< 0.05. Results: A total of 87 subfertile and 23 fertile men were included in the study. The mean MDA level was significantly higher in the subfertile subjects than in the fertile subjects, and the mean antioxidant level was significantly lower in the subfertile subjects than in the fertile subjects. Seminal MDA levels were negatively associated with sperm concentration, motility, and morphology, whereas the opposite was seen with TAC levels. Conclusion: Measurements of seminal TAC and ROS are valuable for predicting semen quality, and hence predicting the outcomes of fertility treatment.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.229-263
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• 2019

The Korean fertility rate has been declining rapidly since 2000 with the fertility rate among provinces following a uniform tendency. In particular, the province-specific fertility rate is an essential tool for local governments to prepare local policies for low fertility aging policy, education and welfare policies. However, there is limitation on how to reflect different trends on the province-specific fertility rate because the KOSTAT's (2017) province-specific fertility rate projection estimates information use the national average birth rate date of vital statistics for the last 10 years (5 years). In this study, we propose an improvement plan that simultaneously considers important stable pattern maintenance and provincial fertility rate differentiation for an annual birth rate estimation. The method proposed in this study (proposal 1 and 2) can reflect birth rate changes from past to present and national and provincial differences by age that use time series data of the annual fertility rate. Proposal 3 also reflects the unique fertility rate trend from the past to the present by age according to province regardless of the relationship with the national trend. Therefore, it is preferable to use a relationship to the national rate when predicting the birth rate, as in proposals 1 and 2 because the national and the provincial fertility rate pattern are similar. These proposals show improved stability in terms of age-specific fertility rates.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.6
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• pp.842-850
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• 2018

Objective: Several studies have reported the development of new molecular methods for the prognosis and diagnosis of male fertility based on biomarkers aimed at overcoming the limitations of conventional male fertility analysis tools. However, further studies are needed for the field application of these methods. Therefore, alternative methods based on existing semen analysis methods are required to improve production efficiency in the animal industry. Methods: we examined the possibility of improving litter size in various pig breeds using combined Hoechst 33258/chlortetracycline fluorescence (H33258/CTC) staining. The correlation between field fertility and capacitation status by combined H33258/CTC staining in different ejaculates spermatozoa (n = 3) from an individual boar (20 Landrace, 20 Yorkshire, and 20 Duroc) was evaluated as well as overall accuracy. Results: The acrosome reacted (AR) pattern after capacitation (%) was positively correlated with the litter size of Landrace, Yorkshire, and Duroc pigs and the overall accuracy was 75%, 75%, and 70% in Landrace, Yorkshire, and Duroc pigs, respectively. The difference (${\Delta}$) in AR pattern before and after capacitation was positively correlated with the litter size of Landrace, Yorkshire, and Duroc pigs and the overall accuracy was 80%, 65%, and 55% in Landrace, Yorkshire, and Duroc pigs, respectively. However, the difference (${\Delta}$) in capacitated (B) pattern before and after capacitation was negatively correlated with the litter size of Landrace pigs and the overall accuracy was 75%. Moreover, average litter size was significantly altered according to different combined H33258/CTC staining parameters. Conclusion: These results show that combined H33258/CTC staining may be used to predict male fertility in various breeds. However, the selection of specific efficiency combined H33258/CTC staining parameters requires further consideration. Taken together, these findings suggest that combined H33258/CTC staining may constitute an alternative method for predicting male fertility until such time as fertility-related biomarkers are further validated.

• Clinical and Experimental Reproductive Medicine
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• v.22 no.2
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• pp.131-141
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• 1995

This study was designed to determine the relationship between sperm acrosome reaction following ionophore challenge(ARIC) and hamster ovum sperm penetration assay(SPA) as assessment of fertilizing capacity of male. ARIC test and SPA were performed in 23 fertile and 19 subfertile men. The results were as follows; Sperm concentration was significantly higher in fertile group compared with subfertile group: $114.6{\pm}64.40$ vs $61.3{\pm}46.50{\times}10^6/ml$. However, there were no significantly differences in seminal volume, motility and motility index, respectively. There was a significantly correlation between spontaneous and induced AR in fertile and subfertile group, respectively. ARIC value was significantly higher in fertile group, compared with subfertile group: $12.0{\pm}5.57%$ vs $2.6{\pm}4.96%$. Both Penetration rate(PR) and Penetration index(PI) were significantly higher in fertile group, compared with subfertile group: $97.4{\pm}7.40%$ vs $64.9{\pm}36$. 20% and $5.4{\pm}2.88$ vs $1.5{\pm}1.47$, respectively. The Positive predictive value(PPV), Negative predictive value(NPV), sensitivity and specificity of ARIC test (cut-off: 8.5) and SPA(PI cut-off : 3.0) in predicting fertility were 95.0%, 81.8%, 82.6%, 94.7% and 95.2%, 85.7%, 87.0% and 94.7%, respectively. There was no significantly difference in predicting fertility between ARIC test and SPA. In conclusion, ARIC test was shown to have a predictive value for fertilizing capacity comparable to that of the hamster ovum sperm penetration assay. Therefore, ARIC test may be a simple and cost-effective addition to existing semenology instead of SPA.

• Clinical and Experimental Reproductive Medicine
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• v.43 no.2
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• pp.126-132
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• 2016

Objective: The purpose of this study was to identify useful clinical factors for the identification of patients with polycystic ovary syndrome (PCOS) who would benefit from in vitro maturation (IVM) treatment without exhibiting compromised pregnancy outcomes. Methods: A retrospective cohort study was performed of 186 consecutive patients with PCOS who underwent human chorionic gonadotropin-primed IVM treatment between March 2010 and March 2014. Only the first IVM cycle of each patient was included in this study. A retrospective case-control study was subsequently conducted to compare pregnancy outcomes between IVM and conventional in vitro fertilization (IVF) cycles. Results: Through logistic regression analyses, we arrived at the novel finding that serum $anti-M{\ddot{u}}llerian$ hormone (AMH) levels and the number of fertilized oocytes in IVM were independent predictive factors for live birth with unstandardized coefficients of 0.078 (95% confidence interval [CI], 1.005-1.164; p=0.037) and 0.113 (95% CI, 1.038-1.208; p=0.003), respectively. Furthermore, these two parameters were able to discriminate patients who experienced live births from non-pregnant IVM patients using cut-off levels of 8.5 ng/mL and five fertilized oocytes, respectively. A subsequent retrospective case-control study of patients with PCOS who had serum AMH levels ${\geq}8.5ng/mL$ showed that IVM had pregnancy outcomes comparable to conventional IVF, and that no cases of ovarian hyperstimulation syndrome were observed. Conclusion: Serum AMH levels are a useful factor for predicting pregnancy outcomes in PCOS patients before the beginning of an IVM cycle. IVM may be an alternative to conventional IVF for PCOS patients if the patients are properly selected according to predictive factors such as serum AMH levels.

• Clinical and Experimental Reproductive Medicine
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• v.43 no.3
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• pp.139-145
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• 2016

Measurements of ovarian reserve play an important role in predicting the clinical results of assisted reproductive technology (ART). The ideal markers of ovarian reserve for clinical applications should have high specificity in order to determine genuine poor responders. Basal follicle-stimulating hormone levels, antral follicle count, and serum anti-$M{\ddot{u}}llerian$ hormone (AMH) levels have been suggested as ovarian reserve tests that may fulfill this requirement, with serum AMH levels being the most promising parameter. Serum AMH levels have been suggested to be a predictor of clinical pregnancy in ART for older women, who are at a high risk for decreased ovarian response. We reviewed the prognostic significance of ovarian reserve tests for patients undergoing ART treatment, with a particular focus on the significance of serum AMH levels in patients at a high risk of poor ovarian response.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.1
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• pp.15-23
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• 1979

In the first paper of the series the five soil fertility factors were evaluated by means of principal component analysis and varimax method. They are interpreted as representing, 1) skeletal available phosporus status, 2) organnic matter status, 3) salt status 4) base status, and 5) free oxide status. In order to resynthesize such fragmented information for the overall soil fertility evaluation, the method of multiple regression analysis was adopted, using the five factor scores and yield data for Korean paddy soils as independent and dependent variables respectively. As test of linear models with different combinations of independent variables the results of t-test of regression coefficient were revealed that the organic matter status (FII) has no relevance to the yield of paddy and that the free oxides and salt supply has by it self only an insignificant contribution to the yield. The multiple correlation coefficient (R) revealed its multiple regression analysis was as low as 0.43. Introduction of quadratic terms to the linear model bettered the result. Thus multiple correlation coefficient (R) was increased as 0.59. Therefore, a coefficient of determination 0.35 was obtained by a quadratic model with interaction terms among the five fertility constituents. Generally we think that the fertility factor has more contribution to raise the rice yield in paddy and that the failure of yield prediction by fertility factor scores was caused by one of follows; 1) the roughness of the yield inspection, and 2) missextraction of fertility constituents. The second step in this study, assuming that the residuals by multiple regression analysis were due to factors other than soil fertility, we can now proceed to predicting the yield from the field characters with the classified fertility groups by means of Hayashi's theory of quantification No. 1. Such variables as fertility groups (FTYG), water availability (WATER), soil drainage (DRNG), climatic zone (CLIZ), surface soil's stickiness (STCKT), surface soil's dry consistence (DCNST), and surface soil's texture (FTEXT) are taken up as the explanatory variables. The quantification appears reasonable; the well to extremely well in soil drainage, very sticky of surface soil, inefficiency in water availability, coarse texture, and very hard to extremely hard dry consistence in soil are detrimental to the rice yield. The R was as high as 0.90 for the set of variables. But the given explanatory variables in this study were not quite effective in explaining rice yield. The method developed seems to be promising only if properly collected data are available. Conditions that should be satisfied in the yield inspection obtained from common cultivator for the purpose of deriving a prediction equation were put forward.

• Informatization Policy
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• v.26 no.4
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• pp.20-35
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• 2019

We empirically analyze the effects of psychological factors, such as the fear of parenting, on fertility rates. An index is calculated based on the share of negative news articles on child care in all social articles from 2000 to 2018. The analysis result shows that as the index increases, the fertility rate after three years falls. This result is repeated in the correlation analysis, simple regression, and VAR analysis. According to Granger causality analysis, it is found that the relation between the index and the fertility rate after three years is not just a simple correlation but a causal relationship. There are differences among age groups. The fertility rate of women in their 20s and 30s shows a significant response to the index, but that of the 40s does not. The index affects the birthrate of first child, but do not affect the birthrate of second or more children. These results are consistent with the intuition that younger women are more likely to be affected by the negative articles about parenting, but not to those who have already experienced childbirth. This study is meaningful in that a significant index for predicting social phenomena is extracted beyond the limited use of news big data such as a simple keyword mention volume monitoring. Also, this big data-based index is a 3-year leading indicator for fertility, which provides the advantage of providing information that helps early detection.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.