Total Progressively Motile Sperm Cell Count as a Predictor of Fertilization Failure
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Abstract Objectives:This study aimed to investigate the differences in clinical parameters between cycles with normal in vitro fertilization (IVF) fertilization rates and those with low fertilization rates requiring rescue intracytoplasmic sperm injection (R-ICSI). Methods:A retrospective analysis was conducted on clinical data from 606 IVF cycles and 97 R-ICSI cycles from January 2019 to August 2024. Non-parametric tests, logistic regression analysis, and ROC curves were employed to analyze the relationship between the data and identify the most influential indicators of fertilization failure. Results:Significant differences were observed between the two groups in female age, male age, female BMI, type of infertility, years of infertility, sperm concentration, number of forward motile spermatozoa, PR, IM, DFI, and acrosomal enzyme activity (P < 0.05). Following R-ICSI, the normal fertilization rate increased from 17.16% (150/874) to 61.78% (540/874), significantly different from the 67.08% (3715/5538) observed in the IVF group (P < 0.05). The area under the curve (AUC) for female BMI, DFI, and total progressively motile sperm cell count (TPMC) were 0.575, 0.617, and 0.703, respectively, with Youden indices of 0.13, 0.21, and 0.31. Conclusions:The results suggest that TPMC is the most effective predictor of fertilization failure.
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Total Progressively Motile Sperm Cell Count as a Predictor of Fertilization Failure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Total Progressively Motile Sperm Cell Count as a Predictor of Fertilization Failure Hai Wang, Zitong Xu, Xianjue Zheng, Jiayong Zheng, Shuqi Xia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5924111/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: This study aimed to investigate the differences in clinical parameters between cycles with normal in vitro fertilization (IVF) fertilization rates and those with low fertilization rates requiring rescue intracytoplasmic sperm injection (R-ICSI). Methods: A retrospective analysis was conducted on clinical data from 606 IVF cycles and 97 R-ICSI cycles from January 2019 to August 2024. Non-parametric tests, logistic regression analysis, and ROC curves were employed to analyze the relationship between the data and identify the most influential indicators of fertilization failure. Results: Significant differences were observed between the two groups in female age, male age, female BMI, type of infertility, years of infertility, sperm concentration, number of forward motile spermatozoa, PR, IM, DFI, and acrosomal enzyme activity (P < 0.05). Following R-ICSI, the normal fertilization rate increased from 17.16% (150/874) to 61.78% (540/874), significantly different from the 67.08% (3715/5538) observed in the IVF group (P < 0.05). The area under the curve (AUC) for female BMI, DFI, and total progressively motile sperm cell count (TPMC) were 0.575, 0.617, and 0.703, respectively, with Youden indices of 0.13, 0.21, and 0.31. Conclusions: The results suggest that TPMC is the most effective predictor of fertilization failure. Health sciences/Medical research/Outcomes research Health sciences/Medical research/Experimental models of disease IVF R-ICSI fertilization rate total progressively motile sperm cell count Figures Figure 1 Background In vitro fertilization (IVF) is the most widely used assisted reproductive technology (ART), primarily suitable for patients with female factors (e.g., ovulation disorders, tubal anomalies, endometriosis, and cervical factors), male factors (e.g., mild to moderate sperm abnormalities), and unexplained infertility. The typical IVF fertilization rate ranges from 60% to 80%, but low fertilization rates (LFR, <25%) occur in approximately 20% of cases, with total fertilization failure (TFF) occurring in 5%-15% of cases [1,2]. To mitigate these issues, early assessment of fertilization is crucial. Degranulation of granulocytes is performed 4-6 hours post-IVF insemination to observe the expulsion of the second polar body, and early rescue intracytoplasmic sperm injection (R-ICSI) is implemented in cycles with fertilization rates below 30% [4]. The causes of low fertilization rates are multifactorial [5]. This study retrospectively analyzed clinical parameters from IVF and R-ICSI cycles at our center to identify more reliable predictors of fertilization success. Materials and methods A retrospective analysis was conducted on clinical data from 606 IVF cycles and 97 R-ICSI cycles from January 2019 to August 2024. Data collected included female age, male age, female BMI, baseline sex hormone levels (E2, LH, FSH, AMH), hepatitis B virus surface antigen (HBsAg), occupation, education, years of infertility, infertility factors, type of infertility, ovulation regimen, gonadotropin (Gn) usage, and days of Gn use. Hormonal profiles on the trigger day (E2, LH, progesterone) and male semen parameters (semen volume, sperm concentration, PR, IM, NP, acrosomal enzyme activity, and DFI) were also recorded. Non-parametric tests, logistic regression analysis, and ROC curves were used to analyze the data. Statistical analyses were performed using SPSS 22.0, with normality assessed via chi-square tests. Data conforming to a normal distribution were expressed as mean ± standard deviation (x̄ ± s), while non-normally distributed data were expressed as median (quartile) [M(P25, P75)]. The Mann-Whitney U test was used for group comparisons, and logistic regression analysis was performed to identify significant predictors. ROC curves were plotted using Python 3.31. This retrospective study utilized anonymized data generated from routine clinical practice at our reproductive medicine center. The research protocol was approved by the Institutional Review Board of Wenzhou People’s Hospital (Approval No.KY-202501-010), and all procedures complied with the Ethical Guidelines for Human Assisted Reproductive Technology and the Declaration of Helsinki. We confirm that written informed consent encompassing potential research use was obtained from all participants during their initial IVF treatment, and all analyzed data were fully de-identified. In summary, as a pivotal assisted reproductive technology, in vitro fertilization (IVF) faces significant success rate limitations due to suboptimal fertilization rates. While extensive research has investigated factors contributing to fertilization failure, the field still lacks precise and universally validated predictive biomarkers. This study conducted a comprehensive analysis of 606 IVF cycles and 97 rescue ICSI (R-ICSI) cycles performed during January 2019 to August 2024, systematically investigating potential correlations between multifactorial clinical parameters and fertilization outcomes. Methodological approaches included non-parametric statistical testing, multivariate logistic regression modeling, and ROC curve analysis to ensure rigorous data interpretation. The subsequent presentation of findings will elucidate how these analytical results address the research questions outlined in the study background, ultimately establishing an evidence base for identifying robust predictors of fertilization failure. Results Comparison of Baseline Characteristics Significant differences were observed between the IVF and R-ICSI groups in female age, male age, female BMI, type of infertility, and years of infertility (P < 0.05). Details are provided in Table 1. Comparison of Ovulation Induction Protocols The ovulation promotion protocols employed in the two groups included the agonist protocol, antagonist protocol, ovarian stimulation under high progesterone conditions during the follicular phase, mild stimulation protocol, luteal phase ovulation induction, and natural cycle protocol. No statistically significant differences were observed between the two groups (P > 0.05). Additionally, there were no significant differences in key parameters such as gonadotropin (Gn) dosage, duration of Gn administration, or hormone levels on the trigger day (including E2, LH, and P) across the subgroups (P > 0.05). For detailed data, please refer to Table 2. Comparison of Semen Parameters No statistically significant differences were observed in semen volume or normal morphology (NP) between the two groups (P > 0.05). However, significant differences were noted in sperm concentration, forward motile sperm count, progressive motility (PR), immotility (IM), sperm DNA fragmentation index (DFI), and acrosomal enzyme activity (P 0.05). However, significant differences were observed in fertilization outcomes (normal fertilization, abnormal fertilization, and unfertilized oocytes) when no R-ICSI procedure was performed (P < 0.05). Following the R-ICSI procedure, the normal fertilization rate increased from 17.16% (150/874) to 61.78% (540/874), the abnormal fertilization rate increased from 2.97% (26/874) to 9.95% (87/874), and the unfertilized rate decreased from 79.86% (698/874) to 28.26% (247/874). Despite the substantial difference in sample sizes between the groups, the differences remained statistically significant (P < 0.05). Detailed results are presented in Table 4. Regression Analysis and ROC Curves Using the grouping criteria of this study as the dependent variable, covariates with P < 0.05 from Tables 1, 2, and 3 were included in the logistic regression equation. The analysis identified three significant indicators: female BMI, total forward-motile sperm count, and sperm DNA fragmentation index (DFI), all with P < 0.05 (Table 5). These three indicators were further analyzed using ROC curves (Figure 1). The area under the curve (AUC) for female BMI was 0.575, with a maximum Youden index of 0.13. The corresponding threshold was 21.8 kg/m², yielding a true positive rate of 67.01% and a false positive rate of 53.96%. The sperm DFI index had an AUC of 0.617, with a maximum Youden index of 0.21. The threshold was 11.88%, resulting in a true positive rate of 63.91% and a false positive rate of 43.07%.For the total forward-motile sperm count, the AUC was 0.703, with a maximum Youden index of 0.31. The optimal threshold was 42.0 × 10⁶ spermatozoa, corresponding to a true positive rate of 63.92% and a false positive rate of 35.31%. Detailed results are presented in Table 6. Table 1 Comparison of baseline conditions between the two groups of patients Items IVF group R-ICSI group χ2, U-value P-value Number 606 97 Female age(years) 34（30，38） 32（28，35.5） -2.878 0.004 Male age(years) 35（31，40） 34（31，37） -2.659 0.008 Female BMI(kg/㎡) 22.2（20.0，24.2） 22.9（20.8，25.7） 2.362 0.018 Type of infertility（%） 6.010 0.014 Primary infertility 31.68%（192/606） 44.33%（43/97） secondary infertility 68.32%（414/606） 55.67%（54/97） Infertility factors（%） 7.216 0.125 Ovulation factors 18.32%（111/606） 23.71%（23/97） Male factor 8.58%（52/606） 15.46%（15/97） Pelvic factor 57.59%（349/606） 47.42%（46/97） Factors on both sides 4.79%（29/606） 4.12%（4/97） Other factors 10.73%（65/606） 9.28%（9/97） Hepatitis status（%） 0.874 0.350 Positive 6.60%（40/606） 4.12%（4/97） Negatives 93.40%（566/606） 95.88%（93/97） Years of infertility 2（1，3） 2（1.5，3） 2.169 0.030 Occupation（%） 1.108 0.953 White collar worker 16.34%（99/606） 15.46%（15/97） Services and commerce 3.96%（24/606） 3.09%（3/97） Blue Collar and Manufacturing 8.75%（53/606） 11.34%（11/97） Specialized technical and knowledge-based 13.04%（79/606） 13.40%（13/97） Freelancing and entrepreneurship 52.48%（318/606） 52.58%（51/97） Other and unemployed 5.45%（33/606） 4.12%（4/97） Education attainment（%） 2.502 0.475 Junior high school ORbelow 45.71%（277/606） 42.27%（41/97） Junior college, vocational high school, high school 36.30%（220/606） 32.99%（32/97） College, undergraduate 17.16%（104/606） 23.71%（23/97） Master's degree or above 0.83%（5/606） 1.03%（1/97） AMH(ng/ml) 2.67（1.32，4.37） 2.90（1.81，4.71） 1.479 0.139 Basic FSH(mIU/ml) 6.87（5.56,8.45） 6.59（5.58,8.23） -0.391 0.696 Basic LH(mIU/ml) 4.58（3.15,6.77） 4.86（3.72,6.36） 1.402 0.161 Basic E2(pmol/L) 132.00（93.03,202.30） 134.60（101.90,196.68） 0.347 0.728 Table 2 Comparison of ovulation promotion in two groups of patients Items IVF group R-ICSI group χ2, U-value P-value Number 606 97 Ovulation program（%） 2.688 0.748 Agonist programs (ultra-long program, short-acting long program, modified follicular phase long program, luteal phase long program, follicular phase long program) 55.61%（337/606） 58.76%（57/97） Antagonist program 22.77%（138/606） 22.68%（22/97） Ovarian stimulation program in the hyperprogesterone state during the follicular phase 10.73%（65/606） 12.37%（12/97） Microstimulation 9.57%（58/606） 6.19%（6/97） Luteal phase ovulation 0.99%（6/606） 0.00%（0/97） Natural cycle 0.33%（2/606） 0.00%（0/97） Gn use（U） 2025（1575，2606） 2100（1750，2850） 1.924 0.054 Gn days（days） 11（9，12） 11（9，13） 0.932 0.351 Trigger day E2（pmol/L） 7670.00（4442.25,14833.08） 8492.65（5381.27,14909.25） 0.806 0.420 Trigger day LH（mIU/ml） 1.51（0.90,3.10） 1.33（0.68,2.89） -1.468 0.142 Trigger day P（nmol/L） 4.90（3.48,6.73） 4.90（3.25,6.50） -0.044 0.965 Table 3 Male semen examination status Items IVF group R-ICSI group U-value P-value Semen volume（ml） 2.5（2.0，3.0） 2.0（2.0，2.75） -1.261 0.207 sperm concentration（10 6 /ml） 70.0（50.0，92.5） 40.0（30.0，70.0） -6.374 0.000 TPMC（10 6 ） 60.0（32.4，98.1） 35.0（18.0，60.0） -6.427 0.000 PR（%） 40.0（30.0，50.0） 30.0（30.0，40.0） -4.191 0.000 NP（%） 20.0（10.0，20.0） 20.0（10.0，20.0） -0.942 0.346 IM（%） 40.0（40.0，50.0） 50.0（40.0，60.0） 4.626 0.000 DFI（%） 10.70（6.31，16.50） 14.30（9.35，20.59） 3.715 0.000 Sperm acrosomal enzyme activity（uIU/10 6 sperm） 76.10（52.80，106.23） 73.30（40.40，94.45） -2.117 0.034 Table 4 Egg acquisition and fertilization Items IVF group R-ICSI group U-value P-value Eggs acquired（%） 1.311 0.252 Mature eggs 88.23%（5538/6277） 86.97%（874/1005） Immature eggs 11.77%（739/6277） 13.03%（131/1005） Fertilization rate before R-ICSI(%) 1850.472 0.000 Normal fertilization 67.08%（3715/5538） 17.16%（150/874） Abnormal fertilization 18.91%（1047/5538） 2.97%（26/874） Unfertilized 14.01%（776/5538） 79.86%（698/874） Fertilization rate after R-ICSI(%) 133.451 0.000 Normal fertilization 67.08%（3715/5538） 61.78%（540/874） Abnormal fertilization 18.91%（1047/5538） 9.95%（87/874） Unfertilized 14.01%（776/5538） 28.26%（247/874） Table 5 Logistic regression equation analysis of influencing factors Predictor variable B S.E. Wald OR P 95%CI Female age -0.011 0.032 0.121 0.989 0.728 0.929-1.053 Male age -0.058 0.032 3.313 0.943 0.069 0.886-1.004 Female BMI 0.090 0.034 7.162 1.094 0.007 1.024-1.169 Sperm acrosomal enzyme activity -0.004 0.003 2.313 0.996 0.128 0.990-1.001 Sperm concentration -0.009 0.006 2.379 0.991 0.123 0.980-1.002 PR 0.009 0.022 0.153 1.009 0.696 0.966-1.054 TPMC -0.013 0.006 5.548 0.987 0.019 0.976-0.998 IM 0.019 0.022 0.783 1.019 0.376 0.977-1.063 DFI 0.034 0.012 8.042 1.034 0.005 1.010-1.058 Type of infertility -0.295 0.259 1.303 0.744 0.254 0.448-1.236 Years of infertility 0.030 0.059 0.262 1.030 0.609 0.919-1.156 Constant -1.229 2.113 0.338 0.293 0.561 Table 6 ROC curve Items AUC 95%CI P-value Optimal Cut-off Point Maximum Youden`s Index Female BMI 0.575 0.512-0.638 0.018 21.8 0.67-0.54 DFI 0.617 0.559-0.675 0.000 11.88 0.64-0.43 TPMC 0.703 0.649-0.757 0.000 42.0 0.66-0.35 Note:Because the TPMC is negatively correlated with fertilization, the data in this table are obtained by curve-fitting the TPMC *-1. Discussion Oocyte fertilization is influenced by a multitude of factors, including the quality of the oocyte itself (e.g., maturity and genetic integrity), sperm quality (e.g., motility, DNA fragmentation, and acrosomal enzyme activity), and the in vitro culture environment (e.g., culture media, conditions, and laboratory handling protocols) [6-8]. Despite the implementation of individualized ovulation regimens tailored to follicular development [6], stringent quality control measures in embryo laboratory practices [7], and optimization of sperm preparation techniques [8], a subset of patients still experiences low IVF fertilization rates. In this study, the incidence of low fertilization rates at our center was 13.80% (97/703), including 18 cases of complete fertilization failure, which were consolidated to minimize statistical bias. Our analysis identified several statistically significant factors associated with fertilization outcomes, encompassing female-related factors (e.g., age and body mass index), male-related factors (e.g., age, sperm concentration, total progressively motile sperm count [TPMC], acrosomal enzyme activity, PR, IM, and DFI), and unexplained factors (e.g., infertility type and duration). These findings align with extensive prior research highlighting the impact of these variables on gamete and embryo quality [9-18]. Regression analysis revealed that female BMI, TPMC, and sperm DFI were the most strongly correlated with IVF fertilization outcomes. Notably, TPMC emerged as the most robust predictor of fertilization success. The hormonal milieu plays a critical role in regulating the menstrual cycle, ovulation, and endometrial development. Obesity disrupts this delicate balance through multiple mechanisms, including impaired secretion and bioavailability of sex hormones, as well as excessive production of leptin, insulin, and adipokines. These adipokines exert detrimental effects at both central and peripheral levels, adversely affecting follicular development and oocyte maturation [19,20]. Women with a BMI exceeding 25 kg/m² often exhibit diminished ovarian function and reduced ovarian reserve, which may contribute to suboptimal ART outcomes [21,22]. Elevated BMI is also associated with increased requirements for ovulation-inducing medications [23], poorer embryo quality, higher miscarriage rates, and lower live birth rates [24]. Sperm DNA fragmentation index (DFI) reflects the integrity of sperm DNA. While some studies suggest that DFI does not directly affect fertilization rates, it has been linked to reduced numbers of transferable embryos, lower high-quality embryo rates, and decreased implantation, clinical pregnancy, and live birth rates following IVF-ET. Additionally, elevated DFI is associated with an increased risk of early miscarriage per transfer cycle [25-27]. Although DFI was not assessed on the day of egg retrieval in this study, data from the most recent semen analysis (within three months) still demonstrated a significant association between DFI and fertilization outcomes in our logistic regression analysis. Total progressively motile sperm count (TPMC), calculated as semen volume × sperm concentration × PR, has been widely validated as a reliable predictor of fertilization failure [14,28-30]. Previous research has even proposed a TPMC threshold of <1.5 × 10^6 post-optimization as a critical indicator, with a sensitivity of 80% for predicting fertilization failure [31]. In our study, semen analysis on the day of egg retrieval focused primarily on TPMC adequacy (using a threshold of >1.5 × 10^6), while other parameters, such as motility and concentration, were manually assessed prior to optimization using a marker plate. Based on ROC curve analysis, we identified a pre-optimization TPMC threshold of >42.0 × 10^6 as the most effective predictor of fertilization failure, with a sensitivity of 66% and a false-positive rate of 35%. In the evaluation of fertilization failure prediction models, the Receiver Operating Characteristic (ROC) curve and its derived parameters - including the Area Under the Curve (AUC), optimal cutoff value, and Youden index - hold significant clinical relevance for assessing diagnostic performance. The AUC quantifies the overall accuracy of prediction models, ranging from 0.5 (equivalent to random chance) to 1.0 (perfect discrimination). In our analysis, the TPMC parameter demonstrated an AUC of 0.703, indicating moderate discriminative ability between fertilization success and failure. This performance surpasses random prediction (AUC=0.5) and supports the clinical utility of TPMC as a predictive biomarker. Moreover, AUC comparisons between different parameters enable evidence-based selection of optimal predictors for clinical decision-making. The optimal cutoff value (42.0×10⁶ spermatozoa for TPMC) represents the threshold that optimally balances sensitivity and specificity. Clinical interpretation follows: values below this threshold suggest elevated fertilization failure risk, prompting consideration of tailored interventions such as modified ovarian stimulation protocols or intracytoplasmic sperm injection (ICSI). Conversely, values exceeding 42.0×10⁶ spermatozoa are associated with higher probabilities of conventional fertilization success. This threshold-based approach facilitates rapid clinical assessment and risk-stratified treatment planning. The Youden index (calculated as sensitivity + specificity - 1) provides a unified metric of diagnostic effectiveness. With a value of 0.31 for TPMC, this index reflects a 31% improvement over random classification in detecting fertilization failure. Parameters with higher Youden indices are preferred when developing multifactorial prediction models, as they optimize the trade-off between false-positive and false-negative outcomes. Clinically, this metric guides resource-efficient treatment strategies by identifying patients who would benefit most from targeted interventions. CONCLUSION This study demonstrates that female BMI, sperm DNA fragmentation index (DFI), and total progressively motile sperm count (TPMC) are significant predictors of fertilization rates in IVF cycles. However, several limitations should be acknowledged. First, the data were collected over a five-year period, and the relatively small number of cycles at our center may limit the generalizability of the findings. Second, potential confounding factors, such as changes in laboratory personnel, updates to experimental protocols, shifts in scientific perspectives, and variations in consumables, may have influenced the results. Additionally, this study focused on a limited set of clinical indicators and did not account for other potential influencing factors, including lifestyle variables (e.g., smoking, alcohol consumption, and diet), genetic and endocrine factors, and subtle variations in laboratory procedures. Notably, sperm morphology data were not collected, which may have provided further insights into fertilization outcomes. Future research should incorporate a broader range of variables, including those mentioned above, to better understand the complex mechanisms underlying IVF fertilization rates. Such comprehensive analyses will not only enhance our ability to predict fertilization outcomes but also inform more personalized therapeutic strategies, ultimately improving the success rates of assisted reproductive technologies. Declarations Ethical Approval Approval for this study was obtained from the Ethics Review Committee of Wenzhou People’s Hospital (Approval No.KY-202501-010). As a retrospective analysis of anonymized clinical data, this study posed minimal risk to participants. According to the ethical guidelines of the National Health Commission of China and the institutional review board policy, written informed consent was waived for this type of study. All data were de-identified prior to analysis to ensure patient confidentiality. Funding This study was funded by Wenzhou basic scientific research project（Y20210334,Y2023528）. Availability of data and materials The datasets utilized in this study have been uploaded to figshare, a reputable data repository, to ensure their long - term preservation and accessibility. The data can be accessed through the following URL: https://figshare.com/articles/dataset/Embryo_data_summary_raw_xlsx/28342367?file=52116827. Author contribution HW and HP contributed to the conception of the study.HW and ZX performed the literature search, data extraction,and study quality assessment. WH, ZX , and XZ were involved in statistical analysis. HW, JZ and SX contributed to the interpretation of the results. HW was responsible for manuscript drafting. All authors read and approved the final manuscript. Conflict of Interest Statement The authors declare no competing interests. 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The effect of rapid and delayed insemination on reproductive outcome in conventional insemination and intracytoplasmic sperm injection in vitro fertilization cycles[J]. J Assist Reprod Genet, 2021, 38(10): 2697-2706. doi: 10.1007/s10815-021-02299-7 Kaltsas A, Zikopoulos A, Moustakli E, et al. The Silent Threat to Women's Fertility: Uncovering the Devastating Effects of Oxidative Stress[J]. Antioxidants (Basel), 2023, 12(8). doi: 10.3390/antiox12081490 Dabbagh Rezaeiyeh R, Mehrara A, Mohammad Ali Pour A, et al. Impact of Various Parameters as Predictors of The Success Rate of In Vitro Fertilization[J]. Int J Fertil Steril, 2022, 16(2): 76-84. doi: 10.22074/ijfs.2021.531672.1134 Candeloro M, Di Nisio M, Ponzano A, et al. Effects of Obesity and Thrombophilia on the Risk of Abortion in Women Undergoing In Vitro Fertilization[J]. Front Endocrinol (Lausanne), 2020, 11: 594867. doi: 10.3389/fendo.2020.594867 Marinelli S, Napoletano G, Straccamore M, et al. Female obesity and infertility: outcomes and regulatory guidance[J]. Acta Biomed, 2022, 93(4): e2022278. doi: 10.23750/abm.v93i4.13466 Hassan MA, Killick SR. Negative lifestyle is associated with a significant reduction in fecundity[J]. Fertil Steril, 2004, 81(2): 384-392. doi: 10.1016/j.fertnstert.2003.06.027 García-Ferreyra J, Carpio J, Zambrano M, et al. Overweight and obesity significantly reduce pregnancy, implantation, and live birth rates in women undergoing In Vitro Fertilization procedures[J]. JBRA Assist Reprod, 2021, 25(3): 394-402. doi: 10.5935/1518-0557.20200105 Purcell SA, Elliott SA, Kroenke CH, et al. Impact of Body Weight and Body Composition on Ovarian Cancer Prognosis[J]. Curr Oncol Rep, 2016, 18(2): 8. doi: 10.1007/s11912-015-0488-3 Zhang H, Zhu FY, He XJ, et al. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology[J]. Open Life Sci, 2023, 18(1): 20220597. doi: 10.1515/biol-2022-0597 Simon L, Zini A, Dyachenko A, et al. A systematic review and meta-analysis to determine the effect of sperm DNA damage on in vitro fertilization and intracytoplasmic sperm injection outcome[J]. Asian J Androl, 2017, 19(1): 80-90. doi: 10.4103/1008-682x.182822 Ferrigno A, Ruvolo G, Capra G, et al. Correlation between the DNA fragmentation index (DFI) and sperm morphology of infertile patients[J]. J Assist Reprod Genet, 2021, 38(4): 979-986. doi: 10.1007/s10815-021-02080-w Tan O, Ha T, Carr BR, et al. Predictive value of postished total progressively motile sperm count using CASA estimates in 6871 non-donor intrauterine insemination cycles[J]. J Assist Reprod Genet, 2014, 31(9): 1147-1153. doi: 10.1007/s10815-014-0306-0 Madbouly K, Isa A, Habous M, et al. Postish total motile sperm count: should it be included as a standard male infertility work up[J]. Can J Urol, 2017, 24(3): 8847-8852. doi: Qureshi SM, Kafeel S, Bibi R, et al. Post-ish total motile sperm count a useful predictor in the decision to perform IVF/ICSI in patients with non-male factor infertility[J]. Journal of Shifa Tameer-e-Millat University, 2020, 3(2): 99-106. doi: 10.32593/jstmu/Vol3.Iss2.108 Wiser A, Ghetler Y, Gonen O, et al. Re-evaluation of post-ish sperm is a helpful tool in the decision to perform in vitro fertilisation or intracytoplasmic sperm injection[J]. Andrologia, 2012, 44(2): 73-77. doi: 10.1111/j.1439-0272.2010.01107.x González-Comadran M, Jacquemin B, Cirach M, et al. The effect of short term exposure to outdoor air pollution on fertility[J]. Reprod Biol Endocrinol, 2021, 19(1): 151. doi: 10.1186/s12958-021-00838-6 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5924111","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":410935739,"identity":"48f6356c-096c-4798-b516-e2df7b6af0b0","order_by":0,"name":"Hai Wang","email":"","orcid":"","institution":"The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People’s Hospital, Wenzhou Maternal and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Wang","suffix":""},{"id":410935740,"identity":"74a1349c-cbc7-410e-b9b0-e530ca5511a9","order_by":1,"name":"Zitong Xu","email":"","orcid":"","institution":"The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People’s Hospital, Wenzhou Maternal and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zitong","middleName":"","lastName":"Xu","suffix":""},{"id":410935741,"identity":"055468b2-4e98-476b-9e25-a86961dc41cc","order_by":2,"name":"Xianjue Zheng","email":"","orcid":"","institution":"The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People’s Hospital, Wenzhou Maternal and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianjue","middleName":"","lastName":"Zheng","suffix":""},{"id":410935742,"identity":"31644ace-0683-4298-bb6e-a4e8d9202276","order_by":3,"name":"Jiayong Zheng","email":"","orcid":"","institution":"The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People’s Hospital, Wenzhou Maternal and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiayong","middleName":"","lastName":"Zheng","suffix":""},{"id":410935743,"identity":"86ec3b79-c9bf-4092-99ec-3da1045db023","order_by":4,"name":"Shuqi Xia","email":"","orcid":"","institution":"The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People’s Hospital, Wenzhou Maternal and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuqi","middleName":"","lastName":"Xia","suffix":""},{"id":410935744,"identity":"640a4700-bff1-4946-a860-352d16b97827","order_by":5,"name":"Haojie Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYFCCAyCCWQ5IMAKZCcRrMQZiBmK1gAFzYgPRWuQbzxh/+LnHOr1fIv/AgQ8VaQz87d349TE2nDEw7HmWnjtzRjLDwRlnchgkzpzdgN9FDGc3JDMcOJy74UYyw2HetgoGA4lc/FrYgFoOA7WkGxCthYfh7MZmoJYEqJYcwlokGM5/Zuw5kG44s+exAdAvaTwE/SI/41jyhx8HrOX52RMfPvhQkSzH396LXwuDxAF0lxIE/A2E1YyCUTAKRsEIBwDy7EzhFRTACgAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haojie","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2025-01-29 12:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5924111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5924111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75581127,"identity":"2f8f223d-32e2-45fa-8f39-747c957d37a4","added_by":"auto","created_at":"2025-02-06 05:38:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66345,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for Female BMI,DFI and TPMC\u003c/p\u003e\n\u003cp\u003eNote: Because the TPMC is negatively correlated with fertilization, the data in this figure are obtained by curve-fitting the TPMC *-1.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5924111/v1/712e29e069dbc576f35ccf29.png"},{"id":79231206,"identity":"99d29cf1-fcb7-4b1e-8085-c89b66fa693f","added_by":"auto","created_at":"2025-03-26 02:46:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":779271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5924111/v1/ab4a877b-39db-487d-918f-1a8858c6feaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Total Progressively Motile Sperm Cell Count as a Predictor of Fertilization Failure","fulltext":[{"header":"Background","content":"\u003cp\u003eIn vitro fertilization (IVF) is the most widely used assisted reproductive technology (ART), primarily suitable for patients with female factors (e.g., ovulation disorders, tubal anomalies, endometriosis, and cervical factors), male factors (e.g., mild to moderate sperm abnormalities), and unexplained infertility. The typical IVF fertilization rate ranges from 60% to 80%, but low fertilization rates (LFR, \u0026lt;25%) occur in approximately 20% of cases, with total fertilization failure (TFF) occurring in 5%-15% of cases [1,2]. To mitigate these issues, early assessment of fertilization is crucial. Degranulation of granulocytes is performed 4-6 hours post-IVF insemination to observe the expulsion of the second polar body, and early rescue intracytoplasmic sperm injection (R-ICSI) is implemented in cycles with fertilization rates below 30% [4]. The causes of low fertilization rates are multifactorial [5]. This study retrospectively analyzed clinical parameters from IVF and R-ICSI cycles at our center to identify more reliable predictors of fertilization success.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eA retrospective analysis was conducted on clinical data from 606 IVF cycles and 97 R-ICSI cycles from January 2019 to August 2024. Data collected included female age, male age, female BMI, baseline sex hormone levels (E2, LH, FSH, AMH), hepatitis B virus surface antigen (HBsAg), occupation, education, years of infertility, infertility factors, type of infertility, ovulation regimen, gonadotropin (Gn) usage, and days of Gn use. Hormonal profiles on the trigger day (E2, LH, progesterone) and male semen parameters (semen volume, sperm concentration, PR, IM, NP, acrosomal enzyme activity, and DFI) were also recorded. Non-parametric tests, logistic regression analysis, and ROC curves were used to analyze the data. Statistical analyses were performed using SPSS 22.0, with normality assessed via chi-square tests. Data conforming to a normal distribution were expressed as mean \u0026plusmn; standard deviation (x̄ \u0026plusmn; s), while non-normally distributed data were expressed as median (quartile) [M(P25, P75)]. The Mann-Whitney U test was used for group comparisons, and logistic regression analysis was performed to identify significant predictors. ROC curves were plotted using Python 3.31.\u003c/p\u003e\n\u003cp\u003eThis retrospective study utilized anonymized data generated from routine clinical practice at our reproductive medicine center. The research protocol was approved by the Institutional Review Board of Wenzhou People\u0026rsquo;s Hospital (Approval No.KY-202501-010), and all procedures complied with the Ethical Guidelines for Human Assisted Reproductive Technology and the Declaration of Helsinki. We confirm that written informed consent encompassing potential research use was obtained from all participants during their initial IVF treatment, and all analyzed data were fully de-identified.\u003c/p\u003e\n\u003cp\u003eIn summary, as a pivotal assisted reproductive technology, in vitro fertilization (IVF) faces significant success rate limitations due to suboptimal fertilization rates. While extensive research has investigated factors contributing to fertilization failure, the field still lacks precise and universally validated predictive biomarkers. This study conducted a comprehensive analysis of 606 IVF cycles and 97 rescue ICSI (R-ICSI) cycles performed during January 2019 to August 2024, systematically investigating potential correlations between multifactorial clinical parameters and fertilization outcomes. Methodological approaches included non-parametric statistical testing, multivariate logistic regression modeling, and ROC curve analysis to ensure rigorous data interpretation. The subsequent presentation of findings will elucidate how these analytical results address the research questions outlined in the study background, ultimately establishing an evidence base for identifying robust predictors of fertilization failure.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eComparison of Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed between the IVF and R-ICSI groups in female age, male age, female BMI, type of infertility, and years of infertility (P \u0026lt; 0.05). Details are provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Ovulation Induction Protocols\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ovulation promotion protocols employed in the two groups included the agonist protocol, antagonist protocol, ovarian stimulation under high progesterone conditions during the follicular phase, mild stimulation protocol, luteal phase ovulation induction, and natural cycle protocol. No statistically significant differences were observed between the two groups (P \u0026gt; 0.05). Additionally, there were no significant differences in key parameters such as gonadotropin (Gn) dosage, duration of Gn administration, or hormone levels on the trigger day (including E2, LH, and P) across the subgroups (P \u0026gt; 0.05). For detailed data, please refer to Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Semen Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo statistically significant differences were observed in semen volume or normal morphology (NP) between the two groups (P \u0026gt; 0.05). However, significant differences were noted in sperm concentration, forward motile sperm count, progressive motility (PR), immotility (IM), sperm DNA fragmentation index (DFI), and acrosomal enzyme activity (P \u0026lt; 0.05). Detailed results are presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFertilization Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe difference in the number of mature and immature oocytes between the two groups was not statistically significant (P \u0026gt; 0.05). However, significant differences were observed in fertilization outcomes (normal fertilization, abnormal fertilization, and unfertilized oocytes) when no R-ICSI procedure was performed (P \u0026lt; 0.05). Following the R-ICSI procedure, the normal fertilization rate increased from 17.16% (150/874) to 61.78% (540/874), the abnormal fertilization rate increased from 2.97% (26/874) to 9.95% (87/874), and the unfertilized rate decreased from 79.86% (698/874) to 28.26% (247/874). Despite the substantial difference in sample sizes between the groups, the differences remained statistically significant (P \u0026lt; 0.05). Detailed results are presented in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis and ROC Curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the grouping criteria of this study as the dependent variable, covariates with P \u0026lt; 0.05 from Tables 1, 2, and 3 were included in the logistic regression equation. The analysis identified three significant indicators: female BMI, total forward-motile sperm count, and sperm DNA fragmentation index (DFI), all with P \u0026lt; 0.05 (Table 5).\u003c/p\u003e\n\u003cp\u003eThese three indicators were further analyzed using ROC curves (Figure 1). The area under the curve (AUC) for female BMI was 0.575, with a maximum Youden index of 0.13. The corresponding threshold was 21.8 kg/m\u0026sup2;, yielding a true positive rate of 67.01% and a false positive rate of 53.96%. \u0026nbsp;The sperm DFI index had an AUC of 0.617, with a maximum Youden index of 0.21. The threshold was 11.88%, resulting in a true positive rate of 63.91% and a false positive rate of 43.07%.For the total forward-motile sperm count, the AUC was 0.703, with a maximum Youden index of 0.31. The optimal threshold was 42.0\u0026nbsp;\u0026times;\u0026nbsp;10⁶\u0026nbsp;spermatozoa, corresponding to a true positive rate of 63.92% and a false positive rate of 35.31%. Detailed results are presented in Table 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eComparison of baseline conditions between the two groups of patients\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003eIVF group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003eR-ICSI group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026chi;2, U-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eFemale age(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e34（30，38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e32（28，35.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e-2.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eMale age(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e35（31，40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e34（31，37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e-2.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eFemale BMI(kg/㎡)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e22.2（20.0，24.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e22.9（20.8，25.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e2.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eType of infertility（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e6.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003ePrimary infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e31.68%（192/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e44.33%（43/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003esecondary infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e68.32%（414/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e55.67%（54/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eInfertility factors（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e7.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eOvulation factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e18.32%（111/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e23.71%（23/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eMale factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e8.58%（52/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e15.46%（15/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003ePelvic factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e57.59%（349/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e47.42%（46/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eFactors on both sides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e4.79%（29/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e4.12%（4/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eOther factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e10.73%（65/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e9.28%（9/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eHepatitis status（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e6.60%（40/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e4.12%（4/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eNegatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e93.40%（566/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e95.88%（93/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eYears of infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e2（1，3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e2（1.5，3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e2.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eOccupation（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e1.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eWhite collar worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e16.34%（99/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e15.46%（15/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eServices and commerce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e3.96%（24/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e3.09%（3/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eBlue Collar and Manufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e8.75%（53/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e11.34%（11/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eSpecialized technical and knowledge-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e13.04%（79/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e13.40%（13/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eFreelancing and entrepreneurship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e52.48%（318/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e52.58%（51/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eOther and unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e5.45%（33/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e4.12%（4/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eEducation attainment（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e2.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eJunior high school ORbelow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e45.71%（277/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e42.27%（41/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eJunior college, vocational high school, high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e36.30%（220/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e32.99%（32/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eCollege, undergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e17.16%（104/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e23.71%（23/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e0.83%（5/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e1.03%（1/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eAMH(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e2.67（1.32，4.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e2.90（1.81，4.71）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eBasic FSH(mIU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e6.87（5.56,8.45）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e6.59（5.58,8.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e-0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eBasic LH(mIU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e4.58（3.15,6.77）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e4.86（3.72,6.36）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e1.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5269%;\"\u003e\n \u003cp\u003eBasic E2(pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6117%;\"\u003e\n \u003cp\u003e132.00（93.03,202.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4274%;\"\u003e\n \u003cp\u003e134.60（101.90,196.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6248%;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.80914%;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of ovulation promotion in two groups of patients\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eIVF group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003eR-ICSI group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026chi;2, U-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eOvulation program（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e2.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eAgonist programs (ultra-long program, short-acting long program, modified follicular phase long program, luteal phase long program, follicular phase long program)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e55.61%（337/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e58.76%（57/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eAntagonist program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e22.77%（138/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e22.68%（22/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eOvarian stimulation program in the hyperprogesterone state during the follicular phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e10.73%（65/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e12.37%（12/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eMicrostimulation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e9.57%（58/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e6.19%（6/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eLuteal phase ovulation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e0.99%（6/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e0.00%（0/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eNatural cycle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e0.33%（2/606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e0.00%（0/97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eGn use（U）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e2025（1575，2606）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e2100（1750，2850）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e1.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eGn days（days）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e11（9，12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e11（9，13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eTrigger day E2（pmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e7670.00（4442.25,14833.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e8492.65（5381.27,14909.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eTrigger day LH（mIU/ml）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e1.51（0.90,3.10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e1.33（0.68,2.89）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e-1.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eTrigger day P（nmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e4.90（3.48,6.73）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.4153%;\"\u003e\n \u003cp\u003e4.90（3.25,6.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80066%;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.299%;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eMale semen examination status\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003eIVF group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eR-ICSI group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003eU-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eSemen volume（ml）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e2.5（2.0，3.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e2.0（2.0，2.75）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e-1.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003esperm concentration（10\u003csup\u003e6\u003c/sup\u003e/ml）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e70.0（50.0，92.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e40.0（30.0，70.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e-6.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eTPMC（10\u003csup\u003e6\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e60.0（32.4，98.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e35.0（18.0，60.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e-6.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003ePR（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e40.0（30.0，50.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e30.0（30.0，40.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e-4.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eNP（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e20.0（10.0，20.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e20.0（10.0，20.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e-0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eIM（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e40.0（40.0，50.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e50.0（40.0，60.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e4.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eDFI（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e10.70（6.31，16.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e14.30（9.35，20.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e3.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5681%;\"\u003e\n \u003cp\u003eSperm acrosomal enzyme activity（uIU/10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003esperm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.2492%;\"\u003e\n \u003cp\u003e76.10（52.80，106.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e73.30（40.40，94.45）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e-2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1329%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Egg acquisition and fertilization\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7607%;\"\u003e\n \u003cp\u003eIVF group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.8803%;\"\u003e\n \u003cp\u003eR-ICSI group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003eU-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eEggs acquired（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e1.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eMature eggs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e88.23%（5538/6277）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e86.97%（874/1005）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eImmature eggs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e11.77%（739/6277）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e13.03%（131/1005）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eFertilization rate before R-ICSI(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e1850.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eNormal fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e67.08%（3715/5538）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e17.16%（150/874）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eAbnormal fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e18.91%（1047/5538）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e2.97%（26/874）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eUnfertilized\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e14.01%（776/5538）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e79.86%（698/874）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eFertilization rate after R-ICSI(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e133.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eNormal fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e67.08%（3715/5538）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e61.78%（540/874）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eAbnormal fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e18.91%（1047/5538）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e9.95%（87/874）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 30.0855%;\"\u003e\n \u003cp\u003eUnfertilized\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7607%;\"\u003e\n \u003cp\u003e14.01%（776/5538）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.8803%;\"\u003e\n \u003cp\u003e28.26%（247/874）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9915%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Logistic regression equation analysis of influencing factors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003ePredictor variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4167%;\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eFemale age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.929-1.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eMale age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e3.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.886-1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eFemale BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e7.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.024-1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eSperm acrosomal enzyme activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.990-1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eSperm concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.980-1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.966-1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eTPMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e5.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.976-0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.977-1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eDFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e8.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.010-1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eType of infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.448-1.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eYears of infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.919-1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e-1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4167%;\"\u003e\n \u003cp\u003e2.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eROC curve\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1076%;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3457%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7531%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003eOptimal Cut-off Point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9877%;\"\u003e\n \u003cp\u003eMaximum Youden`s Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1076%;\"\u003e\n \u003cp\u003eFemale BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3457%;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7531%;\"\u003e\n \u003cp\u003e0.512-0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9877%;\"\u003e\n \u003cp\u003e0.67-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1076%;\"\u003e\n \u003cp\u003eDFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3457%;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7531%;\"\u003e\n \u003cp\u003e0.559-0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e11.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9877%;\"\u003e\n \u003cp\u003e0.64-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1076%;\"\u003e\n \u003cp\u003eTPMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3457%;\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7531%;\"\u003e\n \u003cp\u003e0.649-0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5203%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9877%;\"\u003e\n \u003cp\u003e0.66-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:Because the TPMC is negatively correlated with fertilization, the data in this table are obtained by curve-fitting the TPMC *-1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOocyte fertilization is influenced by a multitude of factors, including the quality of the oocyte itself (e.g., maturity and genetic integrity), sperm quality (e.g., motility, DNA fragmentation, and acrosomal enzyme activity), and the in vitro culture environment (e.g., culture media, conditions, and laboratory handling protocols) [6-8]. Despite the implementation of individualized ovulation regimens tailored to follicular development [6], stringent quality control measures in embryo laboratory practices [7], and optimization of sperm preparation techniques [8], a subset of patients still experiences low IVF fertilization rates. In this study, the incidence of low fertilization rates at our center was 13.80% (97/703), including 18 cases of complete fertilization failure, which were consolidated to minimize statistical bias.\u003c/p\u003e\n\u003cp\u003eOur analysis identified several statistically significant factors associated with fertilization outcomes, encompassing female-related factors (e.g., age and body mass index), male-related factors (e.g., age, sperm concentration, total progressively motile sperm count [TPMC], acrosomal enzyme activity, PR, IM, and DFI), and unexplained factors (e.g., infertility type and duration). These findings align with extensive prior research highlighting the impact of these variables on gamete and embryo quality [9-18]. Regression analysis revealed that female BMI, TPMC, and sperm DFI were the most strongly correlated with IVF fertilization outcomes. Notably, TPMC emerged as the most robust predictor of fertilization success.\u003c/p\u003e\n\u003cp\u003eThe hormonal milieu plays a critical role in regulating the menstrual cycle, ovulation, and endometrial development. Obesity disrupts this delicate balance through multiple mechanisms, including impaired secretion and bioavailability of sex hormones, as well as excessive production of leptin, insulin, and adipokines. These adipokines exert detrimental effects at both central and peripheral levels, adversely affecting follicular development and oocyte maturation [19,20]. Women with a BMI exceeding 25 kg/m\u0026sup2; often exhibit diminished ovarian function and reduced ovarian reserve, which may contribute to suboptimal ART outcomes [21,22]. Elevated BMI is also associated with increased requirements for ovulation-inducing medications [23], poorer embryo quality, higher miscarriage rates, and lower live birth rates [24].\u003c/p\u003e\n\u003cp\u003eSperm DNA fragmentation index (DFI) reflects the integrity of sperm DNA. While some studies suggest that DFI does not directly affect fertilization rates, it has been linked to reduced numbers of transferable embryos, lower high-quality embryo rates, and decreased implantation, clinical pregnancy, and live birth rates following IVF-ET. Additionally, elevated DFI is associated with an increased risk of early miscarriage per transfer cycle [25-27]. Although DFI was not assessed on the day of egg retrieval in this study, data from the most recent semen analysis (within three months) still demonstrated a significant association between DFI and fertilization outcomes in our logistic regression analysis.\u003c/p\u003e\n\u003cp\u003eTotal progressively motile sperm count (TPMC), calculated as semen volume \u0026times; sperm concentration \u0026times; PR, has been widely validated as a reliable predictor of fertilization failure [14,28-30]. Previous research has even proposed a TPMC threshold of \u0026lt;1.5 \u0026times; 10^6 post-optimization as a critical indicator, with a sensitivity of 80% for predicting fertilization failure [31]. In our study, semen analysis on the day of egg retrieval focused primarily on TPMC adequacy (using a threshold of \u0026gt;1.5 \u0026times; 10^6), while other parameters, such as motility and concentration, were manually assessed prior to optimization using a marker plate. Based on ROC curve analysis, we identified a pre-optimization TPMC threshold of \u0026gt;42.0 \u0026times; 10^6 as the most effective predictor of fertilization failure, with a sensitivity of 66% and a false-positive rate of 35%.\u003c/p\u003e\n\u003cp\u003eIn the evaluation of fertilization failure prediction models, the Receiver Operating Characteristic (ROC) curve and its derived parameters - including the Area Under the Curve (AUC), optimal cutoff value, and Youden index - hold significant clinical relevance for assessing diagnostic performance.\u003c/p\u003e\n\u003cp\u003eThe AUC quantifies the overall accuracy of prediction models, ranging from 0.5 (equivalent to random chance) to 1.0 (perfect discrimination). In our analysis, the TPMC parameter demonstrated an AUC of 0.703, indicating moderate discriminative ability between fertilization success and failure. This performance surpasses random prediction (AUC=0.5) and supports the clinical utility of TPMC as a predictive biomarker. Moreover, AUC comparisons between different parameters enable evidence-based selection of optimal predictors for clinical decision-making.\u003c/p\u003e\n\u003cp\u003eThe optimal cutoff value (42.0\u0026times;10⁶ spermatozoa for TPMC) represents the threshold that optimally balances sensitivity and specificity. Clinical interpretation follows: values below this threshold suggest elevated fertilization failure risk, prompting consideration of tailored interventions such as modified ovarian stimulation protocols or intracytoplasmic sperm injection (ICSI). Conversely, values exceeding 42.0\u0026times;10⁶ spermatozoa are associated with higher probabilities of conventional fertilization success. This threshold-based approach facilitates rapid clinical assessment and risk-stratified treatment planning.\u003c/p\u003e\n\u003cp\u003eThe Youden index (calculated as sensitivity + specificity - 1) provides a unified metric of diagnostic effectiveness. With a value of 0.31 for TPMC, this index reflects a 31% improvement over random classification in detecting fertilization failure. Parameters with higher Youden indices are preferred when developing multifactorial prediction models, as they optimize the trade-off between false-positive and false-negative outcomes. Clinically, this metric guides resource-efficient treatment strategies by identifying patients who would benefit most from targeted interventions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that female BMI, sperm DNA fragmentation index (DFI), and total progressively motile sperm count (TPMC) are significant predictors of fertilization rates in IVF cycles. However, several limitations should be acknowledged. First, the data were collected over a five-year period, and the relatively small number of cycles at our center may limit the generalizability of the findings. Second, potential confounding factors, such as changes in laboratory personnel, updates to experimental protocols, shifts in scientific perspectives, and variations in consumables, may have influenced the results. Additionally, this study focused on a limited set of clinical indicators and did not account for other potential influencing factors, including lifestyle variables (e.g., smoking, alcohol consumption, and diet), genetic and endocrine factors, and subtle variations in laboratory procedures. Notably, sperm morphology data were not collected, which may have provided further insights into fertilization outcomes.\u003c/p\u003e\n\u003cp\u003eFuture research should incorporate a broader range of variables, including those mentioned above, to better understand the complex mechanisms underlying IVF fertilization rates. Such comprehensive analyses will not only enhance our ability to predict fertilization outcomes but also inform more personalized therapeutic strategies, ultimately improving the success rates of assisted reproductive technologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for this study was obtained from the Ethics Review Committee of Wenzhou People\u0026rsquo;s Hospital (Approval No.KY-202501-010). As a retrospective analysis of anonymized clinical data, this study posed minimal risk to participants. According to the ethical guidelines of the National Health Commission of China and the institutional review board policy, written informed consent was waived for this type of study. All data were de-identified prior to analysis to ensure patient confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Wenzhou basic scientific research project（Y20210334,Y2023528）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study have been uploaded to figshare, a reputable data repository, to ensure their long - term preservation and accessibility. The data can be accessed through the following URL: https://figshare.com/articles/dataset/Embryo_data_summary_raw_xlsx/28342367?file=52116827.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHW and HP contributed to the conception of the study.HW and ZX performed the literature search, data extraction,and study quality assessment. WH, ZX , and XZ were involved in statistical analysis. HW, JZ and SX contributed to the interpretation of the results. HW was responsible for manuscript drafting. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003evan der Westerlaken L, Helmerhorst F, Dieben S, et al. Intracytoplasmic sperm injection as a treatment for unexplained total fertilization failure or low fertilization after conventional in vitro fertilization[J]. Fertil Steril, 2005, 83(3): 612-617. doi: 10.1016/j.fertnstert.2004.08.029\u003c/li\u003e\n\u003cli\u003eXue Y, Cheng X, Xiong Y, et al. Gene mutations associated with fertilization failure after in vitro fertilization/intracytoplasmic sperm injection[J]. Front Endocrinol (Lausanne), 2022, 13: 1086883. doi: 10.3389/fendo.2022.1086883\u003c/li\u003e\n\u003cli\u003eZhang XD, Liu JX, Liu WW, et al. Time of insemination culture and outcomes of in vitro fertilization: a systematic review and meta-analysis[J]. Hum Reprod Update, 2013, 19(6): 685-695. doi: 10.1093/humupd/dmt036\u003c/li\u003e\n\u003cli\u003eChen C, Kattera S. Rescue ICSI of oocytes that failed to extrude the second polar body 6 h post-insemination in conventional IVF[J]. Hum Reprod, 2003, 18(10): 2118-2121. doi: 10.1093/humrep/deg325\u003c/li\u003e\n\u003cli\u003eWei Y, Wang J, Qu R, et al. Genetic mechanisms of fertilization failure and early embryonic arrest: a comprehensive review[J]. Hum Reprod Update, 2024, 30(1): 48-80. doi: 10.1093/humupd/dmad026\u003c/li\u003e\n\u003cli\u003eJungheim ES, Meyer MF, Broughton DE. Best practices for controlled ovarian stimulation in in vitro fertilization[J]. Semin Reprod Med, 2015, 33(2): 77-82. doi: 10.1055/s-0035-1546424\u003c/li\u003e\n\u003cli\u003eThe Vienna consensus: report of an expert meeting on the development of ART laboratory performance indicators[J]. Reprod Biomed Online, 2017, 35(5): 494-510. doi: 10.1016/j.rbmo.2017.06.015\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;il N, Kabuk\u0026ccedil;u C, \u0026Ccedil;abuş \u0026Uuml;, et al. Retrospective comparison of the semen preparation techniques for intrauterine insemination: Swim-up versus density gradient method[J]. J Gynecol Obstet Hum Reprod, 2022, 51(3): 102321. doi: 10.1016/j.jogoh.2022.102321\u003c/li\u003e\n\u003cli\u003eYeung E, Biedrzycki RJ, G\u0026oacute;mez Herrera LC, et al. Maternal age is related to offspring DNA methylation: A meta-analysis of results from the PACE consortium[J]. Aging Cell, 2024, 23(8): e14194. doi: 10.1111/acel.14194\u003c/li\u003e\n\u003cli\u003eBao S, Yin T, Liu S. Ovarian aging: energy metabolism of oocytes[J]. J Ovarian Res, 2024, 17(1): 118. doi: 10.1186/s13048-024-01427-y\u003c/li\u003e\n\u003cli\u003eKonishi S, Kariya F, Hamasaki K, et al. Fecundability and Sterility by Age: Estimates Using Time to Pregnancy Data of Japanese Couples Trying to Conceive Their First Child with and without Fertility Treatment[J]. Int J Environ Res Public Health, 2021, 18(10). doi: 10.3390/ijerph18105486\u003c/li\u003e\n\u003cli\u003eZhang C, Song S, Yang M, et al. Diminished ovarian reserve causes adverse ART outcomes attributed to effects on oxygen metabolism function in cumulus cells[J]. BMC Genomics, 2023, 24(1): 655. doi: 10.1186/s12864-023-09728-0\u003c/li\u003e\n\u003cli\u003eYazdani A, Halvaei I, Boniface C, et al. Effect of cytoplasmic fragmentation on embryo development, quality, and pregnancy outcome: a systematic review of the literature[J]. Reprod Biol Endocrinol, 2024, 22(1): 55. doi: 10.1186/s12958-024-01217-7\u003c/li\u003e\n\u003cli\u003eRhemrev JP, Lens JW, McDonnell J, et al. The postish total progressively motile sperm cell count is a reliable predictor of total fertilization failure during in vitro fertilization treatment[J]. Fertil Steril, 2001, 76(5): 884-891. doi: 10.1016/s0015-0282(01)02826-6\u003c/li\u003e\n\u003cli\u003eHotaling JM, Smith JF, Rosen M, et al. The relationship between isolated teratozoospermia and clinical pregnancy after in vitro fertilization with or without intracytoplasmic sperm injection: a systematic review and meta-analysis[J]. Fertil Steril, 2011, 95(3): 1141-1145. doi: 10.1016/j.fertnstert.2010.09.029\u003c/li\u003e\n\u003cli\u003eZhu Y, Zhang F, Cheng H, et al. Modified strict sperm morphology threshold aids in the clinical selection of conventional in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI)[J]. Asian J Androl, 2022, 24(1): 62-66. doi: 10.4103/aja.aja_45_21\u003c/li\u003e\n\u003cli\u003eEsiso FM, Cunningham D, Lai F, et al. The effect of rapid and delayed insemination on reproductive outcome in conventional insemination and intracytoplasmic sperm injection in vitro fertilization cycles[J]. J Assist Reprod Genet, 2021, 38(10): 2697-2706. doi: 10.1007/s10815-021-02299-7\u003c/li\u003e\n\u003cli\u003eKaltsas A, Zikopoulos A, Moustakli E, et al. The Silent Threat to Women\u0026apos;s Fertility: Uncovering the Devastating Effects of Oxidative Stress[J]. Antioxidants (Basel), 2023, 12(8). doi: 10.3390/antiox12081490\u003c/li\u003e\n\u003cli\u003eDabbagh Rezaeiyeh R, Mehrara A, Mohammad Ali Pour A, et al. Impact of Various Parameters as Predictors of The Success Rate of In Vitro Fertilization[J]. Int J Fertil Steril, 2022, 16(2): 76-84. doi: 10.22074/ijfs.2021.531672.1134\u003c/li\u003e\n\u003cli\u003eCandeloro M, Di Nisio M, Ponzano A, et al. Effects of Obesity and Thrombophilia on the Risk of Abortion in Women Undergoing In Vitro Fertilization[J]. Front Endocrinol (Lausanne), 2020, 11: 594867. doi: 10.3389/fendo.2020.594867\u003c/li\u003e\n\u003cli\u003eMarinelli S, Napoletano G, Straccamore M, et al. Female obesity and infertility: outcomes and regulatory guidance[J]. Acta Biomed, 2022, 93(4): e2022278. doi: 10.23750/abm.v93i4.13466\u003c/li\u003e\n\u003cli\u003eHassan MA, Killick SR. Negative lifestyle is associated with a significant reduction in fecundity[J]. Fertil Steril, 2004, 81(2): 384-392. doi: 10.1016/j.fertnstert.2003.06.027\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Ferreyra J, Carpio J, Zambrano M, et al. Overweight and obesity significantly reduce pregnancy, implantation, and live birth rates in women undergoing In Vitro Fertilization procedures[J]. JBRA Assist Reprod, 2021, 25(3): 394-402. doi: 10.5935/1518-0557.20200105\u003c/li\u003e\n\u003cli\u003ePurcell SA, Elliott SA, Kroenke CH, et al. Impact of Body Weight and Body Composition on Ovarian Cancer Prognosis[J]. Curr Oncol Rep, 2016, 18(2): 8. doi: 10.1007/s11912-015-0488-3\u003c/li\u003e\n\u003cli\u003eZhang H, Zhu FY, He XJ, et al. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology[J]. Open Life Sci, 2023, 18(1): 20220597. doi: 10.1515/biol-2022-0597\u003c/li\u003e\n\u003cli\u003eSimon L, Zini A, Dyachenko A, et al. A systematic review and meta-analysis to determine the effect of sperm DNA damage on in vitro fertilization and intracytoplasmic sperm injection outcome[J]. Asian J Androl, 2017, 19(1): 80-90. doi: 10.4103/1008-682x.182822\u003c/li\u003e\n\u003cli\u003eFerrigno A, Ruvolo G, Capra G, et al. Correlation between the DNA fragmentation index (DFI) and sperm morphology of infertile patients[J]. J Assist Reprod Genet, 2021, 38(4): 979-986. doi: 10.1007/s10815-021-02080-w\u003c/li\u003e\n\u003cli\u003eTan O, Ha T, Carr BR, et al. Predictive value of postished total progressively motile sperm count using CASA estimates in 6871 non-donor intrauterine insemination cycles[J]. J Assist Reprod Genet, 2014, 31(9): 1147-1153. doi: 10.1007/s10815-014-0306-0\u003c/li\u003e\n\u003cli\u003eMadbouly K, Isa A, Habous M, et al. Postish total motile sperm count: should it be included as a standard male infertility work up[J]. Can J Urol, 2017, 24(3): 8847-8852. doi: \u003c/li\u003e\n\u003cli\u003eQureshi SM, Kafeel S, Bibi R, et al. Post-ish total motile sperm count a useful predictor in the decision to perform IVF/ICSI in patients with non-male factor infertility[J]. Journal of Shifa Tameer-e-Millat University, 2020, 3(2): 99-106. doi: 10.32593/jstmu/Vol3.Iss2.108\u003c/li\u003e\n\u003cli\u003eWiser A, Ghetler Y, Gonen O, et al. Re-evaluation of post-ish sperm is a helpful tool in the decision to perform in vitro fertilisation or intracytoplasmic sperm injection[J]. Andrologia, 2012, 44(2): 73-77. doi: 10.1111/j.1439-0272.2010.01107.x\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Comadran M, Jacquemin B, Cirach M, et al. The effect of short term exposure to outdoor air pollution on fertility[J]. Reprod Biol Endocrinol, 2021, 19(1): 151. doi: 10.1186/s12958-021-00838-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IVF, R-ICSI, fertilization rate, total progressively motile sperm cell count","lastPublishedDoi":"10.21203/rs.3.rs-5924111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5924111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003eThis study aimed to investigate the differences in clinical parameters between cycles with normal in vitro fertilization (IVF) fertilization rates and those with low fertilization rates requiring rescue intracytoplasmic sperm injection (R-ICSI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eA retrospective analysis was conducted on clinical data from 606 IVF cycles and 97 R-ICSI cycles from January 2019 to August 2024. Non-parametric tests, logistic regression analysis, and ROC curves were employed to analyze the relationship between the data and identify the most influential indicators of fertilization failure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eSignificant differences were observed between the two groups in female age, male age, female BMI, type of infertility, years of infertility, sperm concentration, number of forward motile spermatozoa, PR, IM, DFI, and acrosomal enzyme activity (P \u0026lt; 0.05). Following R-ICSI, the normal fertilization rate increased from 17.16% (150/874) to 61.78% (540/874), significantly different from the 67.08% (3715/5538) observed in the IVF group (P \u0026lt; 0.05). The area under the curve (AUC) for female BMI, DFI, and total progressively motile sperm cell count (TPMC) were 0.575, 0.617, and 0.703, respectively, with Youden indices of 0.13, 0.21, and 0.31.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThe results suggest that TPMC is the most effective predictor of fertilization failure.\u003c/p\u003e","manuscriptTitle":"Total Progressively Motile Sperm Cell Count as a Predictor of Fertilization Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 05:30:51","doi":"10.21203/rs.3.rs-5924111/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8408b960-9420-4724-875b-4056720ce893","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43810966,"name":"Health sciences/Medical research/Outcomes research"},{"id":43810967,"name":"Health sciences/Medical research/Experimental models of disease"}],"tags":[],"updatedAt":"2025-03-26T02:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-06 05:30:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5924111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5924111","identity":"rs-5924111","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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