Vasectomy histology: Is it still useful?

OBJECTIVES: To determine if histological evaluation of the vasa is useful when post-vasectomy semen analysis (PVSA) compliance is low and to determine whether compliance could be predicted. METHODS: A retrospective evaluation of patients undergoing vasectomy between 2018 and 2022 was undertaken. A comparison of the PVSA between three vasa histological categorisations was made: complete divisions, incomplete division(s), absent vas(a). A multivariate model was constructed to predict PVSA compliance. RESULTS: From 388 patients, 191 (49.2%) undertook PVSA. Four patients had a revision of vasectomy. On 3 occasions this was due to the histology findings and once from semen analysis with normal histology. There was no significant difference in the number of azoospermic samples (95.4% vs 91.2%, ns), of samples with presence of Rare Non-Motile Sperm (RNMS) (2.6% vs 8.8%, ns) and those with sperm present (2.0 vs 0%, ns), between patients with complete division of the vasa on both sides and those with incomplete division on one side respectively. There was no difference in patient characteristics between those who complied with PVSA and those who did not. CONCLUSIONS: This paper suggests that there is a role for histological evaluation of the vasa when PVSA compliance is poor. Incompletely divided vasa on histology are not associated with an adverse PVSA.

Sperm Migration and Hyaluronic Acid Binding: Implications for Male Fertility Evaluation.

Mature, vital, and motile spermatozoa are essential for reaching the oocyte and binding to hyaluronic acid (HA) in the cumulus oophorus matrix. This study aims to determine the relationship between sperm-migration ability and HA-binding potential, as well as the relationship between sperm concentration and motility. Semen samples were collected from 702 men aged 20-56 years (median 34.8). We evaluated the sperm concentration and motility from basic semen analysis, the swim-up test (expressed as millions per mL and the migration efficiency percentage), and the hyaluronan-binding assay (HBA). A moderate positive correlation was found between the migration test results and HBA (R = 0.48). The highest correlation was observed between the concentration of motile spermatozoa and the migration test results (R = 0.85) and HBA (R = 0.4). The sperm migration efficiency strongly correlated with progressive motility (R = 0.6). Although significantly higher sperm migration was observed in patients with normal HBA results, the results of the functional tests were found to differ in some cases. For infertility treatment, the current diagnostic algorithm should be enhanced with more comprehensive seminological methods that assess the sperm-migration ability and HA-binding potential. We also recommend incorporating the swim-up method into the diagnostic protocol before planning assisted reproductive technology (ART) treatment.

Sperm DNA Fragmentation: Unraveling Its Imperative Impact on Male Infertility Based on Recent Evidence.

Male factors may be present in up to 50-70% of infertile couples and the prevalence of male infertility accounts for 20-30% of infertility cases. Understanding the mechanisms and causes behind male infertility remains a challenge, but new diagnostic tools such as DNA fragmentation might aid in cases where the routine semen analysis is insufficient. DNA fragmentation, which refers to damages or breaks of the genetic material of the spermatozoa, is considered one of the main causes of male infertility due to impaired functional capability of sperm. The aim of the present narrative review is to investigate and enlighten the potential correlation between DNA fragmentation and male infertility parameters such as the seminal profile and the reproductive outcomes. Comprehensive research in PubMed/Medline and Scopus databases was conducted and 28 studies were included in the present review. Fourteen studies provided data regarding the impact of DNA fragmentation and seminal parameters and showed a correlation of significantly lower sperm count, lower concentration, motility, and abnormal morphology with an increased DNA fragmentation index (DFI). Similarly, 15 studies provided data regarding the impact of DFI on reproductive outcomes. Two studies showed higher aneuploidy rates with higher DFI values, and seven studies showed significantly lower pregnancy rates and live birth rates with higher DFI values. Ultimately, the studies included in this review highlight, collectively, the importance of measuring sperm DFI in the assessment of male infertility. Further studies are needed to explore the effectiveness of interventions aiming to reduce DFI levels.

Retrospective study investigating the performance of the SQA-vision analyser compared with manual semen analysis.

The objective of this study was to compare the results of semen analysis using the manual method and the SQA-Vision sperm analyser after four years of practice and with a large cohort of patients. This was a comparative study of 1130 cases collected for semen analysis between October 2019 and October 2023, which were analysed simultaneously and independently by different operators using the manual microscopic method and an SQA-V automated analyser. For each sample, sperm concentration, progressive motility, motility, normal morphology, and round cells count were performed. There was no significant difference between the SQA-V method and manual assessment for all sperm parameters (Mann-Whitney test p > 0.05). According to the parameter studied, there was a strong correlation (rho = 0.81) and a very high correlation (rho = 0.98) between manual assessment and the SQA-V method. In the analysis of sperm concentration, the sensitivity and specificity were 0.90 and 0.99, respectively. The sensitivity and specificity for the analysis of sperm progressive motility were 0.98 and 0.99, respectively, while the sensitivity and specificity for the analysis of sperm motility were 0.87 and 0.99, respectively. The sensitivity and specificity for the analysis of normal morphology were 0.88 and 0.99, respectively. Regarding the analysis of round cells, the sensitivity and specificity were 0.98 and 0.99, respectively. The results of this retrospective study indicate that the SQA-V system offers satisfactory performance for routine sperm analysis.

Screening of cytokine expression in human seminal plasma in associations with sperm disorders and markers of oxidative-antioxidant balance.

Among the many factors with a proven relation to semen quality and male fertility, the determination of seminal plasma cytokines provides a promising direction for research into the identification of factors connected with male infertility. The interleukins: IL-1α, -1ß, -2, -4, -6, -8, -10, -12p40, -12p70, -18, IFNγ, and GM-CSF, total oxidant (TOS) and antioxidant (TAS) status, were simultaneously examined in seminal plasmas and blood sera in terato- (n = 32), asthenoterato- (n = 33), and oligoasthenoteratozoospermic (n = 29) infertile men and in normozoospermic fertile men (n = 20). Our research shows different cytokine composition of the sera and seminal plasmas in all studied groups, along with much higher concentrations of seminal plasma GM-CSF, IFNγ, IL-1α, IL-4, IL-6, and IL-8 and lower IL-18 and TOS in the comparison to their sera levels. The seminal plasma concentrations of GM-CSF, IFNγ, IL-1α, -4, and -6 differ significantly between fertile and infertile as well as between teratozoospermic, asthenoteratozoospermic, and oligoasthenoteratozoospermic groups. The indication of the cause of different concentrations of cytokines in seminal plasmas of infertile men, and their associations with semen parameters and oxidative status, may be a promising direction for the search for new therapeutic targets that would directly affect the cells and tissues of male reproductive organs.

Comparison of computer-assisted sperm analysis and smartphone-applied sperm analysis for evaluation of frozen-thawed bull semen.

This study aims to compare the efficacy of computer-assisted sperm analysis (CASA) and smartphone-applied sperm analysis (SASA) in assessing the quality of frozen-thawed bull semen. A total of 75 straws (n = 75) semen samples were used from different production batches of five Holstein bulls. The semen analyses were conducted in three groups: Group I (CASA-37°C), semen samples were evaluated using the CASA system at 37°C (n = 25); Group II (SASA-25°C), semen samples were assessed using the SASA system at a temperature of room heat (25°C) (n = 25); and Group III (SASA-37°C), semen samples were evaluated using the SASA system at 37°C (n = 25). The frozen-thawed bull semen samples were analysed in terms of total motility (TM), progressive motility (PM), immotile, velocity average path (VAP), velocity curve linear (VCL), velocity straight line (VSL) and sperm concentration. There was no significant difference between the groups in terms of spermatozoa concentration (p > .05). However, significant differences among the groups were observed for total motile spermatozoa values (p < .001). Values of progressive motile spermatozoa were lower in Group I and Group II compared to Group III (p < .001). The immotile spermatozoa values were significant between the groups (p < .001) and were found to be proportional to total motile spermatozoa values. Additionally, the VAP, VCL and VSL values were comparable between Group II and Group III, but lower when compared to Group I. In conclusion, the results of the study demonstrate that the Sperm Cell™ system can accurately analyse the concentration of frozen-thawed bull semen. The analyses performed at room temperature indicate a parallelism between the PM value and CASA results. However, it is thought that SASA devices require a series of standardization studies in different semen extenders, different sample concentrations and different animal species, analogous to the standardization evolution process of CASA devices in semen analysis.

The prediction of semen quality based on lifestyle behaviours by the machine learning based models.

PURPOSE: To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior. METHODS: The medical records of men whose semen was analyzed for any reason were collected. Those who had data about their lifestyle behaviors were included in the study. All semen analyses of the men included were evaluated according to the WHO 2021 guideline. All semen analyses were categorized as normozoospermia, oligozoospermia, teratozoospermia, and asthenozoospermia. The Extra Trees Classifier, Average (AVG) Blender, Light Gradient Boosting Machine (LGBM) Classifier, eXtreme Gradient Boosting (XGB) Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms. RESULTS: Seven hundred thirty-four men who met the inclusion criteria and had data about lifestyle behavior were included in the study. 356 men (48.5%) had abnormal semen results, 204 (27.7%) showed the presence of oligozoospermia, 193 (26.2%) asthenozoospermia, and 265 (36.1%) teratozoospermia according to the WHO 2021. The AVG Blender model had the highest accuracy and AUC for predicting normozoospermia and teratozoospermia. The Extra Trees Classifier and Random Forest Classifier models achieved the best performance for predicting oligozoospermia and asthenozoospermia, respectively. CONCLUSION: The ML models have the potential to predict semen quality based on lifestyles.

Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing.

OBJECTIVE: To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis. DESIGN: Anonymized sperm samples were videographed under a quantitative phase microscope, followed by generating and analyzing superresolution motion traces of individual spermatozoa. SETTING: Not applicable. PATIENT(S): Centrifuged human sperm samples. INTERVENTION(S): Not applicable. MAIN OUTCOME MEASURE(S): Precision of motion trace of individual sperms, presence of a helical pattern in the motion trace, mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression. RESULT(S): Spatially sensitive quantitative phase imaging with a superresolution computational technique MUltiple SIgnal Classification ALgorithm allowed achieving motion precision of 340 nm using ×10, 0.25 numerical aperture lens whereas the diffraction-limited resolution at this setting was 1,320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with a speed >25 µm/s were randomly selected from the same healthy donor semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. In addition, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (e.g., >70 µm/s) may demonstrate irregular motion and need further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUltiple SIgnal Classification ALgorithm is an image analysis technique that may vaguely fall under the machine learning category, but the conventional metrics for reporting found in Enhancing the QUAlity and Transparency Of health Research network do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility. CONCLUSION(S): Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion patterns, and whether irregularity of motion indicates poor quality regarding artificial insemination needs further investigation. The presented technique can be generalized for sperm analysis for a variety of fertility conditions.

First-line infertility treatment in normal or subnormal sperm: Interest of a simplified pre-IMSI test.

BACKGROUND: In the field of male infertility, when sperm is normal/subnormal, a few "add-on" routine tests can complete the basic semen examination. OBJECTIVES: The aim of this study was to develop and evaluate a faster, simplified motile sperm organelle morphology examination (MSOME) technique for selected infertile patients with apparently normal/subnormal sperm and, in their background: failure of two or three intrauterine insemination (IUI) cycles, repeatedly fragmented embryos, embryonic development to blastocyst-stage failures, repeated miscarriages, a long period of infertility or 2 or more IVF attempts without pregnancy. Our test results were correlated with IUI, conventional in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI) and intracytoplasmic morphologically selected sperm injection (IMSI) outcomes. MATERIALS AND METHODS: We validated an adapted version of the MSOME analysis called the pre-IMSI test (PIT), based on vacuole evaluation alone. 248 infertile patients from our assisted reproductive technology (ART) Center were retrospectively selected and split into three PIT score subgroups (patients with ≤8% (score I), 9 to 15% (score II) and ≥16% normal spermatozoa (score III)) based on the correlation between PIT results and each ART technique outcome. The choice of one or another of these ART techniques had been made according to the usual clinico-biological criteria. RESULTS: Clinical outcomes for each of the three PIT subgroups were compared individually for the different ART techniques. For ICSI, the effect of the PIT score subgroup was significant for clinical pregnancies (p = 0.0054) and presented a trend for live births (p = 0.0614). Miscarriage rates of IVF attempts were statistically different depending on the PIT score (p = 0.0348). Furthermore, the odds ratios of clinical pregnancy rates were significantly different according to PIT score subgroup when comparing ICSI vs. IMSI or IVF vs. ICSI attempts. DISCUSSION: IMSI appears to be recommended when sperm belongs to PIT score I, ICSI when it belongs to PIT score II and IVF or IUI when sperm is of PIT score III quality in selected infertile couples. The lack of statistical power in these PIT subgroups means that we must remain cautious in interpreting results. CONCLUSION: Our results support the interest of this simplified test for certain couples with normal/subnormal sperm to help choose the most efficient ART technique, even as first-line treatment.

Analysis of 12 kinds of cytokines in seminal plasma by flow cytometry and their correlations with routine semen parameters.

OBJECTIVE: To investigate the levels of 12 kinds of cytokines in seminal plasma and their correlations with routine semen parameters. METHODS: The remaining seminal plasma samples of 134 patients undergoing routine semen examination were collected for detecting cytokines. The parameters for sperm concentration, percentage of progressively motile sperm (PR), and motility were analyzed by a computer-assisted sperm analysis (CASA) system. According to the results of sperm concentration, PR and motility, 134 patients were divided into the normal routine semen parameters group, oligoasthenospermia group and azoospermia group. The levels of 12 kinds of cytokines in seminal plasma, including interleukin (IL)-1ß, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12P70, IL-17, interferin (IFN)-α, IFN-γ, and tumor necrosis factor (TNF)-α, were detected by flow cytometry. Two seminal plasma samples were detected for 10 times, respectively, to calculate the coefficients of variation (CV) of each cytokine. The linear range of each cytokine was measured using the standard, and the correlation coefficient (r) was calculated. RESULTS: The r2 of 12 kinds of cytokines detected by flow cytometry were all greater than 0.99. The reproducibility of 2 seminal plasma samples showed that the CVs of all cytokines were lower than 15 % except for TNF-α in sample 1 (15.15 %). Seminal plasma IL-6 levels were negatively correlated with semen volume (P < 0.01). Seminal plasma IL-5 levels were positively correlated with sperm concentration (P < 0.01). Seminal plasma IL-8 levels were negatively correlated with sperm motility (P < 0.01). Seminal plasma IL-8, IL-17 and IL-12P70 levels were negatively correlated with sperm PR (P < 0.05). In addition to the significant negative correlation between IL-5 and IL-17 (P < 0.05), there was a significant positive correlation between the majority of other cytokines. The levels of seminal plasma IL-17 and IL-12P70 in the oligoasthenospermia group and IL-1ß and IL-12P70 in the azoospermia group were significantly higher than those in the normal routine semen parameters group (P ≤ 0.05), while the levels of IL-10 in the azoospermia group were significantly lower than that in the normal routine semen parameters group (P < 0.05). CONCLUSION: There are certain correlations between seminal plasma cytokines and routine semen parameters and strong correlations between different seminal plasma cytokines, suggesting that the imbalance between seminal plasma cytokines may affect sperm quality. However, it still needs to be further confirmed by large samples and multi-center clinical studies and related basic researches.

A new model for determining risk of male infertility from serum hormone levels, without semen analysis.

We investigated a screening method using only serum hormone levels and AI (artificial intelligence) predictive analysis. Among 3662 patients, numbers for NOA (non-obstructive azoospermia), OA (obstructive azoospermia), cryptozoospermia, oligozoospermia and/or asthenozoospermia, normal, and ejaculation disorder were 448, 210, 46, 1619, 1333, and 6, respectively. "Normal" was defined as semen findings normal according to the WHO (World Health Organization) Manual for Human Semen Testing of 2021. We extracted age, LH (luteinizing hormone), FSH (follicle stimulating hormone), PRL (prolactin), testosterone, E2 (estradiol), and T (testosterone)/E2 from medical records. A total motility sperm count of 9.408 × 106 (1.4 ml × 16 × 106/ml × 42%) was defined as the lower limit of normal. The Prediction One-based AI model had an AUC (area under the curve) of 74.42%. For the AutoML Tables-based model, AUC ROC (receiver operating characteristic) was 74.2% and AUC PR (precision-recall) 77.2%. In a ranking of feature importance from 1st to 3rd, FSH came a clear 1st. T/E2 and LH ranked 2nd and 3rd for both Prediction One and AutoML Tables. Using data from 2021 and 2022 to verify the Prediction One-based AI model, the predicted and actual results for NOA were 100% matched in both years.

Optimization of spermatozoa analysis in mice: A comprehensive protocol.

Sperm quality is critical to predict reproductive alterations caused by immunological factors or toxicant agents. Yet, no detailed protocol has been published focusing on analyses of sperm parameters in mice. Our aim was to evaluate the most efficient diluent for mice sperm analyses and to optimize the sperm morphology classification, through the comparison of different staining methods. The diluents assessed were PBS (baseline), HTF, DMEM, 1 % BSA in PBS and 9 % skimmed powdered milk diluted in PBS. Spermatozoa were evaluated for vitality, motility, and morphology, smears were stained with Papanicolaou, HE, Giemsa, and Rapid staining. Sperm vitality and total motility reached better scores in milk based and DMEM diluents. HE raised up as an effective option since its combination with any of the diluents we tested, resulted in a fair staining, which was appropriated to evaluate mice spermatozoa. Finally, based on WHO manual, we have updated the current morphological classification for mice sperm, since we have detailed the head defects as well as included midpiece and tail defects on it. Taken together, we presented a useful, low cost, and reliable method to assess sperm morphology that could be employed worldwide by laboratories dedicated to study reproductive biology on mice model.

Multimodal distribution and its impact on the accurate assessment of spermatozoa morphological data: Lessons from machine learning.

Objective assessment of sperm morphology is an essential component for assessing ejaculate quality. Due to economic limitations, investigators often divert to conducting observational studies instead of experimental ones, which provide the strongest statistical power, yielding more heterogeneous data regardless of the number of data sources (barns/farms). Using such data inevitably leads to higher variances of estimates, which negatively impacts the statistical power of a study. In this article, we describe a statistical methodology called finite mixture modeling (FMM), which, based on the supplied data and assumed number of sub-classes, classifies the data into two or more homogeneous types of distributions and determines their fractional size relative to the entire cohort. The goal is to use statistical methods that will confound the variance of the sample. A figure from a previous publication was used to generate simulated data (n=1559) on the cytoplasmic droplet rate. We identified that a bi-modal distribution with two latent classes best described the simulated data. Post-hoc estimation showed that about 80 % of observations belonged to latent class 1, with 20 % in latent class 2. The FMM methodology identified a cutoff point of 8.7 %. Finally, when estimating the standard error for the total cohort, the FMM methodology yielded a 40 % reduction in the standard error compared to standard methodologies. In conclusion, here we show that FMM successfully confounded the variance of the data and, as such, yielded lower estimates of the variance than standard methodologies, increasing the statistical power of the cohort.

A 2-step remote TUNEL approach for sperm DNA fragmentation assessment. Analysis in donors and patients.

OBJECTIVES: Infertility is a disease of the male or female reproductive systems. Male reproductive workup is based on routine semen analysis, although of limited value. The 2021 WHO Manual incorporated Sperm DNA Fragmentation (SDF) assessment, and highlighted the need for individual laboratories to define suitable thresholds. This study aimed to present an alternative to address this issue, determine an SDF cut-off value with fertile donors, and characterize SDF in a patient cohort and their relationship with semen parameters. STUDY DESIGN: A service unit was established to remotely perform TUNEL assay in a 2 step-process. Semen samples were received at andrology laboratories, subjected to routine semen analysis (WHO, 2010), partially processed and transported to the service unit for SDF evaluation. Using this setting, studies were done in fertile donors (n = 15) to define the cut-off value, and in men undergoing infertility workup (n = 318). RESULTS: A cut-off value of 9.17 % was determined with the fertile donor cohort. With this cut-off, a 64.46 % abnormal SDF incidence was determined in the patient cohort. SDF negatively correlated with sperm number, vitality and motility, and positively with abnormal morphology and male age (P < 0.05). TUNEL-positive cases depicted lower sperm quality and higher male age (P < 0.05). A similar abnormal SDF incidence was determined among patients with semen abnormalities. Asthenozoospermic and ≥40 years patient samples depicted higher (P < 0.05) SDF than those of the general population. SDF incidence was also high in normozoospermic patients. CONCLUSIONS: Using a 2-step remote approach with a standardized procedure and an SDF cut-off value established with fertile donors, high SDF incidence in semen samples depicting normal and abnormal quality were identified in men consulting for infertility, highlighting the relevance of its evaluation as part of the male fertility workup.

Proteomic Analysis of Differences in the Freezability of Porcine Sperm Identifies α-Amylase As a Key Protein.

To investigate the mechanisms underlying the differences in the freezability of boar semen, Yorkshire boars with freezing-tolerant semen (YT, n = 3), Yorkshire boars with freezing-sensitive semen (YS, n = 3), Landrace boars with freezing-tolerant semen (LT, n = 3), and Landrace boars with freezing-sensitive semen (LS, n = 3) were selected for this study. Their sperm was subjected to protein extraction, followed by data-independent acquisition proteomics and functional bioinformatics analysis. A total of 3042 proteins were identified, of which 2810 were quantified. Some key KEGG pathways were enriched, such as starch and sucrose metabolism, carbohydrate digestion and absorption, mineral absorption, the HIF-1 signaling pathway, and the necroptosis pathways. Through PRM verification, we found that several proteins, such as α-amylase and epididymal sperm-binding protein 1, can be used as molecular markers of the freezing resistance of boar semen. Furthermore, we found that the addition of α-amylase to cryoprotective extender could significantly improve the post-thaw motility and quality of boar semen. In summary, this study revealed some molecular markers and potential molecular pathways contributing to the high or low freezability of boar sperm, identifying α-amylase as a key protein. This study is valuable for optimizing boar semen cryopreservation technology.

Optimized sperm selection: a highly efficient device for the isolation of progressive motile sperm with low DNA fragmentation index.

PURPOSE: To identify the sperm preparation procedure that selects the best sperm population for medically assisted reproduction. METHODS: Prospective observational study comparing the effect of four different sperm selection procedures on various semen parameters. Unused raw semen after routine diagnostic analysis was split in four fractions and processed by four different methods: (1) density gradient centrifugation (DGC), (2) sperm wash (SW), (3) DGC followed by magnetic activated cell sorting (MACS), and (4) using a sperm separation device (SSD). Each fraction was analyzed for progressive motility, morphology, acrosome index (AI), and DNA fragmentation index (DFI). RESULTS: With DGC as standard of care in intraclass correlation coefficient analysis, only SSD was in strong disagreement regarding progressive motility and DFI [0.26, 95%CI (- 0.2, 0.58), and 0.17, 95%CI (- 0.19, 0.45), respectively]. When controlling for abstinence duration, DFI was significantly lower after both MACS and SSD compared to DGC [- 0.27%, 95%CI (- 0.47, - 0.06), p = 0.01, and - 0.6%, 95%CI (- 0.80, - 0.41), p < 0.001, respectively]. Further comparisons between SSD and MACS indicate significantly less apoptotic cells [Median (IQR) 4 (5), 95%CI (4.1, - 6.8) vs Median (IQR) 5 (8), 95%CI (4.9, - 9.2), p < 0.001, respectively] and dead cells [Median (IQR) 9.5 (23.3), 95%CI (13.2, - 22.4) vs Median (IQR) 22 (28), 95%CI (23.1, - 36.8), p < 0.001, respectively] in the SSD group. CONCLUSION: The selection of a population of highly motile spermatozoa with less damaged DNA from unprocessed semen is ideally performed with SSD. Question remains whether this method improves the embryological outcomes in the IVF laboratory.

Comparison of Testicular Sonography and Elastography Findings With Semen Parameters in Cases Investigated for Infertility.

OBJECTIVES: This study aimed to investigate the correlation between testicular shear wave elastography (SWE) values and semen analysis results in men with infertility. METHODS: This was a retrospective case-control study. Patients were categorized as normal, abnormal, or azoospermic based on sperm analysis results. Testicular volume was measured using B-mode ultrasonography using the Lambert formula. Subsequently, 40-80 regions of interest measuring 1.5 × 1.5 mm were manually positioned in both testicles based on their size, and two-dimensional SWE was applied through virtual touch imaging quantification software. RESULTS: The patients had a mean age of 33.79 ± 6.3 years, with semen analysis revealing normal results in 15 patients (22.4%), pathological findings in 35 patients (52.2%), and azoospermia in 17 patients (25.4%). Right, left, total, and mean testicular volumes were significantly lower in patients with azoospermia compared to those in both normal and impaired semen parameters (P < .05). Conversely, testicular elastography scores were higher in patients with azoospermia than in the other groups (P < .05). The significant negative correlation between volume and elastographic findings remained independent of age (r = 0.4, P < .001). The accuracy rates for detecting impaired semen parameters and azoospermia were 94.3% and 94.1%, respectively, after considering factors such as age, testicular volume (right/left/total), and elastography (right/left/total). Notably, the total mean elastography score ranked first, with 100% in the independent normalized importance distribution of these variables. CONCLUSION: SWE can be used effectively alone or in combination with other diagnostic tools to evaluate histopathological changes in the testicles of male patients with infertility.

Interplay between male gonadal function and overall male health.

The process-of-male reproduction is intricate, and various medical conditions-have the potential to disrupt spermatogenesis. Moreover, infertility in males can serve as an indicator of-potential future health issue. Numerous conditions with systemic implications have been identified, encompassing genetic factors (such as Klinefelter Syndrome), obesity, psychological stress, environmental factors, and others. Consequently, infertility assessment-presents an opportunity for comprehensive health counseling, extending-beyond discussions about reproductive goals. Furthermore, male infertility has been suggested as a harbinger of future health problems, as poor semen quality and a diagnosis of-male infertility are associated with an increased risk of hypogonadism, cardiometabolic disorders, cancer, and even mortality. This review explores the existing-literature on the relationship between systemic illnesses and male fertility, impacting both clinical-outcomes and semen parameters. The majority of the literature analyzed, which compared gonadal function with genetic, chronic, infectious or tumoral diseases, confirm the association between overall male health and infertility.

Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking.

Male infertility is a global health issue, with 40-50% attributed to sperm abnormalities. The subjectivity and irreproducibility of existing detection methods pose challenges to sperm assessment, making the design of automated semen analysis algorithms crucial for enhancing the reliability of sperm evaluations. This paper proposes a comprehensive sperm tracking algorithm (Sperm YOLOv8E-TrackEVD) that combines an enhanced YOLOv8 small object detection algorithm (SpermYOLOv8-E) with an improved DeepOCSORT tracking algorithm (SpermTrack-EVD) to detect human sperm in a microscopic field of view and track healthy sperm in a sample in a short period effectively. Firstly, we trained the improved YOLOv8 model on the VISEM-Tracking dataset for accurate sperm detection. To enhance the detection of small sperm objects, we introduced an attention mechanism, added a small object detection layer, and integrated the SPDConv and Detect_DyHead modules. Furthermore, we used a new distance metric method and chose IoU loss calculation. Ultimately, we achieved a 1.3% increase in precision, a 1.4% increase in recall rate, and a 2.0% improvement in mAP@0.5:0.95. We applied SpermYOLOv8-E combined with SpermTrack-EVD for sperm tracking. On the VISEM-Tracking dataset, we achieved 74.303% HOTA and 71.167% MOTA. These results show the effectiveness of the designed Sperm YOLOv8E-TrackEVD approach in sperm tracking scenarios.