Identification and Structure Prediction of Human Septin-4 as a Biomarker for Diagnosis of Asthenozoospermic Infertile Patients—Critical Finding Toward Personalized Medicine

Semen parameters are been found as a key factor to evaluate the count and morphology in the given semen sample. The deep knowledge of male infertility will unravel with semen parameters correlated with molecular and biochemical parameters. The current research study is to identify the motility associated protein and its structure through the in-silico approach. Semen samples were collected and initial analysis including semen parameters was analyzed by using the World Health Organization protocol. Semen biochemical parameters, namely, seminal plasma protein concentration, fructose content, and glucosidase content were calculated and evaluated for correlation. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) were carried out for identification of Septin-4 presence in the semen sample. Mascot search was done for protein conformation and in-silico characterization of Septin-4 by structural modeling in Iterative Threading Assembly Refinement (I-TASSER). Twenty-five nanoseconds molecular dynamics (MD) simulations results showed the stable nature of Septin-4 in the dynamic system. Overall, our results showed the presence of motility-associated protein in normospermia and control samples and not in the case of asthenospermia and oligoasthenospermia. Molecular techniques characterized the presence of Septin-4 and as a novel biomarker for infertility diagnosis.

Comparison of postal and non-postal post-vasectomy semen sample submission strategies on compliance and failures: an 11-year analysis of the audit database of the Association of Surgeons in Primary Care of the UK

BackgroundVasectomy occlusive success is defined by the recommendation of ‘clearance’ to stop other contraception, and is elicited by post-vasectomy semen analysis (PVSA). We evaluated how the choice of either a postal or non-postal PVSA submission strategy was associated with compliance to PVSA and effectiveness of vasectomy.MethodsWe studied vasectomies performed in the UK from 2008 to 2019, reported in annual audits by Association of Surgeons in Primary Care members. We calculated the difference between the two strategies for compliance with PVSA, and early and late vasectomy failure. We determined compliance by adding the numbers of men with early failure and those given clearance. We performed stratified analyses by the number of test guidance for clearance (one-test/two-test) and the study period (2008–2013/2014–2019).ResultsAmong 58 900 vasectomised men, 32 708 (56%) and 26 192 (44%) were advised submission by postal and non-postal strategies, respectively. Compliance with postal (79.5%) was significantly greater than with non-postal strategy (59.1%), the difference being 20.4% (95% CI 19.7% to 21.2%). In compliant patients, overall early failure detection was lower with postal (0.73%) than with non-postal (0.94%) strategy (−0.22%, 95% CI −0.41% to −0.04%), but this difference was neither clinically nor statistically significant with one-test guidance in 2014–2019. There was no difference in late failure rates.ConclusionsPostal strategy significantly increased compliance to PVSA with similar failure detection rates. This resulted in more individuals receiving clearance or early failure because of the greater percentage of postal samples submitted. Postal strategy warrants inclusion in any future guidelines as a reliable and convenient option.

P–109 Comparison between the outcome of sperm vitrification protocol and conventional slow freezing protocol for semen cryopreservation

Study question Does sperm vitrification technique helps in increasing sperm survival and low DNA fragmentation index post warming. Summary answer Sperm vitrification protocol results in better motility, high progression and low DNA fragmentation index as compared to slow freezing. What is known already Cryopreservation is ceasing and resuming the cell metabolism, which can be achieved by different techniques like slow freezing and vitrification .Vitrification allows solidification of the cells and extracellular milieu into a glass like state without formation of ice which protects intracellular and extracellular ice formation, and further helps in avoiding different types of cryo-injuries and cellular damage. Study design, size, duration: Comparative study from July 2019 to Oct 2020 in IVF unit of IKDRC Hospital. Two hundred and ten patients were randomized by computer generated list and divided into two groups. Group 1 (n = 110) samples cryopreserved by vitrification and Group 2 (n = 100) samples cryopreserved by conventional slow freezing. Participants/materials, setting, methods Semen sample were analyzed by WHO 2010 laboratory manual, including all normozoospermic samples , other abnormal samples were excluded from the study . Method of semen preparation before cryopreservation is similar for both the groups, double density gradient method of preparation was used . Semen sample with high viscosity, hypo and hyper-spermia were also excluded. Similar cryovials of 2ml volume were used for both groups. Main results and the role of chance In group 1 where samples were cryopreserved by vitrification sperm motility was (54.3% vs 49.2%)vs in group 2 where samples were cryopreserved by slow freezing , non- significant difference were observed , but progressive motility was significantly higher in group 1 as compared to group 2 (36.8%vs17.9%) and DNA fragmentation index is significantly lower in group 1 vitrification than in group 2slow freezing ( 9.7% vs 20%). Limitations, reasons for caution Technical proficiency of the operator to avoid human errors and still larger randomized control studies are needed to strengthen these results Wider implications of the findings: Our study demonstrates that vitrification is better than slow freezing of human sperm, improved survival rates with high progression were found with vitrification and low DNA fragmentation index were also observed in samples cryopreserved with vitrification protocol. Trial registration number Not applicable

P–069 microfluidic sperm sorting vs density gradient to yield sperm with reduced DFI for patients undergoing IVF-ICSI

Study question To compare the effect of sperm preparation methods on the DFI of semen sample for patients undergoing ICSI. Summary answer On comparing the results, microfluidic sperm sorting yielded sperms with significantly less DFI as compared to density gradient method of sperm preparation. What is known already The DNA integrity of the sperm plays an important role to ensure formation of good quality embryos with increased potential of fertilization, growth and ultimately implantation.. Centrifugation has shown to add stress to the sperm and leading to DNA damage, therefore there is a need to develop techniques of sperm preparation which help in retrieving as many sperms with intact DNA from the unprocessed sample as possible. Microfludic is fluid dynamic based technique of sperm preparation. in this study, we evaluated if microfluidic sperm sorter can recover motile sperm with better DNA integrity compared to density gradient preparation method. Study design, size, duration Prospective randomized study conducted in 80 patients undergoing IVF-ICSI with normal semen parameters (based WHO criteria 2010). DFI was done using Sperm Chromatin Dispersion (SCD) test in split semen samples prepared by microfluidic sperm sorter and density gradient method. Sperm morphology and motility were also recorded and evaluated based on the WHO 2010 criteria. Participants/materials, setting, methods Semen parameters of the sample were assessed by microscopic examination. DFI of each unprocessed sample was carried out using SCD test, following that the sample was split and sperm preparation was done using microfluidic sperm sorter and density gradient. the recovered sperm were tested for DFI and the results were compared. Main results and the role of chance Mean DFI in unprocessed semen samples was 23%. the analysis of split semen samples post preparation showed that the DFI was significantly reduced with the use of microfluidic sperm sorter (mean DFI 0.6%) as compared to density gradient (mean DFI 9%). Limitations, reasons for caution A major limitation of the microfluidic sperm sorter is the use sperm concentration and motility of the semen sample. In oligospermic and asthenospermic samples, density gradient is the preferred method of preparation. Lack of data showing improvement in clinical outcomes with reduced DFI is also a major limitation. Wider implications of the findings: Microfluidics has shown to significantly reduce the DFI of the semen sample, it requires no extra equipment and cost and is relatively easy to pick up. Density gradient method of sperm preparation continues to be the preferred method due to its versatility and recovery of good quality sperm. Trial registration number Not applicable

P–029 Identification of spermatozoa by unsupervised learning from video data

Study question Can artificial intelligence (AI) algorithms identify spermatozoa in a semen sample without using training data annotated by professionals? Summary answer Unsupervised AI methods can discriminate the spermatozoon from other cells and debris. These unsupervised methods may have a potential for several applications in reproductive medicine. What is known already Identification of individual sperm is essential to assess a given sperm sample’s motility behaviour. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa. Study design, size, duration Three sperm-like paint images were manipulated using a graphic design tool and used to train our AI system. Two paintings have an ash colour background and randomly distributed white colour circles, and one painting has a predefined pattern of circles. Selected semen sample videos from a public dataset with videos obtained from 85 participants were used to test our AI system. Participants/materials, setting, methods Generative adversarial networks (GANs) have become common AI methods to process data in an unsupervised way. Based on single image frames extracted from videos, a GAN (SinGAN) can be trained to determine and track locations of sperms by translating the real images into localization paintings. The resulting model showed the potential of identifying the presence of sperms without any prior knowledge about data. Main results and the role of chance Visual comparisons of localization paintings to real sperm images show that inverse training of SinGANs can track sperms. Converting colour frames into grayscale frames and using grayscale synthetic sperm-like frames showed the best visual quality of generated localization paintings of sperm frames. Feeding real sperm video frames to the SinGAN at different scaling factors, which is defining the resolution of the input image, showed different quality levels of generated sperm localization paintings. A sperm frame given to the algorithm with a scaling factor of one leads to random sperm tracking, while the scales two to four result in more accurate localization maps than scaling levels five to eight. In contrast, scales from six to eight result in an output close to the input frame. The proposed method is robust in terms of the number of spermatozoa, meaning that the detection works well for samples with a low or high sperm count. For visual comparisons, visit our Github page: https://vlbthambawita.github.io/singan-sperm/. The sperm tracking speed of our SinGAN using an NVIDIA 1080 graphic processing unit, is around 17 frames per second, which can be improved by using parallel video processing capabilities. This shows the capability of using this method for real-time analysis. Limitations, reasons for caution Unsupervised methods are hard to train, and the results need human verification. The proposed method will need quality control and must be standardized. Unsupervised sperm tracking SinGAN may identify blurry bright spots as non-existing sperm heads which may restrict the use of SinGAN sperm tracking for sperm counting. Wider implications of the findings: Assessment of semen samples according to the WHO guidelines is subjective and resource-demanding. This unsupervised model might be used to develop new systems for less time-consuming and more accurate evaluation of semen samples. It may also be used for real-time analysis of prepared spermatozoa for use in assisted reproduction technology. Trial registration number N/A

P–040 Correlation between DNA fragmentation index and big halo pattern with sperm decondensation index and semen sample parameters

Study question Does sperm DNA integrity evaluated by DNA fragmentation index (DFI) and big halo pattern correlate with sperm decondensation index (SDI) and semen sample parameters? Summary answer DFI correlates with SDI and semen sample parameters in a stronger way than the big halo pattern What is known already The sperm chromatin dispersion test evaluates DNA integrity by measuring the susceptibility of sperm DNA containing breaks to denature when treated by an acid solution. Spermatozoa with intact DNA produce big or medium size halos of dispersed DNA loops, whereas small halos or no halos indicate fragmented DNA. The DFI calculates the proportion of spermatozoa with fragmented DNA. Data have been published documenting the negative effect of sperm DFI on embryo viability, suggesting that its evaluation could contribute to the prediction of the male reproductive potential Study design, size, duration A prospective study between 2011 to 2019 included 300 patients attending our clinic for fertility treatment. All sperm samples were analyzed according to WHO criteria, and the results from the DNA integrity analysis were related to the semen sample indices Participants/materials, setting, methods Of the 300 males included in the study, 118 were normozoospermic, 16 were oligozoospermic (O), 63 were asthenozoospermic (A), 9 were teratozoospermic, 7 were AT, 51 were OA, 5 were OT, and 31 OAT. The DNA integrity was assessed by the Halosperm test, and DNA decondensation by the aniline blue assay. A big halo was defined as a dispersion greater or equal to the length of the minor diameter of the core Main results and the role of chance DFI showed negative correlations with progressive motility (r= –0.532, p = 2.816 E–23), total motility (r= –0.598, p = 1.688 E–30) and morphology (r= –0.338, p = 2.954 E–9). Accordingly, when compared with normozoospermic, DFI was significantly higher in A and T samples (29.5 ±12.0 and 36.5±4.8 respectively, p < 0.002) with the highest levels found in samples with combined defects (45.2±12.5 in AT, p < 0.002; 51.3±17.2 in OAT, p < 0.002). DFI also showed a negative correlation with the big halo pattern (r= –0.656, p = 2.934 E–38) and a positive correlation with the SDI (r = 0.429, p = 7.314 E–15). For the big halo, negative correlations were found with progressive motility (r = 0.429, p = 7.314 E–15) and morphology (r = 0.407, p = 4.077 E–13) resulting in a lower incidence in T samples (27.0±9.6, p < 0.002) that was especially relevant in AT (18.3±14.5, p < 0.002), OT (33.0±10.2, p < 0.02) and OAT samples (20.6±15.8, p < 0.002). SDI presented a negative correlation with total motility (r= –0.403, p = 3.849 E–13) and was fond to be increased in A samples (32.4±11.8, p < 0.002) as well as in samples with double defects (38.9±19.2 in AT samples and 38.8±15.9 in OA samples, p < 0.002) and triple defects (42.6±16.8 in OAT, p < 0.002) Limitations, reasons for caution The study did not evaluate the lifestyle and reproductive history of the patients Wider implications of the findings: Although the effects of sperm DNA damage on reproductive outcomes are still unclear, the correlation between sperm DNA fragmentation, semen parameters and reproductive potential is emerging. DFI, big halo and SDI could contribute to the diagnosis of male infertility especially in categories of patients with poor prognosis of pregnancy. Trial registration number Not applicable

P–123 How to develop accurate Computer Assisted Sperm Analysis (CASA) AI in the absence of protocol standardization and abundance of human error when performing semen analyses?

Study question How can an automation & artificial intelligent tools be developed to perform according to WHO recommendations? Summary answer Developing CASA performs at < 20% error margin requires AI trained with high quality datasets and a robotic system adheres to WHO guidelines. What is known already A survey of 40 andrology laboratories, in 22 countries, revealed that > 90% had nonconformities in correct use of equipment, standardisation of protocols and quality control, leading to a lack of compliance to WHO protocols. Conventional CASA systems can standardize analysis, but controversy has occurred due to differences between manual and automated analyses stemming from: 1) all cells in a semen sample are detected including debris; 2) protocol variation when compared to top-notch manual analysis. The first point can be addressed by AI. The second point can be addressed by robotics designed to adhere to WHO guidelines. Study design, size, duration A mojo AISA (AI-powered semen analysis) system was placed in four clinical laboratories mentioned above capturing images of over 300 samples, one million images were generated over a course of 2 years. Mojo AISA’s AI was trained on data collected from the four clinics using robotic system is developed according to WHO guidelines. Participants/materials, setting, methods For an AI to detect sperm accurately, sperm samples were captured using mojo AISA smart microscopy and then the extracted sperm images expertly annotated. To evaluate the system-ability for semen analysis, fresh sample were analysed for concentration and motility by a manual operator and compared to a mojo AISA test. Main results and the role of chance To train the sperm detection AI, representative sperm images were carefully captured using mojo AISA and processed according to the following criteria: the number of images and videos to train and to test the model: 50,000 spermatozoon head and tails with various variations the variety of images: data used to train the AI has to be representative of the population that will undergo the analysis: 1) wide concentration ranges from 0 to 300 M/ml, 2) high and low density of debris and cells, 3) Presence of slight aggregations careful and precise annotation: expert andrology scientists annotated sperm images and identify objects to exclude, such as debris in seminal plasma, Mojo AISA is an attempt strictly build CASA AI system to WHO-guidelines. The marriage of AI and robotics automation has shown a promising results to mimic humans when measuring a semen sample and attempt to obtain results comparable to the manual analysis. mojo AISA’s performance improved three-fold (from 0,85 to 0,95 Pearson sperm count correlation and from >100% means relative error to 25% mean relative error). Limitations, reasons for caution Lack of standardization for semen analysis laboratory process globally is a bottleneck towards building a robust multi-center study, on-site CASA testing and generating an actionable data pool for studying the causes behind male fertility declineWider implications of the findings: Key learnings for parties advancing developing AI based on images and videos for application in the fertility space. Trial registration number Not applicable

SEMEN BACTERIA FLORA OF RHODE ISLAND BREEDER COCKS IN ZARIA, KADUNA STATE, NIGERIA.

The semen used in this was collected from 77 Island Breeder cocks reared in battery cages under intensive management fro a farm in Zaria, Kaduna state, Nigeria using back message procedure, 27 of 77 semen sample (35.1%) contained bacteria isolates.None of the sample grew fungi. Bac teria isolates obtained from the semen include:Escherichia, coli, staphylococcus, aureus, streplococcus faecalis, Proteus species and Klebsiella species. Seventy of the semen sample were negative for brucellosis but sevrn sample exhibited Brucella specie agglutinins using tube agglutination test and level of antibody titres are 61.5, 82.0 and 102.5 iu/ml respectively. The presence of agglutinin detected in this study is significant since brucellosis is of public health and significance. In addition, the presence of bateria contaminantsin semen should be viewed with seriouness. As a consequence, routine control of bateria in collected semen desirable. This study sought to identify the bateria flora and pathogens in semen collected from cocks and see how they be effectively reduced or destroyed in the interest of the efficient collection, preservation and delivery of highly fertile semen artificially. Areas for further investigation were highlighted.