Objective: In this study, specimens from testicular biopsies of men with nonobstructive azoospermia (NOA) were used to investigate whether RNF8 gene could serve as a biomarker to predict the presence of sperm in these patients. Methods: Testicular biopsy specimens from 47 patients were classified according to the presence of sperm (positive vs. negative groups) and investigated for the expression of RNF8. The level of RNF8 gene expression in the testes was compared between these groups using reverse-transcription polymerase chain reaction. Results: The expression level of RNF8 was significantly higher in testicular samples from the positive group than in those from the negative group. Moreover, the area under the curve of RNF8 expression for the entire study population was 0.84, showing the discriminatory power of RNF8 expression in differentiating between the positive and negative groups of men with NOA. A receiver operating characteristic curve analysis showed that RNF8 expression had a sensitivity of 81% and a specificity of 84%, with a cutoff level of 1.76. Conclusion This study points out a significant association between the expression of RNF8 and the presence of sperm in NOA patients, which suggests that quantified RNF8 expression in testicular biopsy samples may be a valuable biomarker for predicting the presence of spermatozoa in biopsy samples.

Purpose: Despite all past efforts, the current guidelines are not explicit enough regarding the indications for performing azoospermia factor (AZF) screening and karyotype, burdening clinicians with the decision to assess whether such tests are meaningful for the infertile male patient. These assessments can be costly and it is up to the healthcare practitioner to decide which are necessary and to weigh the benefits against economic/psychological harm. The aim of this study is to address such gaps and provide update on current management options for this group of patients. Materials and Methods: To address such gaps in male infertility management and to elucidate whether AZF screening is indicated in individuals who concomitantly harbor chromosomal abnormalities we conducted a retrospective cohort analysis of 10,388 consecutive patients with non-obstructive azoospermia (NOA) and severe oligozoospermia. Results: Previously, it has been suggested that all NOA cases with chromosomal defects, except males with 46,XY/45,X karyotype, have no indication for AZF screening. Our findings revealed that cases carrying the following chromosomal abnormalities inv(Y)(p11.2q12); idic(Y)(q11.2); 46,XY,r(Y); idic(Y)(p11.2) and der(Y;Autosome) (76/169; 44.9%; 95% CI, 37.7–52.5) should also be referred for AZF deletion screening. Here, we also report the correlation between sperm count and AZF deletions as a secondary outcome. In accordance with previously reported data from North America and Europe, our data revealed that only 1% of cases with >1×106 sperm/mL had Y chromosome microdeletions (YCMs). Conclusions In the era of assisted reproduction, finding cost-minimization strategies in infertility clinics without affecting the quality of diagnosis is becoming one of the top prioritized topics for future research. From a diagnostic viewpoint, the results reflect a need to reconsider the different karyotype presentations and the sperm count thresholds in male infertility guidelines as indicators for YCM screening during an infertility evaluation.

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.