Controversy and consensus on indications for sperm DNA fragmentation testing in male infertility: a global survey, current guidelines, and expert recommendations.

PURPOSE: Sperm DNA fragmentation (SDF) testing was recently added to the sixth edition of the World Health Organization laboratory manual for the examination and processing of human semen. Many conditions and risk factors have been associated with elevated SDF; therefore, it is important to identify the population of infertile men who might benefit from this test. The purpose of this study was to investigate global practices related to indications for SDF testing, compare the relevant professional society guideline recommendations, and provide expert recommendations. MATERIALS AND METHODS: Clinicians managing male infertility were invited to take part in a global online survey on SDF clinical practices. This was conducted following the CHERRIES checklist criteria. The responses were compared to professional society guideline recommendations related to SDF and the appropriate available evidence. Expert recommendations on indications for SDF testing were then formulated, and the Delphi method was used to reach consensus. RESULTS: The survey was completed by 436 experts from 55 countries. Almost 75% of respondents test for SDF in all or some men with unexplained or idiopathic infertility, 39% order it routinely in the work-up of recurrent pregnancy loss (RPL), and 62.2% investigate SDF in smokers. While 47% of reproductive urologists test SDF to support the decision for varicocele repair surgery when conventional semen parameters are normal, significantly fewer general urologists (23%; p=0.008) do the same. Nearly 70% would assess SDF before assisted reproductive technologies (ART), either always or for certain conditions. Recurrent ART failure is a common indication for SDF testing. Very few society recommendations were found regarding SDF testing. CONCLUSIONS: This article presents the largest global survey on the indications for SDF testing in infertile men, and demonstrates diverse practices. Furthermore, it highlights the paucity of professional society guideline recommendations. Expert recommendations are proposed to help guide clinicians

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Does varicocele repair improve conventional semen parameters? A meta-analytic study of before-after data

Purpose The purpose of this meta-analysis is to study the impact of varicocele repair in the largest cohort of infertile males with clinical varicocele by including all available studies, with no language restrictions, comparing intra-person conventional semen parameters before and after the repair of varicoceles. Materials and Methods The meta-analysis was performed according to PRISMA-P and MOOSE guidelines. A systematic search was performed in Scopus, PubMed, Cochrane, and Embase databases. Eligible studies were selected according to the PICOS model (Population: infertile male patients with clinical varicocele; Intervention: varicocele repair; Comparison: intra-person before-after varicocele repair; Outcome: conventional semen parameters; Study type: randomized controlled trials [RCTs], observational and case-control studies). Results Out of 1,632 screened abstracts, 351 articles (23 RCTs, 292 observational, and 36 case-control studies) were included in the quantitative analysis. The before-and-after analysis showed significant improvements in all semen parameters after varicocele repair (except sperm vitality); semen volume: standardized mean difference (SMD) 0.203, 95% CI: 0.129–0.278; p<0.001; I2=83.62%, Egger’s p=0.3329; sperm concentration: SMD 1.590, 95% CI: 1.474–1.706; p<0.001; I2=97.86%, Egger’s p<0.0001; total sperm count: SMD 1.824, 95% CI: 1.526–2.121; p<0.001; I2=97.88%, Egger’s p=0.0063; total motile sperm count: SMD 1.643, 95% CI: 1.318–1.968; p<0.001; I2=98.65%, Egger’s p=0.0003; progressive sperm motility: SMD 1.845, 95% CI: 1.537%–2.153%; p<0.001; I2=98.97%, Egger’s p<0.0001; total sperm motility: SMD 1.613, 95% CI 1.467%–1.759%; p<0.001; l2=97.98%, Egger’s p<0.001; sperm morphology: SMD 1.066, 95% CI 0.992%–1.211%; p<0.001; I2=97.87%, Egger’s p=0.1864. Conclusions The current meta-analysis is the largest to date using paired analysis on varicocele patients. In the current meta-analysis, almost all conventional semen parameters improved significantly following varicocele repair in infertile patients with clinical varicocele. Keywords Controlled before-after studies; Infertility, male; Meta-analysis; Varicocel

The interface of machine learning and carbon quantum dots: from coordinated innovative synthesis to practical application in water control and electrochemistry

Not long ago, carbon quantum dots (CQDs) came into view as a revolutionary class of materials, propelling advancements in water remediation and electrochemical technology. This comprehensive review explores the cutting-edge developments in CQDs-based materials and their applications, addressing critical challenges in water treatment and electrochemical processes. Synthesized as ultra-tiny, dispersed particles with dimensions less than 10 nm, CQDs exhibit remarkable optical properties, including adjustable fluorescence emission across various colors. With a surge in published scientific articles, CQDs have garnered significant attention, offering potential solutions in heavy metal sensing, remediation, and electrocatalytic hydrogen evolution reactions (HER). The review highlights the high sensitivity of CQDs as fluorescent sensors, detecting contaminants in water with limits of detection down to femtomolar concentrations. Moreover, CQDs demonstrate excellent adsorptive capabilities for heavy metal removal, surpassing traditional adsorbents in terms of removal efficiency. Furthermore, CQDs serve as promising electrocatalysts, enhancing reaction kinetics and enabling efficient water splitting for clean energy generation. Furthermore, this review emphasizes the importance of machine learning in advancing CQDs-based materials, supported by case studies and examples that illustrate how machine learning techniques optimize CQDs synthesis, enhance their properties, and broaden their applications. However, challenges remain in the precise synthesis of CQDs, scalability of production processes, and understanding the interactions between CQDs and pollutants. Overcoming these challenges will unlock the full potential of CQDs-based materials, leading to sustainable and efficient solutions in water control and electrochemical processes.<br/