As the age of paternity rises in the developed world, issues of chronic disease may affect prospective fathers. Given the high prevalence of hypertension, researchers have begun to explore the relationship between hypertensive disease and male fertility. The current literature suggests an association between hypertension and semen quality. The use of various antihypertensive medications has also been linked to impaired semen parameters, making it difficult to discern whether the association exists with hypertension or its treatment. Further investigation is warranted to determine whether the observed associations are causal.
Adrenergic beta-Antagonists ; Chronic Disease ; Fathers ; Fertility* ; Humans ; Hypertension* ; Infertility ; Male* ; Paternity ; Prevalence ; Prospective Studies ; Semen ; Semen Analysis

PURPOSE: To investigate the fatigue and fracture resistance of computer-aided design and computer-aided manufacturing (CAD/CAM) ceramic molar crowns on dental implants and human teeth. MATERIALS AND METHODS: Molar crowns (n=48; n=8/group) were fabricated of a lithium-disilicate-strengthened lithium aluminosilicate glass ceramic (N). Surfaces were polished (P) or glazed (G). Crowns were tested on human teeth (T) and implant-abutment analogues (I) simulating a chairside (C, crown bonded to abutment) or labside (L, screw channel) procedure for implant groups. Polished/glazed lithium disilicate (E) crowns (n=16) served as reference. Combined thermal cycling and mechanical loading (TC: 3000×5℃/3000×55℃; ML: 1.2×106 cycles, 50 N) with antagonistic human molars (groups T) and steatite spheres (groups I) was performed under a chewing simulator. TCML crowns were then analyzed for failures (optical microscopy, SEM) and fracture force was determined. Data were statistically analyzed (Kolmogorow-Smirnov, one-way-ANOVA, post-hoc Bonferroni, α=.05). RESULTS: All crowns survived TCML and showed small traces of wear. In human teeth groups, fracture forces of N crowns varied between 1214±293 N (NPT) and 1324±498 N (NGT), differing significantly (P≤.003) from the polished reference EPT (2044±302 N). Fracture forces in implant groups varied between 934±154 N (NGI_L) and 1782±153 N (NPI_C), providing higher values for the respective chairside crowns. Differences between polishing and glazing were not significant (P≥.066) between crowns of identical materials and abutment support. CONCLUSION: Fracture resistance was influenced by the ceramic material, and partly by the tooth or implant situation and the clinical procedure (chairside/labside). Type of surface finish (polishing/glazing) had no significant influence. Clinical survival of the new glass ceramic may be comparable to lithium disilicate.
Ceramics ; Computer-Aided Design ; Crowns ; Dental Implants ; Fatigue ; Glass ; Humans ; In Vitro Techniques ; Lithium ; Mastication ; Microscopy ; Molar ; Tooth

Sperm identification and selection is an essential task when processing human testicular samples for in vitro fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.