Sperm during the freezing and thawing process is damaged by oxidative stress. Thus, its antioxidant scavenger is essential for sperm survival and death in frozen–thawed semen. We used melatonin and silymarin in experiments after the dose-dependent experiment. Our study aimed to identify the effect of melatonin and silymarin on the motility and viability of sperm, reactive oxygen species (ROS), and nitric oxide (NO) production in frozen–thawed boar semen. Melatonin and silymarin were treated alone and cotreated in the fresh boar semen. Boar semen was collected using the gloved-hand method from ten crossbred pigs, and samples were used in the experiments. We evaluated sperm viability using SYBR-14 and PI kit, and ROS and NO production were detected by DCF-DA and DAF-2, respectively. The sperm motility was not significantly different between non-treatment and treatment. ROS and NO production in frozen–thawed sperm were decreased by melatonin and silymarin. Moreover, silymarin significantly reduced NO production more than melatonin. Melatonin and silymarin enhanced the viability of sperm. We suggest that melatonin and silymarin are essential antioxidants in semen cryopreservation for protecting sperm damage and maintaining sperm viability. Melatonin and silymarin may be useful antioxidants in freezing boar sperm.

Magnetized water is defined as the amount of water that has passed through a magnet. The magnetic field weakens the hydrogen bonds between the water molecules, leading to the magnetized liquid acquiring special characteristics such as easy supercooling and forming smaller ice crystals. We researched the influences of a magnetized freezing extender on cell membrane damage and in vitro fertilization of boar sperm during cryopreservation. The freezing extenders were passed through 0, 2000, 4000, and 6000 gausses (G) of magnetic devices using a liquid cycling pump system and then used for the sperm freezing process. The damage to plasma, acrosomal, and mitochondrial membranes in frozen-thawed spermatozoa was investigated by flow cytometry, and motility was assessed using the CASA system. The fertility of frozen-thawed sperm was estimated using in vitro fertilization. The damage to the membranes was significantly decreased in the magnetized freezing extender by the 6000 G magnetic field compared to that of the control in frozen-thawed sperm, and motility was increased in the 6000 G group. Although there were no significant differences in the cleavage rates of in vitro fertilized oocytes among the treatment groups, the ratio of blastocyst formation increased in the magnetized freezing extender groups compared with that in the control group. The number of blastocysts was significantly higher in the 4000 G group than in the 0 G group. In conclusion, these results suggest that a magnetized freezing extender could improve the freezability of sperm and the development of oocytes fertilized in vitro with frozen-thawed sperm.

The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception–ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception–ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system’s calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.

So-Yeon Jo, Yong Hwangbo, Sang-Hee Lee, Hee-Tae Cheong, Dong-Ku Kim and Choon-Keun Park. Journal of Animal Reproduction and Biotechnology 2021;36:239-46. https://doi.org/10.12750/JARB.36.4.239

Identification of amyloid beta ( $\text{A}\beta$ ) plaques in the cerebral cortex in models of Alzheimer’s Disease (AD) is of critical importance for research into therapeutics. Here we propose an innovative framework which automatically measures $\text{A}\beta $ plaques in the cortex of a rodent model, based on anatomical segmentation using a deep learning approach. The framework has three phases: data acquisition to enhance image quality using preprocessing techniques and image normalization with a novel plaque removal algorithm, then an anatomical segmentation phase using the trained model, and finally an analysis phase to quantitate $\text{A}\beta $ plaques. Supervised training with 946 sets of mouse brain section annotations exhibiting $\text{A}\beta $ protein-labeled plaques ( $\text{A}\beta $ plaques) were trained with deep neural networks (DNNs). Five DNN architectures: FCN32, FCN16, FCN8, SegNet, and U-Net, were tested. Of these, U-Net was selected as it showed the most reliable segmentation performance. The framework demonstrated an accuracy of 83.98% and 91.21% of the Dice coefficient score for atlas segmentation with the test dataset. The proposed framework automatically segmented the somatosensory cortex and calculated the intensity and extent of $\text{A}\beta $ plaques. This study contributes to image analysis in the field of neuroscience, allowing region-specific quantitation of image features using a deep learning approach.