Study unveils protein complex critical for sperm morphology

It’s long been thought that sperm with Y chromosomes, or “boy” sperm, swim faster than sperm with X chromosomes, known as “girl” sperm. The exact cause for low sperm motility can vary. Some men may have a genetic cause, while others may have an undiagnosed medical condition. Lifestyle and environmental factors also play a big role in sperm motility. Smoking, for example, has A condition called varicocele occurs when veins inside the scrotum become enlarged. This has also been linked to decreased sperm motility. Low sperm motility may also be due to a disorder in the male accessory sex gland secretion, which leads to the glands emptying more slowly. Sperm motility can be tested through a routine semen analysis. For the test, you’ll need to provide at least two semen samples. These are usually obtained by masturbation at a doctor’s office or testing facility. It’s also possible to obtain a sperm sample by having sex with a condom or withdrawing to obtain the sample. The sample must be kept at room temperature and delivered to the facility within 30 to 60 minutes. If less than 40 percent of your sperm are motile, your considered to have low sperm motility. In addition to sperm motility, your doctor can also use a semen analysis to test:

‌It’s quite possible! Male fertility is a complex issue in which many factors play a role. Most scientists are adamant that fertilization is possible even in cases of severe structural abnormality among high percentages of sperm. Moreover, just as the body constantly builds and replaces its cells, it constantly builds and replaces its own sperm. The sperm that’s there today is quite literally not the sperm that will be there tomorrow, so many lifestyle changes can be made to improve all your body’s cellular “factories,” including its reproductive system. These include the following: ‌

Affimers are small in size, stable at different temperature ranges, relatively easy to generate, and offer appealing opportunities to complement the shortcomings of antibodies. Their small size is thought to be one of the attributes making affimers more sensitive than antibodies when used in histochemical staining, as they can penetrate tissues better. Affimers are both commercially available and can be synthesized by individual laboratories using publicly available methods, thereby ensuring it is accessible to the entire scientific community. One key advantage of affimers, as opposed to antibodies or nanobodies, is the speed at which they can be screened and developed. The screening process for affimers involves three iterations of phage display and ELISA assays. This can be carried out in 12 days, which is significantly less than the time needed for antibody and nanobody animal inoculation times. Furthermore, the screening process by which binding reagents are identified is more efficient because the target proteins are screened in a folded state.

One of the main reasons why compounds fail is that they actually don't work in the patients they are tested in. We may have picked the wrong target since although we've actually created a molecule that, for example, inhibits the activity of the protein in the disease, it may actually turn out that over activity of that e protein isn't as important as we thought it was in that disease. The other reasons molecules fail now is primarily unexpected toxicity. You could take a molecule all the way through to phase 3 trials – the biggest trials in patients – and suddenly find that in your long-term, two-year carcinogenicity studies in animals, that you've got something that is quite cancer-provoking, so you have to stop development. Those are the two reasons: efficacy and toxicity. Somehow, we've got to find a way of trying to increase the chances of success or making the process less costly and leaner.

Toxicology is a very diverse field, spanning chemistry, biology, and clinical science. Data varies enormously in accessibility, quantity, quality, terminology, and format. That makes it a complex domain for AI, but also one where AI can be very useful when applied carefully. A realistic AI-assisted safety pipeline today is not about replacing the expert toxicologist. It is about augmentation. AI agents and Large Language Models (LLMs) can autonomously search for data, collate information, generate summaries, and produce plots. This allows safety scientists to bring their expertise to bear more efficiently, while AI helps uncover and organize the evidence. We should also remember task-specific models. DeepDILI, for example, combined classical and deep learning approaches to predict drug-induced liver injury (DILI), a major cause of post-marketing drug withdrawals. Models like this can have a real impact when focused on specific safety pain points.