We Have A Winner
In assisted reproductive technology (ART), the aim of sperm cells' preparation is to select competent spermatozoa with the highest fertilization potential and in this context, the intracytoplasmic sperm injection (ICSI) represents the most applied technique for fertilization. This makes the process of identifying the perfect spermatozoa extremely important. A number of methods have now been developed to mimic some of the natural selection processes that exist in the female reproductive tract. Although many studies have been conducted to identify the election technique, many doubts and disagreements still remain. In this review, we will discuss all the sperm cell selection techniques currently available for ICSI, starting from the most basic methodologies and continuing with those techniques suitable for sperm cells with reduced motility. Furthermore, different techniques that exploit some sperm membrane characteristics and the most advanced strategy for sperm selection based on microfluidics, will be examined. Finally, a new sperm selection method based on a micro swim-up directly on the ICSI dish will be analyzed. Eventually, advantages and disadvantages of each technique will be debated, trying to draw reasonable conclusions on their efficacy in order to establish the gold standard method.
We Have A Winner
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But it was only this last semester, the one that just concluded this week, where I felt that at every point during the semester --- from day 1 all the way through turning in course grades yesterday --- the specs grading system I had in place was working the way I wanted. It's still not 100% there, of course, but I think I have a blueprint of how to use specs grading moving forward and of course, I want to share it with everyone.
Specs grading still uses an A/B/C/D/F course grade reporting approach, but the letter grades are earned differently. Rather than calculating complex weighted averages of points --- which you can't do because there are no points --- letter grades are earned by completing "bundles" of work which increase in size and scope as the letter grade being targeted goes higher. The idea is that students who want a "C" in the course have to do a certain amount of work that meets the specs; those wanting a "B" have to do everything the "C" people do, but more of it and of higher quality and/or difficulty level. Similarly the "A" students do everything the "B" students do plus even greater quantity and quality.
Students could come on these Fridays and take as many or as few of these Learning Target assessments as they wanted. Only the Learning Targets that we'd discussed in class were available, but once they were available they were always available. Previously-given Learning Target assessments would have new versions of the same problem available to do. So a student who didn't feel ready to be assessed on Learning Target G.6 didn't have to take the assessment for G.6, but just wait two weeks and try it then.
At the end of the course students took a final exam. The final exam consisted of eight randomly selected Learning Target assessments that were given previousy in the course along with a final question for feedback on the course. The Learning Targets were selected so that at least one Learning Target from each of the four main course topics was represented. I'd never given a final exam in a specs grading class before this semester; in past classes, the final exam period was set aside as one more session for any student who needed to pass Learning Targets to have a chance to do so. I instituted the final exam this time because I wasn't satisfied that the combo of Learning Target assessments plus Challenge Problems was giving me reliable data about student learning. I was getting students who would pass a Learning Target early in the course, then forget that they had done so, and "accidentally" retake that Learning Target later... and not pass it. So I wanted to have one additional layer of assessment to get students to recertify on the basic skills at the end of the course. The fact that all I was doing was recycling old Learning Target assessments made this easy to make up, by just randomly selecting the Learning Targets and assessment versions and then merging the PDFs. (I made four different versions for test security.)
I broke again from the specs grading mold and graded the final using points, grading each recycled Learning Target with either 0, 4, 8, or 12 points. A 12-point score was given if the work would have earned Satisfactory marks according to the original specs, 8 if it was "almost Satisfactory", and so on. The feedback questions were given 4 points, bringing the total to an even 100 points.
The grade of "C" is considered "baseline competency", and to earn that grade you have to complete the "C bundle", which is passing 75% of the Learning Targets and completing five Challenge Problems, with no requirement of excellent/exemplary work required. The "B bundle" is everything in the "C bundle" with more Learning Targets passed and more Challenge Problems completed plus some evidence of excellent/exemplary work. The "A bundle" is likewise everything in the "B bundle" with even more Learning Targets and Challenge Problems completed plus even more extensive evidence of excellent/exemplary work. Notice, students get to choose which Challenge Problems they attempt -- we had 17 Challenge Problems in all and students just picked the ones they liked.
In my view, the whole plus/minus system here fouls up what it otherwise a beautifully simple grading system, but according to the Dean's office I have to have some plus/minus system in place. This is the best I could come up with.
I haven't gotten back evaluations for this course yet, so I just have verbal feedback and mid-semester surveys to go on. But based on what I have, students were totally thrilled by this system. They remarked about how it took a little while to get used to it, but once they "got it" they wished that all their other courses did the same thing. Computer science majors in particular --- who make a career out of determining how to debug their code from feedback given by the compiler --- really resonate with the idea of being able to debug their math work by using detailed feedback. I've been contacted by at least one other professor in the CS department here who's had students from my course talk about how much they appreciated it and how much it helped them learn.
Likewise, this system inverts the way students tend to approach the course as a whole, that is by coming to class without a clear idea of what they want out of it and just hoping for the best. Here, students have to think about the grade they want to earn first, then this tells them which "bundle" to look at in the syllabus and this lays out an agenda for what they need to accomplish in the course. There is no "hoping for a grade"; the student is in control and we talk about targeting the grade you wish to earn instead of hoping for the grade you think you "deserve".
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This is widely confirmed by many studies based on animal models: when exposed to low doses of chemotherapeutic agents providing an amount of reactive oxygen species (ROS), animals produce sperm cells with epigenetic defects, which lead to deformity in the offspring and are often inherited in the following generations . Human epidemiological studies  have shown a correlation between paternal age and correct neurological development. Some studies have shown that the lack of acrosomal reaction and the persistence of the perinuclear theca on the sperm cell head is associated with a delay in de-condensation of the genetic material at the time of fertilization . Other studies have shown that male subjects born from ICSI generate seminal fluid with a low number of cells and with reduced motility when compared to subjects born spontaneously . Further, ICSI has been associated with an increased risk for many health issues, ranging from premature births and diverse metabolic disorders in the offspring to more severe complications, such as abortions, congenital malformations, and imprinting disorders . It has been suggested that one of the reasons for the relatively low efficiency of ART is that we currently lack an effective methodology to separate this specific sperm subpopulation for its use in ARTs [12,13].
Unfortunately, classic methods have given unsatisfactory results, as they rely on separation mechanisms dependent on morphology and motility, such as swim up (SU) and density gradient centrifugation (DGC) . To be adopted in the ART routine procedures, sperm cell preparation techniques should be simple, cheap and fast, allowing a highly efficient selection that differentiates motile and morphologically normal spermatozoa from other cell species, leukocytes or bacteria and toxic substances, avoiding the production of ROS .