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The Veterinary Record Jul 2023
PubMed: 37477317
DOI: 10.1002/vetr.3269 -
Animal Reproduction Science May 2024The optimization of processes associated with artificial insemination (AI) is of great importance for the success of the pig industry. Over the last two decades, great... (Review)
Review
The optimization of processes associated with artificial insemination (AI) is of great importance for the success of the pig industry. Over the last two decades, great reproductive performance has been achieved, making further significant progress limited. Optimizing the AI program, however, is essential to the pig industry's sustainability. Thus, the aim is not only to reduce the number of sperm cells used per estrous sow but also to improve some practical management in sow farms and boar studs to transform the high reproductive performance to a more efficient program. As productivity is mainly influenced by the number of inseminated sows, guaranteeing a constant breeding group and with healthy animals is paramount. In the AI studs, all management must ensure conditions to the health of the boars. Some strategies have been proposed and discussed to achieve these targets. A constant flow of high-quality and well-managed breeding groups, quality control of semen doses produced, more reliable technology in the laboratory routine, removal of less fertile boars, the use of intrauterine AI, the use of a single AI with control of estrus and ovulation (fixed-time AI), estrus detection based on artificial intelligence technologies, and optimization regarding the use of semen doses from high genetic-indexed boars are some strategies in which improvement is sought. In addition to these new approaches, we must revisit the processes used in boar studs, semen delivery network, and sow farm management for a more efficient AI program. This review discusses the challenges and opportunities in adopting some technologies to achieve satisfactory reproductive performance and efficiency.
PubMed: 38782677
DOI: 10.1016/j.anireprosci.2024.107501 -
Australian Veterinary Journal May 2024A number of methods are currently used to predict the optimal date of insemination of the breeding bitch, particularly with the use of frozen-thawed canine semen which... (Review)
Review
A number of methods are currently used to predict the optimal date of insemination of the breeding bitch, particularly with the use of frozen-thawed canine semen which has a far shorter lifespan than fresh semen. Aside from confirming cytological oestrus, vaginal cytology is of no assistance in predicting the most fertile day(s) in a bitch; however, a neglected avenue of research suggests that vaginal cytology may be of great importance in confirming the days of optimal fertility retrospectively. Similarly, vaginoscopy provides clues as to the stage of a bitch's cycle but is inadequate as a sole determinant of her most fertile days. Nevertheless, vaginoscopy is useful to identify very late oestrus and the onset of dioestrus, as well as Stage I of labour (cervical dilatation). Due to variations in the rate at which circulating progesterone concentrations rise in individual bitches, the reliability of circulating progesterone concentrations for determining the optimal day(s) of insemination with frozen-thawed semen decreases as values rise. Moreover, progesterone assay results can vary widely due to extrinsic factors such as the time of blood sampling, sample storage conditions and the assay employed. Finally, this review investigates evidence surrounding various insemination routes and suggests that well-performed vaginal insemination, even with frozen-thawed semen, may be an acceptable approach for cases where transcervical insemination is impractical.
PubMed: 38733177
DOI: 10.1111/avj.13336 -
Fertility and Sterility Sep 2023There have been concerns on the potential overuse of in vitro fertilization (IVF) in view of the lack of evidence on effectiveness in certain populations, potential... (Review)
Review
There have been concerns on the potential overuse of in vitro fertilization (IVF) in view of the lack of evidence on effectiveness in certain populations, potential short and long-term safety risks, and economic considerations. On the other hand, the use of alternatives to IVF seems to be underappreciated in clinical practice as well as research. In this review, we summarized the up-to-date evidence on the effectiveness, safety as well as cost-effectiveness of different alternatives to IVF, including expectant management, intrauterine insemination, tubal flushing, in vitro maturation as well as intravaginal culture. We also discussed the trend of IVF use over the last decade and the available tiers of service because of intravaginal culture, and revisited the roles of different alternatives to IVF in modern reproductive medicine from both clinical and research perspectives.
Topics: Female; Humans; Insemination, Artificial; Fertilization in Vitro; Reproduction; Cost-Benefit Analysis; Cost-Effectiveness Analysis
PubMed: 36642301
DOI: 10.1016/j.fertnstert.2023.01.011 -
The Veterinary Clinics of North... Mar 2024The cause of subfertility or poor fertility in naturally mated bulls should be differentiated from impotentia coeundi, generandi, or erigendi prior to ancillary semen... (Review)
Review
The cause of subfertility or poor fertility in naturally mated bulls should be differentiated from impotentia coeundi, generandi, or erigendi prior to ancillary semen evaluation. Bulls used for artificial insemination may undergo ancillary semen evaluation following low fertility rates as judged by poor conception or low pregnancy rates. Morphologically abnormal sperm have long been associated with bull subfertility and infertility. Some morphological defects such as improper sperm chromatin condensation are not visible using traditional light microscopy and require specialized staining. Ancillary semen evaluation is useful in cases where the reason for low or absence of fertility needs to be identified. As compared to SEM, TEM can be extremely useful for identifying minuscule acrosome defects, issues with chromatin, and centrosome defects and is considered the gold standard method for the identification of midpiece and tail defects.
Topics: Pregnancy; Female; Male; Animals; Cattle; Semen; Spermatozoa; Fertility; Insemination, Artificial; Chromatin; Infertility; Cattle Diseases
PubMed: 37442678
DOI: 10.1016/j.cvfa.2023.06.002 -
Molecular Reproduction and Development Jul 2023Over the years, reproductive efficiency in the swine industry has focused on reducing the sperm cell number required per sow. Recent advances have included the... (Review)
Review
Over the years, reproductive efficiency in the swine industry has focused on reducing the sperm cell number required per sow. Recent advances have included the identification of subfertile boars, new studies in extended semen quality control, new catheters and cannulas for intrauterine artificial insemination (AI), and fixed-time AI under commercial use. Therefore, it is essential to link field demands with scientific studies. In this review, we intend to discuss the current status of porcine AI, pointing out challenges and opportunities to improve reproductive efficiency.
Topics: Swine; Animals; Male; Female; Semen; Semen Analysis; Fertility; Insemination, Artificial; Sperm Count; Semen Preservation; Spermatozoa
PubMed: 36063484
DOI: 10.1002/mrd.23643 -
Fertility and Sterility Nov 2023To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed...
OBJECTIVE
To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed intercourse (TI) in natural cycles.
DESIGN
A retrospective cohort study.
SETTING
A large in vitro fertilization unit.
PATIENT(S)
Patients who underwent 2,467 natural cycle-frozen embryo transfer cycles between 2018 and 2022.
INTERVENTION(S)
None.
MAIN OUTCOME MEASURE(S)
Prediction accuracy of the optimal day for performing insemination or TI.
RESULT(S)
The data set was split into a training set including 1,864 cycles and 2 test sets. In the test sets, ovulation was determined according to either expert opinion, with 2 independent fertility experts determining ovulation day ("expert") (496 cycles), or according to the disappearance of the leading follicle between 2 consecutive days' ultrasound examinations ("certain ovulation") (107 cycles). Two algorithms were trained: an NGBoost machine learning model estimating the probability of ovulation occurring on each cycle day and a treatment management algorithm using the learning model to determine an optimal insemination day or whether another blood test should be performed. The estradiol progesterone and luteinizing hormone levels on the last test performed were the most influential features used by the model. The mean numbers of tests were 2.78 and 2.85 for the "certain ovulation" and "expert" test sets, respectively. In the "expert" set, the algorithm correctly predicted ovulation and suggested day 1 or 2 for performing insemination in 92.9% of the cases. In 2.9%, the algorithm predicted a "miss," meaning that the last test day was already ovulation day or beyond, suggesting avoiding performing insemination. In 4.2%, the algorithm predicted an "error," suggesting performing insemination when in fact it would have been performed on a nonoptimal day (0 or -3). The "certain ovulation" set had similar results.
CONCLUSION(S)
To our knowledge, this is the first study to implement a machine learning model, on the basis of the blood tests only, for scheduling insemination or TI with high accuracy, attributed to the capability of the algorithm to integrate multiple factors and not rely solely on the luteinizing hormone surge. Introducing the capabilities of the model may improve the accuracy and efficiency of ovulation prediction and increase the chance of conception.
CLINICAL TRIAL REGISTRATION NUMBER
HMC-0008-21.
Topics: Female; Humans; Pregnancy; Artificial Intelligence; Retrospective Studies; Ovulation Induction; Luteinizing Hormone; Fertilization in Vitro; Insemination; Insemination, Artificial; Pregnancy Rate
PubMed: 37490977
DOI: 10.1016/j.fertnstert.2023.07.008 -
Animal Reproduction Science May 2024Feeding of breeding boars in artificial insemination (AI) centers is critical to maintaining and improving breeding quality and performance in agriculture. Modern... (Review)
Review
Feeding of breeding boars in artificial insemination (AI) centers is critical to maintaining and improving breeding quality and performance in agriculture. Modern feeding strategies for AI boars are aiming towards maximizing their lifetime semen dose output. Given the high growth potential of modern swine genetics, AI boars should be controlled in their daily gain to reduce stress factors for the locomotion system, final body weights, and improve their survivability but also the ease of handling boars. The feeding program should be designed in such a way that young boars (up to 7 months of age) are limited to a daily gain of 400-600 g. Mature boars should be fed towards a body condition score of '2'. Aside from energy intake, protein sources should provide 0.62% SID lysine. As far as minerals and vitamins, special attention should be given to Calcium and Phosphorus as they play a crucial role in bone mineralization. A standardized total tract digestible Calcium-Phosphorus ratio between 1.75:1 and 1.82:1 seems to be most favorable. While certain nutritional requirements are needed to enable the production of ejaculates eligible for AI, considerations should be given to the mitigation of risk factors like mycotoxins, herbicides, and pesticides. Most feed additives and supplements lack consistency in their effect across studies evaluated in this review.
PubMed: 38744579
DOI: 10.1016/j.anireprosci.2024.107497 -
Therapeutic Advances in Reproductive... 2023Many factors associated with assisted reproductive technologies significantly influence the success of pregnancy after fertilization (IVF) either directly or... (Review)
Review
Many factors associated with assisted reproductive technologies significantly influence the success of pregnancy after fertilization (IVF) either directly or indirectly. These factors include sperm processing techniques, egg retrieval, intrauterine artificial insemination, intracytoplasmic sperm injection, and embryo transfer. Among these technologies, sperm quality is one of the most critical factors for a successful IVF pregnancy. The method used for sperm processing plays a crucial role in determining the quality of sperm. Several widely used sorting techniques, such as conventional swim-up, density gradient centrifugation, magnetic activated cell sorting, and hyaluronic acid, have been extensively compared in various studies. Previous studies have shown that each sperm processing method causes varying degrees of sperm damage, particularly in sperm motility, concentration, morphological features, viability, and DNA integrity. However, sperm processing techniques have been developed slowly, and the impact of these methods on pregnancy rates is still unclear. Further exploration is needed. In this review, we aim to compare the results of different sperm processing techniques concerning sperm quality and IVF pregnancy rates. We will also discuss possible clinical approaches, such as microfluidics and integrated approaches, for testing and improving sperm quality.
PubMed: 37497119
DOI: 10.1177/26334941231188656 -
The Cochrane Database of Systematic... Sep 2023In vitro fertilisation (IVF) is a treatment for unexplained subfertility but is invasive, expensive, and associated with risks. (Review)
Review
BACKGROUND
In vitro fertilisation (IVF) is a treatment for unexplained subfertility but is invasive, expensive, and associated with risks.
OBJECTIVES
To evaluate the effectiveness and safety of IVF versus expectant management, unstimulated intrauterine insemination (IUI), and IUI with ovarian stimulation using gonadotropins, clomiphene citrate (CC), or letrozole in improving pregnancy outcomes.
SEARCH METHODS
We searched following databases from inception to November 2021, with no language restriction: Cochrane Gynaecology and Fertility Register, CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL. We searched reference lists of articles and conference abstracts.
SELECTION CRITERIA
Randomised controlled trials (RCTs) comparing effectiveness of IVF for unexplained subfertility with expectant management, unstimulated IUI, and stimulated IUI.
DATA COLLECTION AND ANALYSIS
We followed standard Cochrane methods.
MAIN RESULTS
IVF versus expectant management (two RCTs) We are uncertain whether IVF improves live birth rate (LBR) and clinical pregnancy rate (CPR) compared to expectant management (odds ratio (OR) 22.0, 95% confidence interval (CI) 2.56 to 189.37; 1 RCT; 51 women; very low-quality evidence; OR 3.24, 95% CI 1.07 to 9.8; 2 RCTs; 86 women; I = 80%; very low-quality evidence). Adverse effects were not reported. Assuming 4% LBR and 12% CPR with expectant management, these would be 8.8% to 9% and 13% to 58% with IVF. IVF versus unstimulated IUI (two RCTs) IVF may improve LBR compared to unstimulated IUI (OR 2.47, 95% CI 1.19 to 5.12; 2 RCTs; 156 women; I = 60%; low-quality evidence). We are uncertain whether there is a difference between IVF and IUI for multiple pregnancy rate (MPR) (OR 1.03, 95% CI 0.04 to 27.29; 1 RCT; 43 women; very low-quality evidence) and miscarriage rate (OR 1.72, 95% CI 0.14 to 21.25; 1 RCT; 43 women; very low-quality evidence). No study reported ovarian hyperstimulation syndrome (OHSS). Assuming 16% LBR, 3% MPR, and 6% miscarriage rate with unstimulated IUI, these outcomes would be 18.5% to 49%, 0.1% to 46%, and 0.9% to 58% with IVF. IVF versus IUI + ovarian stimulation with gonadotropins (6 RCTs), CC (1 RCT), or letrozole (no RCTs) Stratified analysis was based on pretreatment status. Treatment-naive women There may be little or no difference in LBR between IVF and IUI + gonadotropins (1 IVF to 2 to 3 IUI cycles: OR 1.19, 95% CI 0.87 to 1.61; 3 RCTs; 731 women; I = 0%; low-quality evidence; 1 IVF to 1 IUI cycle: OR 1.63, 95% CI 0.91 to 2.92; 2 RCTs; 221 women; I = 54%; low-quality evidence); or between IVF and IUI + CC (OR 2.51, 95% CI 0.96 to 6.55; 1 RCT; 103 women; low-quality evidence). Assuming 42% LBR with IUI + gonadotropins (1 IVF to 2 to 3 IUI cycles) and 26% LBR with IUI + gonadotropins (1 IVF to 1 IUI cycle), LBR would be 39% to 54% and 24% to 51% with IVF. Assuming 15% LBR with IUI + CC, LBR would be 15% to 54% with IVF. There may be little or no difference in CPR between IVF and IUI + gonadotropins (1 IVF to 2 to 3 IUI cycles: OR 1.17, 95% CI 0.85 to 1.59; 3 RCTs; 731 women; I = 0%; low-quality evidence; 1 IVF to 1 IUI cycle: OR 4.59, 95% CI 1.86 to 11.35; 1 RCT; 103 women; low-quality evidence); or between IVF and IUI + CC (OR 3.58, 95% CI 1.51 to 8.49; 1 RCT; 103 women; low-quality evidence). Assuming 48% CPR with IUI + gonadotropins (1 IVF to 2 to 3 IUI cycles) and 17% with IUI + gonadotropins (1 IVF to 1 IUI cycle), CPR would be 44% to 60% and 28% to 70% with IVF. Assuming 21% CPR with IUI + CC, CPR would be 29% to 69% with IVF. There may be little or no difference in multiple pregnancy rate (MPR) between IVF and IUI + gonadotropins (1 IVF to 2 to 3 IUI cycles: OR 0.82, 95% CI 0.38 to 1.77; 3 RCTs; 731 women; I = 0%; low-quality evidence; 1 IVF to 1 IUI cycle: OR 0.76, 95% CI 0.36 to 1.58; 2 RCTs; 221 women; I = 0%; low-quality evidence); or between IVF and IUI + CC (OR 0.64, 95% CI 0.17 to 2.41; 1 RCT; 102 women; low-quality evidence). We are uncertain if there is a difference in OHSS between IVF and IUI + gonadotropins with 1 IVF to 2 to 3 IUI cycles (OR 6.86, 95% CI 0.35 to 134.59; 1 RCT; 207 women; very low-quality evidence); and there may be little or no difference in OHSS with 1 IVF to 1 IUI cycle (OR 1.22, 95% CI 0.36 to 4.16; 2 RCTs; 221 women; I = 0%; low-quality evidence). There may be little or no difference between IVF and IUI + CC (OR 1.53, 95% CI 0.24 to 9.57; 1 RCT; 102 women; low-quality evidence). We are uncertain if there is a difference in miscarriage rate between IVF and IUI + gonadotropins with 1 IVF to 2 to 3 IUI cycles (OR 0.31, 95% CI 0.03 to 3.04; 1 RCT; 207 women; very low-quality evidence); and there may be little or no difference with 1 IVF to 1 IUI cycle (OR 1.16, 95% CI 0.44 to 3.02; 1 RCT; 103 women; low-quality evidence). There may be little or no difference between IVF and IUI + CC (OR 1.48, 95% CI 0.54 to 4.05; 1 RCT; 102 women; low-quality evidence). In women pretreated with IUI + CC IVF may improve LBR compared with IUI + gonadotropins (OR 3.90, 95% CI 2.32 to 6.57; 1 RCT; 280 women; low-quality evidence). Assuming 22% LBR with IUI + gonadotropins, LBR would be 39% to 65% with IVF. IVF may improve CPR compared with IUI + gonadotropins (OR 14.13, 95% CI 7.57 to 26.38; 1 RCT; 280 women; low-quality evidence). Assuming 30% CPR with IUI + gonadotropins, CPR would be 76% to 92% with IVF.
AUTHORS' CONCLUSIONS
IVF may improve LBR over unstimulated IUI. Data should be interpreted with caution as overall evidence quality was low.
Topics: Pregnancy; Female; Humans; Letrozole; Abortion, Spontaneous; Insemination, Artificial; Fertility Agents, Female; Fertilization in Vitro; Infertility; Clomiphene; Ovarian Hyperstimulation Syndrome; Ovulation Induction; Gonadotropins; Pregnancy Rate; Live Birth
PubMed: 37753821
DOI: 10.1002/14651858.CD003357.pub5