Control of polyspermic fertilization in short-term insemination

In conventional in vitro fertilization（IVF）.since sperm metabolites,granular cells and death spermatozoa may consume lots of energy in the culture medium due to longer co-incubation of oocytes and high concentration spermatozoa.The oocytes are in innutritive environment,which leads to the hardening of oocyte plasma membrane. At the same time,the high levels of estradiol（E2） and progestone（P） produced by granular cells have direct toxic effects to affect embryo cleavage,development and implantation.Therefore,short-term insemination is adopted in more and more reproductive centers. 1.Short-term insemination may increase oocyte-utilization rate,high-quality embryo rate and embryo-utilization rate.2.Retention of cumulus cells may reduce polyspermic fertilization rate.Studies have indicated that the polyspermic fertilization rate is significantly higher in cumulus cell-free group than in cumulus cell group.In short-term insemination,the remaining oocytes should retain cumulus cells to reduce polyspermic fertilization under the circumstance of successful fertilization.3.There is no significant difference in 2PN embryo chromosome abnormality between conventional IVF group and short-term insemination group.4.Short-term insemination may significantly decrease ICSI rate and partial ICSI rate.5.Complete fertilization failure rate significantly decreases in short-term insemination. Short-term insemination reduces unfavourable factors for embryo development,therefore increases high quality embryo rate.If short-term insemination is adopted in IVF,under the circumstance of successful fertilization,the remaining oocytes should retain cumulus cells as much as possible to reduce polyspermic fertilization,improve oocyteutilization rate and optimize IVF outcomes.

Bacteriospermia among smallholder artificial insemination boars in the Philippines and potential associated factors

Objective:To determine the prevalence of bacteriospermia,the bacterial load,and the potential factors associated with bacterial contamination in boar semen collected by local smallholder artificial insemination operators.Methods:Fifteen individual raw semen samples were collected from locally available artificial insemination boars owned by different smallholder boar operators within the 5th district of Leyte,Philippines and were subjected to standard bacteriological culture and identification,including a survey of potentially associated factors.Prevalence and bacterial count were determined accordingly,while boar characteristics and collection practices were clustered following agglomerative hierarchical clustering technique.Results:One hundred percent contamination with a bacterial count of(2.01±0.38)×10^(3) CFU/mL was observed.At least 73.33%of the samples were positive for Bacillus spp.,while other identified isolates included Enterobacter spp.,Staphylococcus spp.,E.coli,Pseudomonas spp.,Citrobacter spp.,and Klebsiella spp.Conclusions:Despite the high prevalence of bacteriospermia,the bacterial count is low.Nevertheless,on-farm practices on boar health and management,semen collection,and sanitation as well as the enhancement of basic protocols to control contamination should be conscientiously considered in smallholder artificial insemination operation.

Can the Prediction of Intrauterine Insemination Results by Used Aniline Blue Stain (ABS) and Sperm Chromatin Dispersion (SCD) Levels?

Introduction: This study aimed to perform routine seminal fluid analysis, sperm DNA fragmentation, and sperm function tests at the chromatin maturation level and evaluate pregnancy in the patients passing intrauterine insemination before starting Intrauterine Insemination (IUI) method. Materials and Methods: In this prospective study, 111 couples who underwent Intrauterine Insemination (IUI) in unexplained infertility patients were admitted to Al-Farah IVF and assisted reproductive center in Baghdad, Iraq between November 2020 and February 2021 were evaluated. Semen fluid analysis was performed based on (WHO 4th) guiding rules. In addition, Sperm Chromatin Dispersion (halo test) and sperm maturation were performed with Aniline Blue Stain (ABS). Results: Sperm Chromatin Dispersion (SCD) groups were compared in terms of pregnancy outcome;the positive pregnancy rate was found to be above in the normal SCD groups (p = 0.0005). In addition, Aniline Blue Stain (ABS) groups were compared in the terms of pregnancy outcome;the positive pregnancy rate was found to be higher in the normal ABS group (p = 0.017). Conclusion: Our study showed that the use of DNA fragmentation (SCD) and sperm maturation tests (ABS) together with routine semen analysis in intrauterine insemination cases will make a significant contribution to the prediction of Intrauterine Insemination (IUI) increased results. So, these results indicate a defect in the effect of DNA fragmentation on the outcome of intrauterine insemination.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

Short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function

BACKGROUND Hepatectomy is the first choice for treating liver cancer.However,inflammatory factors,released in response to pain stimulation,may suppress perioperative immune function and affect the prognosis of patients undergoing hepatectomies.AIM To determine the short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function.METHODS Clinical data from patients with liver cancer admitted to Suzhou Ninth People’s Hospital from January 2020 to December 2023 were retrospectively analyzed.Thirty-five patients underwent laparoscopic hepatectomy for liver cancer(liver cancer resection group)and 35 patients underwent medical image-guided microwave ablation(liver cancer ablation group).The short-term efficacy,complications,liver function,and immune function indices before and after treatment were compared between the two groups.RESULTS One month after treatment,19 patients experienced complete remission(CR),8 patients experienced partial remission(PR),6 patients experienced stable disease(SD),and 2 patients experienced disease progression(PD)in the liver cancer resection group.In the liver cancer ablation group,21 patients experienced CR,9 patients experienced PR,3 patients experienced SD,and 2 patients experienced PD.No significant differences in efficacy and complications were detected between the liver cancer ablation and liver cancer resection groups(P>0.05).After treatment,total bilirubin(41.24±7.35 vs 49.18±8.64μmol/L,P<0.001),alanine aminotransferase(30.85±6.23 vs 42.32±7.56 U/L,P<0.001),CD4+(43.95±5.72 vs 35.27±5.56,P<0.001),CD8+(20.38±3.91 vs 22.75±4.62,P<0.001),and CD4+/CD8+(2.16±0.39 vs 1.55±0.32,P<0.001)were significantly different between the liver cancer ablation and liver cancer resection groups.CONCLUSION The short-term efficacy and safety of microwave ablation and laparoscopic surgery for the treatment of liver cancer are similar,but liver function recovers quickly after microwave ablation,and microwave ablation may enhance immune function.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Short-term train arrival delay prediction:a data-driven approach

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.

Off-Pump Coronary Artery Bypass Grafting in Patients with Left Ventricular Dysfunction: Short-Term Results from a Single Center in Bangladesh

Background: Off-pump coronary artery bypass grafting (OPCAB) is considered a safer alternative to on-pump surgery, especially in patients with left ventricular dysfunction (LVD). Objectives: This study assessed short-term outcomes and functional improvements in LVD patients post-OPCAB. Methods: The study included 200 coronary artery disease patients who underwent isolated off-pump coronary artery bypass grafting (OPCAB) at the National Heart Foundation Hospital and Research Institute between January 2019 and June 2020. Patients were categorized into Group 1, with a left ventricular ejection fraction (LVEF) of 30% - 39%, and Group 2, with an LVEF of 40% or higher. Echocardiographic assessments of left ventricular dimensions and ejection fraction were performed preoperatively, at discharge, and one month postoperatively. Results: In Group 1, preoperative left ventricular internal dimensions during diastole (LVIDd) and systole (LVIDs) were 53.48 ± 4.40 mm and 44.23 ± 3.93 mm, respectively, with a left ventricular ejection fraction (LVEF) of 35.28% ± 2.26%. At discharge, these values improved to 51.58 ± 4.04 mm (LVIDd), 41.23 ± 5.30 mm (LVIDs), and 39.25% ± 3.75% (LVEF). One month postoperatively, further improvements were observed: 46.29 ± 3.76 mm (LVIDd), 37.45 ± 3.68 mm (LVIDs), and 43.22% ± 4.67% (LVEF). Group 2 showed similar positive outcomes, with preoperative values of 47.09 ± 5.06 mm (LVIDd), 35.11 ± 5.25 mm (LVIDs), and 50.13% ± 7.25% (LVEF), improving to 42.37 ± 4.18 mm (LVIDd), 31.05 ± 4.19 mm (LVIDs), and 55.33% ± 7.05% (LVEF) at one month postoperatively. Both groups demonstrated significant improvements in left ventricular function and NYHA class, with most patients moving from class III/IV to I/II. Complications were minimal, and no mortality was observed. Conclusion: OPCAB is safe and effective for patients with LVEF 30% - 39% and LVEF ≥ 40%, providing significant short-term functional improvements without increased risk.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Study on Key Technique of Artificial Insemination for Silkworm,Bombyx mori

[Objective] The aim of this study was to investigate the efficient technique of artificial insemination for silkworm. [Method] Sperms were extracted from bursa copulatrix of female moths mated for 30 min through extruding and centrifugal method, and then the semen was injected into other virgin moths with trypsinase. [ Result] A high-effective collection technology of spermatids from silkworm was established successfully, 50 μl semen could be collected by only one person in each hour. The survival rate of spermatids was over 80% in vito after collected from bursa copulatrix, while the obtained semen was quite pure and the average fertilization rate of silkworm was 76,5%. [ Conclusion] The establishment of high-effective semen extraction technique of silkworm provides the technical basis for studies on other related techniques for silkworm sperm.

Efficiency of Artificial Insemination in Giant Pandas at the Wolong Breeding Center

The goal of the giant panda ( Ailuropoda melanoleuca ) breeding program is to develop a self sustaining,genetically diverse population.Due to the common problems about sexual incompatibility and a limited number of captive born males that breed naturally,artificial insemination (AI) has become a critical genetic management tool.It is common practice,however,to combine natural mating and AI using semen from non breeding males.From 1998 to 2000 at the Wolong breeding facility,12 of 18 (66.7%) females produced 20 cubs following combined natural mating and AI.The objective of this study was to determine the efficiency of AI without natural breeding.In 1998 and 2000,seven females were anesthetized for transcervical AI on two consecutive days.Ejaculates from six males were collected by electroejaculation,diluted in an egg yolk diluent containing 0% or 4% glycerol and used either fresh or following cold storage at 4℃ (for 24 or 48 h) or cryopreservation using the pellet freezing method.Mean (±SEM) ejaculate traits in six male sperm donors were:ejaculate volume,3.3±0.5 ml;sperm concentration,1,429.8±235.4×10 6/ml;sperm motility,81.7±2.1%;progression (0～5,5=best),3.1±0.1;and normal sperm,79.3±9.2%.For AI (n = 14) in seven females,mean inseminate traits were:spermic volume inseminated,2.4±0.3 ml;sperm motility,73.5±2.9%;progression,2.5±0.1;and total motile sperm inseminated/AI,684.2±118.2×10 6.Four of seven (57.1%) females became pregnant and produced five cubs of which four survived.Mean gestation and litter size was 131.5±9.7 days and 1.3±0.3 cubs/litter,respectively.These results indicate that the efficiency of AI is sufficient for recovering valuable genes from non breeding individuals to enhance genetic diversity in the ex situ population of giant pandas.

Double versus single homologous intrauterine insemination for male factor infertility： a systematic review and meta-analysis

Male factor infertility affects 30%-50% of infertile couples worldwide, and there is an increasing interest in the optimal management of these patients. In studies comparing double and single intrauterine insemination （IUI）, a trend towards higher pregnancy rates in couples with male factor infertility was observed. Therefore, we set out to perform a meta-analysis to examine the superiority of double versus single IUI with the male partner＇s sperm in couples with male factor infertility. An odds ratio （OR） of 95% confidence intervals （CIs） was calculated for the pregnancy rate. Outcomes were analysed by using the ManteI-Haesel or DerSimonian-Laird model accordingto the heterogeneity of the results. Overall, five trials involving 1125 IUI cycles were included in the meta-analysis. There was a two-fold increase in pregnancies after a cycle with a double IUI compared with a cycle with a single IUI （OR. 2.0; 95% CI. 1.07-3.75; P〈O.03）. Nevertheless, this result was mainly attributed to the presence of a large trial that weighted as almost 50% in the overall analysis. Sensitivity analysis, excluding this large trial, revealed only a trend towards higher pregnancy rates among double IUI cycles （OR. 1.58; 95% CI. 0.59-4.21）, but without statistical significance （P=0.20）. Our systematic review highlights that the available evidence regarding the use of double IUI in couples with male factor infertility is fragmentary and weak. Although there may be a trend towards higher pregnancy rates when the number of IUIs per cycle is increased, further large and well-designed randomized trials are needed to provide solid evidence toide current clinical practice.

Successful pregnancy and birth after intrauterine insemination using caput epididymal sperm by percutaneous aspiration

<abstract>Aim: To manage male infertility with obstructive azoospermia by means of percutaneous epididymal sperm aspiration (PESA) and intrauterine insemination (IUI). Methods: Ninety azoospermic patients with congenital bilateral absence of the vas deferens (BAVD, n=58) or bilateral caudal epididymal obstruction (BCEO, n=32) requesting for fine needle aspiration (FNA), PESA and IUI were recruited. The obstruction was diagnosed by vasography and determination of the fructose, carnitine and alpha-glucosidase levels in the seminal fluid. Results: The mean sperm motility, density, abnormal sperm and total sperm count of the caput epdidymis were 16 %±22 %, (12±31) ×106/mL, 55 %±36 % and (16±14)×106, respectively. In the 90 couples, a total of 74 PESA procedures and 66 cycles of IUI were performed. Three pregnancies resulted, including one twin pregnancy giving birth to two healthy boys, one single pregnancy with a healthy girl and another single pregnancy aborted at week 6 of conception. The pregnancy rate per IUI cycle was 4.5 %. Conclusion: The birth of normal, healthy infants by IUI using PESA indicates that the caput epididymal sperm possess fertilization capacity. The PESA-IUI programme is a practical and economical procedure for the management of patients with obstructive azoospermia.

Artificial Insemination of Cherry Valley Ducks with Muscovy Ducks

[Objective] To explore artificial insemination technique for production of mule ducks. [Method] Female Cherry Valley ducks were artificially inseminated with semen collected from male Muscovy ducks by massage method and vagina-insemination method to investigate the effects of insemination dose, insemination interval, insemination time and diluents on fertilization rate. [ Result ] The average fertilization rate was only 39.58%, when the female Cherry Valley ducks naturally mated with the male Muscovy ducks. However, it was increased to 74.79% by artificial insemination. The fertilization rate was 75.24% after semen was diluted by PBS buffer, while it was 75.16% after semen was diluted by Lake＇s buffer; however, there was not significant difference between the fertilization rate of the diluted semen and that of fresh semen （74.10%）. After semen was respectively diluted by PBS buffer and Lake＇s buffer and then stored at 5 ℃ for 24 h, the fertilization rate was low, respectively 23.76% and 34133%. [ Conclusion] Artificial insemination technology can reduce insemination dose and increase fertilization rate in production of mule ducks.