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PurposeThe aim of this study was to evaluate the associations of thyroid autoimmunity (TAI) with the number of oocytes retrieved (NOR), fertilization rate (FR), and embryo quality (EQ) in euthyroid women with infertility and diminished ovarian reserve (DOR).MethodsThis retrospective cohort study involved 1,172 euthyroid women aged 20–40 years with infertility and DOR who underwent an oocyte retrieval cycle. TAI was diagnosed in the presence of serum thyroperoxidase antibody (TPOAb) concentrations higher than 34 IU/ml and/or serum thyroglobulin antibody (TgAb) concentrations exceeding 115.0 IU/ml. Among these women, 147 patients with TAI were classified as the TAI-positive group, while 1,025 patients without TAI were classified as the TAI-negative group. Using generalized linear models (GLMs) adjusted for confounding factors, we evaluated the associations of TAI and the serum TPOAb and TgAb concentrations and NOR, FR, and EQ in this study’s subjects. The TPOAb and TGAb values were subjected to log10 transformation to reduce skewness. Logistic regression models were used to estimate the effects of TPOAb and TgAb concentrations on the probabilities of achieving a high NOR (≥7) and high FR (>60%).ResultsFor the whole study population, women with TAI had a significantly lower NOR and poorer EQ than women without TAI (P < 0.001 for both). Interestingly, in the TSH ≤2.5 subgroup, the TAI-positive group also had a significantly lower NOR and poorer EQ than the TAI-negative group (P < 0.001 for both). Furthermore, negative associations were observed between log10(TPOAb) concentrations and NOR and the number of high-quality embryos and available embryos (P < 0.05 for all). The log10(TgAb) concentrations were inversely associated with NOR and the number of high-quality embryos (P < 0.05 for all). In the regression analysis, the log10(TPOAb) concentrations had lower probabilities of achieving a high NOR [adjusted odds ratio (aOR): 0.56; 95% confidence interval (95% CI) 0.37, 0.85; P = 0.007].ConclusionsTAI and higher TPOAb and TgAb concentrations were shown to be associated with reductions in the NOR and EQ in the study population. Our findings provide further evidence to support systematic screening and treatment for TAI in euthyroid women with infertility and DOR.

The isothermal tensile test of 6016-T6 aluminum alloy was carried out on Gleeble-3500 at the temperature range of 400 °C–550 °C and the strain rate range of 0.01–10 s ^−1 . The results show that the thermal deformation mechanism of 6016-T6 is dynamic recovery and dynamic recrystallization. In this paper, the phenomenological Arrhenius constitutive model and the data-driven WOA-LSTM constitutive model for predicting the hot tensile deformation behavior of 6016-T6 aluminum alloy were studied in contrast. The whale optimization algorithm was used to optimize the hyperparameters of LSTM neural network to improve the prediction accuracy of flow stress. The optimization results show that the optimal hidden layer node, maximum training period, initial learning rate and mini batch size of WOA-LSTM are 46, 260, 0.0248 and 16, respectively. In addition, the influence of the number of hidden layers on the results of the network was discussed. The appropriate hidden layer of the network was determined to be 2. The result show that the prediction accuracy of WOA-LSTM constitutive model is better than the Arrhenius constitutive model. The mean absolute error and correlation coefficient are 0.9348% and 0.99952, respectively. Among them, in this study, the Arrhenius constitutive model has low precision and only has high precision within a single temperature range.

ObjectiveTo determine whether preimplantation genetic testing for aneuploidy (PGT-A) can improve the pregnancy outcomes of patients aged under 38 years who have a history of recurrent implantation failure(RIF).DesignRetrospective cohort study.MethodsWe retrospectively studied the pregnancy outcomes of RIF patients aged under 38 years from January 2017 to December 2021.178 patients were divided into two groups according to whether they underwent PGT-A: the PGT-A group(n=59)and the control group(n=119).In the PGT-A group, we compared the euploidy rate of the different quality and developmental rate blastocysts. In both groups,the patients were the first frozen-thaw single blastocysts transfer after the diagnosis of RIF. Among the pregnancy outcomes, the clinical pregnancy rate was assessed as the primary outcome. The spontaneous abortion rate and ongoing pregnancy rate were the secondry outcomes. The generalized estimation equation was used to adjust for the blastocysts derived from the same patients. Multivariate logistic analysis models were used to compare the pregnancy outcomes between the two groups.ResultsIn the PGT-A group, 293 blastocysts obtained from59 patients underwent PGT-A. The proportions of euploidy, aneuploidy and mosaic blastocysts were 56.31%, 25.60% and 18.09%, respectively. A comparison of the euploidy rates of different quality blastocysts showed that the rate of good-quality blastocysts was significantly higher than that of poor-quality blastocysts (67.66% vs 46.88%; odds ratio [OR], 2.203; 95%confidence interval[CI], 0.943–3.612; P=0.002). However, no significant difference was observed in the different developmental rates blastocysts. Compared with Day 5 blastocysts, the euploidy rates of Day 6 and Day 7 blastocysts were not significantly different(61.54%vs51.91%; OR,0.945; 95%CI, 0.445–2.010; P=0.884; and 61.54%vs47.37%; OR, 1.106; 95%CI, 0.774–1.578; P=0.581, respectively).As for the pregnancy outcomes, the clinical pregnancy rate was significantly increase after the use of PGT-A compared with the control group(71.19%vs56.30%; OR, 0.538; 95%CI, 0.262–1.104; P=0.039). However, the spontaneous abortion rates and ongoing pregnancy rates were not significantly different between the control and PGT-A groups (21.43% vs 19.40%; aOR,0.727; 95%CI,0.271–1.945; P=0.525; and55.93% vs 45.38%; aOR, 0.649; 95%CI, 0.329–1.283; P = 0.214,respectively).ConclusionPGT-A improved the clinical pregnancy rate after blastocyst transfer in RIF patients aged under 38 years.

ObjectiveTo investigate the clinical outcomes of Day 7 (D7) frozen-thawed embryo transfer (FET) and to provide a reference value for clinical work.MethodsThis was a retrospective cohort study. Patients undergoing FET cycles in the Reproductive Medicine Center of the Third Affiliated Hospital of Zhengzhou University between December 2015 and January 2021 were included. According to the developmental stage of the embryos at transfer, the embryos were divided into three groups: Day (D) 5, D6 and D7 blastocysts. Group D7 was compared with Groups D5 and D6. Simultaneously, the preimplantation genetic testing (PGT) and non-PGT cycles in Group D7 were analyzed and compared. The main outcomes were the clinical pregnancy, live birth and miscarriage rates. The secondary outcomes were the implantation and euploidy rates.ResultsIn total, 5945, 4094 and 137 FET cycles were included in the D5, D6 and D7 groups, respectively. The clinical pregnancy rate was significantly lower in Group D7 than in Groups D5 (13.9% vs 62.9%, P

Abstract Background Ovarian cancer (OC) progression is an unmet medical challenge. Since omental metastases were palpated harder than their primary counterparts during cytoreductive surgery of patients with epithelial ovarian cancer (EOC), we were inspired to investigate OC progression from the perspective of biomechanics. Methods Atomic Force Microscope (AFM) was used to measure the Young’s modulus of tissues. The collagen-coated polyacrylamide hydrogel (PA gel) system was prepared to mimic the soft and stiff substrates in vitro. The effect of TAGLN was evaluated both in vitro and in vivo using transwell assay, immunofluorescence, western blot analysis and immunohistochemistry. Results We quantitatively confirmed that omental metastases were stiffer and more abundant in desmoplasia compared with paired primary tumors, and further demonstrated that matrix stiffness could notably regulate OC progression. Remarkably, TAGLN, encoding an actin cross-linking/gelling protein, was identified as a potent mechanosensitive gene that could form a regulation loop with Src activation reacting to environmental stiffness, thus mediating stiffness-regulated OC progression through regulating RhoA/ROCK pathway. Conclusions These data demonstrate that targeting extra-cellular matrix (ECM) stiffness could probably hamper OC progression, and of note, targeting TAGLN might provide promising clinical therapeutic value for OC therapy.

Abstract Background Standard morphologic evaluation has been the most widely adopted approach to embryo selection, and remains the most common strategy.The objective of the study to determine the association between the morphologic grading and implantation rate of euploid blastocysts in single frozen-thawed embryo transfer (SET) cycles. Methods A total of 271 patients aged 20–40 years undergoing euploid SET from January 2017 to December 2019 were included in retrospective cohort study.The cycles were divided into three groups based on their morphologic grading before cryopreservation: good-quality (n = 58), average-quality (n = 88) and poor-quality blastocysts (n = 125). The pregnancy outcome of the three morphologic groups were analyzed and a logistic regression of implantation rate was conducted. Results Good-quality blastocysts yielded statistically significantly higher implantation rates than poor-quality (79.31% vs. 48%; P

ObjectiveThe aim of this study was to investigate whether blastocyst morphology and developmental rate are associated with euploidy and live birth rates (LBRs) in single euploid frozen–thawed embryo transfer (FET) cycles.DesignRetrospective cohort study.MethodsThis study included 431 preimplantation genetic testing for aneuploidy (PGT-A) cycles followed by 393 FET cycles performed at our center from June 2017 to March 2021. All cycles were analyzed for euploidy based on blastocyst morphology (good, average and poor), developmental stage (day 5 and 6) and maternal age (< 35 and ≥ 35 years old). Multivariate logistic analysis models were used to identify the independent effects of conventional blastocyst morphology, developmental rate and morphological parameters (degree of blastocoele expansion, and grade of inner cell mass and trophectoderm (TE)) on LBRs.ResultsIn the group of women aged < 35 years, compared with poor-quality blastocysts, good-quality blastocysts (62.90% vs. 32.46%; odds ratio (OR) 3.163, 95% confidence interval (CI) 2.247–4.451; P < 0.001) and average-quality blastocysts (46.70% vs. 32.46%; OR 1.665, 95% CI 1.287–2.154; P < 0.001) had significantly higher euploidy rates. Additionally, day 5 blastocysts were associated with higher euploidy rates than day 6 blastocysts (49.28% vs. 35.02%; OR 1.506, 95% CI 1.191–1.903; P= 0.001). In the group of women aged ≥ 35 years, euploidy rates were also associated with blastocyst morphology, with 41.86%, 45.65% and 24.39% of good, average and poor-quality embryos, respectively, exhibiting euploidy. However, no relationship was seen between euploidy and blastocyst developmental rate. Multiple logistic regression analysis show that overall blastocyst morphology of euploid embryos was not associated with LBR, only embryos with A-grade TE had significantly higher LBRs than those with C-grade TE (62.71% vs. 45.40%; OR 2.189, 95% CI 1.166–4.109; P=0.015). Similarly, LBRs were significantly higher when day 5 blastocysts were transferred than when day 6 blastocysts were transferred (57.75% vs. 41.67%; OR 2.132, 95% CI 1.370–3.318; P = 0.001).ConclusionPoor-quality embryos have reduced rates of euploidy. However, blastocyst developmental rate only significantly associates with euploidy rates in women aged younger than 35. Furthermore, only TE grade and blastocyst developmental rate are significantly associated with LBRs following FET cycles.

Abstract Purpose To investigate the associations between blastocyst development and the sex ratio (male:female) among singleton live births resulting from single-blastocyst frozen embryo transfer (FET) cycles. Methods Patients with singleton live births following the first autologous single FET of non- preimplantation genetic testing (PGT) blastocysts in a single reproductive medicine department between January 2015 and February 2019 were included in this retrospective study. The primary outcome measure was the singleton sex ratio. Multivariable logistic regression models were used to estimate the associations between blastocyst quality and singleton sex ratio after adjustment for some potential confounders. Results There were 638 high-quality and 572 poor-quality single blastocyst FETs, and the blastocysts were conceived via 855 IVF and 355 ICSI treatments. A total of 1210 singleton live births were assessed. High-quality single blastocyst FET resulted in a significantly higher sex ratio than did poor-quality single blastocyst FET (60% vs. 49.7%, P

The superparent one-dependence estimators (SPODEs) is a popular family of semi-naive Bayesian network classifiers, and the averaged one-dependence estimators (AODE) provides efficient single pass learning with competitive classification accuracy. All the SPODEs in AODE are treated equally and have the same weight. Researchers have proposed to apply information-theoretic metrics, such as mutual information or conditional log likelihood, for assigning discriminative weights. However, while dealing with different instances the independence assumptions for different SPODEs may hold to different extents. The quest for highly scalable learning algorithms is urgent to approximate the ground-truth attribute dependencies that are implicit in training data. In this study we set each instance as the target and investigate extensions to AODE by measuring the independence assumption of SPODEs and assigning weights. The proposed approach, called independence weighted AODE (IWAODE), is validated on 40 benchmark datasets from the UCI machine learning repository. Experimental results reveal that, the resulting weighted SPODEs delivers computationally efficient low-bias learning, proving to be a competitive alternative to state-of-the-art single and ensemble Bayesian network classifiers (such as tree-augmented naive Bayes, k-dependence Bayesian classifier, WAODE-MI and etc).

Summary: Background: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. Methods: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. Findings: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886–0·936) in the internal dataset, 0·870 (95% CI 0·822–0·918) in external validation dataset 1, and 0·831 (95% CI 0·793–0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0–90·2] vs 78·3% [72·1–84·5], p

ObjectiveTo investigate whether patients with a history of recurrent implantation failure (RIF) are associated with adverse perinatal outcomes in singleton live births following frozen-thawed embryo transfer (FET) cycles.DesignRetrospective cohort study.MethodsThis study analyzed the obstetric and neonatal outcomes of patients with and without a history of RIF who underwent FET cycles in a single reproductive center between January 2017 and October 2020. A total of 1,100 women with singleton live births beyond 28 weeks of gestation were included. The primary outcome measures were perinatal outcomes, especially gestational age, birthweight, preterm birth (PTB), large for gestational age (LGA), small for gestational age (SGA), congenital malformation rates, and premature rupture of the membranes (PROM). Multiple logistic regression was used to establish relationships between RIF and adverse perinatal outcomes after adjusting for relevant baseline demographics and cycle characteristics.Result(s)The RIF group showed a preferred transfer of two embryos and cleavage embryos compared with the control group (P

Due to the excellent classification performance and expressivity, the study of Bayesian network classifiers (BNCs) has attracted great attention ever since the success of Naive Bayes (NB). Information theory has established mathematical basis for the rapid development of BNC. In this paper we propose the definition of entropy function HB(D), which corresponds to the optimal number of bits encoded in the network structure B and can roughly measure the amount of information implicated in training data D. Each factor in HB(D) explicitly represents statements about causal relationships. An efficient heuristic search strategy is introduced to minimize HB(D) and explore the optimal topology of BNC. Our extensive experimental evaluation on 40 datasets reveals that this out-of-core algorithm achieves competitive classification performance compared to state-of-the-art learners such as tree augmented Naive Bayes, k-dependence Bayesian classifier, support vector machine, logistic regression and neural network.

Next-generation sequencing (NGS) is a high throughput sequencing technology, which has revolutionized both basic and clinical research of the human genetic disorders. This technology is also called massively parallel sequencing (MPS) due to its ability to generate a huge amount of output data in a cost- and time-effective manner. NGS is widely utilized for different sequencing applications such as targeted sequencing (a group of candidate genes), exome sequencing (all coding regions), and whole genome sequencing (the entire human genome). With NGS, a variety of genomic aberrations can be screened simultaneously such as common and rare variants, structural variations (amplifications and deletion), copy-number variation, and fusion transcripts. NGS technologies combined with advanced bioinformatic analysis have tremendously expanded our knowledge. On the one hand, the basic research area involves direct use of NGS to identify novel variations and determine human disease mechanisms. On the other hand, clinical research is being advanced by highthroughput genetic tests with high resolution and clinically relevant genetic information for molecular diagnoses of human disorders. In this communication, we introduce NGS technologies and review a few key areas where NGS has made a significant impact, with an emphasis on the application of NGS to the identification of the molecular bases of human genetic diseases.

Bayesian network classifiers (BNCs) have demonstrated competitive classification performance in a variety of real-world applications. A highly scalable BNC with high expressivity is extremely desirable. This paper proposes Redundant Dependence Elimination (RDE) for improving the classification performance and expressivity of k-dependence Bayesian classifier (KDB). To demonstrate the unique characteristics of each case, RDE identifies redundant conditional dependencies and then substitute/remove them. The learned personalized k-dependence Bayesian Classifier (PKDB) can achieve high-confidence conditional probabilities, and graphically interpret the dependency relationships between attributes. Two thyroid cancer datasets and four other cancer datasets from the UCI machine learning repository are selected for our experimental study. The experimental results prove the effectiveness of the proposed algorithm in terms of zero-one loss, bias, variance and AUC.

To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary to BNC T learned from training data T . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood. We take the k-dependence Bayesian classifier as an example to investigate the effect of UTL on BNC P and BNC T . Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance.

Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditional mutual information. However, for different class labels, the conditional dependence relationships may be different rather than invariant when attributes take different values, which may result in classification bias. To address this issue, we propose a novel framework, called discriminatory target learning, which can be regarded as a tradeoff between probabilistic model learned from unlabeled instance at the uncertain end and that learned from labeled training data at the certain end. The final model can discriminately represent the dependence relationships hidden in unlabeled instance with respect to different possible class labels. Taking k-dependence Bayesian classifier as an example, experimental comparison on 42 publicly available datasets indicated that the final model achieved competitive classification performance compared to state-of-the-art learners such as Random forest and averaged one-dependence estimators.