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Purpose To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation. Methods A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated. Results The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35‐37, 38‐39, 40‐41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE (P = 0.01). Conclusions AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE. We made the AI classifiers of the deep learning with the convolutional neural networks from the image of the blastocyst categorized by age to predict the probability of achieving live birth. The AI classifiers categorized by age can predict live birth from the image of the blastocyst.

Purpose To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. Methods A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos. Results The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005). Conclusions Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome. Predicting live birth without aneuploidy by machine learning. Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.

Purpose To identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age. Methods Retrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated. Results The values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35‐37, 38‐39, 40‐41, and ≥42 years old, respectively. Conclusions Though there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best. The combination method involving artificial intelligence classifiers in images of blastocysts along with a conventional embryo evaluation in order to predict the probability of achieving a live birth in patients classified by age seemed to be the best, though there were mostly no significant differences in terms of the AUC and the sensitivity plus specificity in all age categories.

Purpose This study aimed to analyze whether tripronuclear (3PN) zygotes, with two normal‐sized PNs and an additional smaller PN (2.1PN), can be used for embryo transfer. Method(s) A retrospective embryo cohort study was conducted on 695 patients who underwent intracytoplasmic sperm injection treatment. Blastocyst formation rates were compared between 2.1PN and 2PN zygotes and PGT‐A analysis was performed on 15 blastocysts derived from 2.1PN zygotes. Result(s) Blastocyst formation rates were comparable between 2.1PN (43.8%) and 2PN zygotes (54.8%; p = 0.212). The rates of blastocysts with good morphology derived from 2.1 PN and 2PN zygotes were 18.8% and 25.5%, respectively. No significant differences were detected (p = 0.383). All of the analyzed blastocysts were diploid; however, 13 of these were found to be aneuploid, with a further two being mosaic. Conclusion Our results suggest that 2.1PN embryos can reach blastocyst stage. These blastocysts were diploid, however, predominantly aneuploid, and therefore could not be used for embryo transfer. Tri#x02010;pronuclear embryos that showing two normal‐sized pronuclei and additional smaller pronuclei are diploid but mostly aneuploidies and, therefore, may not be useful for embryo transfer.

The retrospective noninterventional study investigated the kinetic energy of video images of 18 fertilized eggs (7 were normal and 11 were abnormal) recorded by a time-lapse device leading up to the beginning of the first cleavage. The norm values of cytoplasmic particles were measured by the optical flow method. Three phase profiles for normal cases were found regarding the kinetic energy: 2.199 × 10−24 ± 2.076 × 10−24, 2.369 × 10−24 ± 1.255 × 10−24, and 1.078 × 10−24 ± 4.720 × 10−25 (J) for phases 1, 2, and 3, respectively. In phase 2, the energies were 2.369 × 10−24 ± 1.255 × 10−24 and 4.694 × 10−24 ± 2.996 × 10−24 (J) (mean ± SD, p = 0.0372), and the time required was 8.114 ± 2.937 and 6.018 ± 5.685 (H) (p = 0.0413) for the normal and abnormal cases, respectively. The kinetic energy change was considered a condition for applying the free energy principle, which states that for any self-organized system to be in equilibrium in its environment, it must minimize its informational free energy. The kinetic energy, while interpreting it in terms of the free energy principle suggesting clinical usefulness, would further our understanding of the phenomenon of fertilized egg development with respect to the birth of human life.

Purpose This study aimed to analyze whether a regimen of aromatase inhibitor (AI) could reduce the occurrence of smooth endoplasmic reticulum clusters (sERCs) in oocytes. Method(s) The AI and the clomiphene citrate (CC) regimens were compared, regarding the sERC (+) rates and the serum estradiol and progesterone levels on the date of hCG administration, and the duration of AI, CC, and hMG administration. Result(s) The occurrence of sERCs in oocytes from patients treated with AI was significantly higher than that in oocytes from those treated with CC. Both the serum estradiol and progesterone levels were found to be significantly higher in sERC (+) than in sERC (‐) cycles. With regard to the CC cycles, no significant differences were detected. The duration of AI and hMG administration was longer for sERC (+) than for sERC (‐) cycles. Conclusion As AI did not reduce the occurrence of sERCs, the elevation of estradiol may not be the cause of sERC occurrence but a consequence. Considering the higher levels of progesterone and longer duration of hMG in sERC (+) cycles, the negative effects of premature luteinization, which frequently occur with the AI protocol, should be investigated further.

Purpose This study aimed to analyze whether the presence of refractile bodies (RFs) negatively affects fertilization, embryo development, and/or implantation rates following intracytoplasmic sperm injection (ICSI). Methods This retrospective embryo cohort study involved a total of 272 patients undergoing ICSI treatment of blastocyst cryopreservation. Results In the study, no significant differences were found regarding 2PN formation rates between RF(+) (76.5%) and RF(−) oocytes (77.2%). However, the blastocyst formation rate on Day 5 in RF(+) oocytes was 45.8%, which was significantly lower than that of 52.2% in RF(−) oocytes (aOR 0.74, 95% CI 0.59‐0.93, P = .011). Implantation rates were also significantly lower in RF(+) oocytes (24.2%) as compared to RF(−) oocytes (42.2%) (aOR 0.46, 95% CI 0.26‐0.78, P = .005). Furthermore, the implantation rate of RF(+) oocytes (28.6%), when high‐quality blastocysts were transferred, was significantly lower than that of RF(−) oocytes (46.1%) (aOR 0.50, 95% CI 0.25‐0.96, P = .043). Conclusion Our results suggest that oocytes with the presence of RFs have a lower potential for blastocyst development. Even when they develop into high‐quality blastocysts, the chances of implantation are reduced. Oocytes with the presence of RFs have a lower potential to develop into blastocysts, and even when they develop into high‐quality blastocysts, the chances of implantation are reduced.

To investigate the usefulness of an original dual artificial intelligence (AI) system, in which the first AI system eliminates the background of sliced tomographic blastocyst images, then the second AI system predicts implantation success using three-dimensional (3D) reconstructed images of the sequential images and conventional embryo evaluation parameters (CEE) such as maternal age. Patients (from June 2022 to July 2023) could opt out and there was additional information on the Web site of the clinic. Implantation and non-implantation cases numbered 458 and 519, respectively. A total of 10 747 tomographic images of the blastocyst in a time-lapse incubator system with the CEE were obtained. The statistic values by the dual AI system were 0.774 ± 0.033 (mean ± standard error) for area under the characteristic curve, 0.727 for sensitivity, 0.719 for specificity, 0.727 for predictive value of positive test, 0.719 predictive value of negative test, and 0.723 for accuracy, respectively. The usefulness of the dual AI system in predicting implantation of blastocyst in handling 3D data with conventional embryo evaluation information was demonstrated. This system may be a feasible option in clinical practice.

Gait variables derived from trunk accelerometry may predict the risk of falls; however, their associations with falls are not fully understood. The purpose of the study was to determine which gait variables derived from upper and lower trunk accelerometry are associated with the incidence of falls, and to compare the discriminative ability of gait variables and physical performance. This study was a 1-year prospective study. Older people (n = 73) walked normally while wearing accelerometers attached to the upper and lower trunk. Participants were classified as fallers (n = 16) or non-fallers (n = 57) based on the incidence of falls over 1 year. The harmonic ratio (HR) of the upper and lower trunk was measured. Physical performance was measured in five chair stands and in the timed up and go test. The HR of the upper and lower trunk were consistently lower in fallers than non-fallers (P < 0.05). Upper trunk HR, was independently associated with the incidence of falls (P < 0.05) after adjusting for confounding factors including physical performances. Consequently, upper trunk HR showed high discrimination for the risk of falls (AUC = 0.81). HR derived from upper trunk accelerometry may predict the risk of falls, independently of physical performance. The discriminative ability of HR for the risk of falls may have some validity, and further studies are needed to confirm the clinical relevance of trunk HR.

Photoreceptors are conserved in green algae to land plants and regulate various developmental stages. In the ocean, blue light penetrates deeper than red light, and blue-light sensing is key to adapting to marine environments. Here, a search for blue-light photoreceptors in the marine metagenome uncover a chimeric gene composed of a phytochrome and a cryptochrome ( Dualchrome1 , DUC1 ) in a prasinophyte, Pycnococcus provasolii . DUC1 detects light within the orange/far-red and blue spectra, and acts as a dual photoreceptor. Analyses of its genome reveal the possible mechanisms of light adaptation. Genes for the light-harvesting complex (LHC) are duplicated and transcriptionally regulated under monochromatic orange/blue light, suggesting P. provasolii has acquired environmental adaptability to a wide range of light spectra and intensities. Blue light penetrates deeper than red light in ocean, thus blue light sensing is critical for adaptation to marine environments. Here, the authors report the genome of Pyconococcus provasolii and identify a chimeric dual orange/far-red and blue light receptor composed of a phytochrome and a cryptochrome by analyzing the marine metagenomes.