Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss.

RESEARCH QUESTION: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? DESIGN: In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). RESULTS: Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. CONCLUSIONS: This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.

The Internet of Things in assisted reproduction.

The Internet of Things (IoT) is a network connecting physical objects with sensors, software and internet connectivity for data exchange. Integrating the IoT with medical devices shows promise in healthcare, particularly in IVF laboratories. By leveraging telecommunications, cybersecurity, data management and intelligent systems, the IoT can enable a data-driven laboratory with automation, improved conditions, personalized treatment and efficient workflows. The integration of 5G technology ensures fast and reliable connectivity for real-time data transmission, while blockchain technology secures patient data. Fog computing reduces latency and enables real-time analytics. Microelectromechanical systems enable wearable IoT and miniaturized monitoring devices for tracking IVF processes. However, challenges such as security risks and network issues must be addressed through cybersecurity measures and networking advancements. Clinical embryologists should maintain their expertise and knowledge for safety and oversight, even with IoT in the IVF laboratory.

Locomotion Outcome Improvement in Mice with Glioblastoma Multiforme after Treatment with Anastrozole.

Glioblastoma Multiforme (GBM) is a tumor that infiltrates several brain structures. GBM is associated with abnormal motor activities resulting in impaired mobility, producing a loss of functional motor independence. We used a GBM xenograft implanted in the striatum to analyze the changes in Y (vertical) and X (horizontal) axis displacement of the metatarsus, ankle, and knee. We analyzed the steps dissimilarity factor between control and GBM mice with and without anastrozole. The body weight of the untreated animals decreased compared to treated mice. Anastrozole reduced the malignant cells and decreased GPR30 and ERα receptor expression. In addition, we observed a partial recovery in metatarsus and knee joint displacement (dissimilarity factor). The vertical axis displacement of the GBM+anastrozole group showed a difference in the right metatarsus, right knee, and left ankle compared to the GBM group. In the horizontal axis displacement of the right metatarsus, ankle, and knee, the GBM+anastrozole group exhibited a difference at the last third of the step cycle compared to the GBM group. Thus, anastrozole partially modified joint displacement. The dissimilarity factor and the vertical and horizontal displacements study will be of interest in GBM patients with locomotion alterations. Hindlimb displacement and gait locomotion analysis could be a valuable methodological tool in experimental and clinical studies to help diagnose locomotive deficits related to GBM.

Automated identification of blastocyst regions at different development stages.

The selection of the best single blastocyst for transfer is typically based on the assessment of the morphological characteristics of the zona pellucida (ZP), trophectoderm (TE), blastocoel (BC), and inner cell-mass (ICM), using subjective and observer-dependent grading protocols. We propose the first automatic method for segmenting all morphological structures during the different developmental stages of the blastocyst (i.e., expansion, hatching, and hatched). Our database contains 592 original raw images that were augmented to 2132 for training and 55 for validation. The mean Dice similarity coefficient (DSC) was 0.87 for all pixels, and for the BC, BG (background), ICM, TE, and ZP was 0.85, 0.96, 0.54, 0.63, and 0.71, respectively. Additionally, we tested our method against a public repository of 249 images resulting in accuracies of 0.96 and 0.93 and DSC of 0.67 and 0.67 for ICM and TE, respectively. A sensitivity analysis demonstrated that our method is robust, especially for the BC, BG, TE, and ZP. It is concluded that our approach can automatically segment blastocysts from different laboratory settings and developmental phases of the blastocysts, all within a single pipeline. This approach could increase the knowledge base for embryo selection.

Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation.

RESEARCH QUESTION: Is it possible to explore an association between individual sperm kinematics evaluated in real time and spermatozoa selected by an embryologist for intracytoplasmic sperm injection (ICSI), with subsequent normal fertilization and blastocyst formation using a novel artificial vision-based software (SiD V1.0; IVF 2.0, UK)? DESIGN: ICSI procedures were randomly video recorded and subjected to analysis using SiD V1.0, proprietary software developed by our group. In total, 383 individual spermatozoa were retrospectively analysed from a dataset of 78 ICSI-assisted reproductive technology cycles. SiD software computes the progressive motility parameters, straight-line velocity (VSL) and linearity of the curvilinear path (LIN), of each sperm trajectory, along with a quantitative value, head movement pattern (HMP), which is an indicator of the characteristics of the sperm head movement patterns. The mean VSL, LIN and HMP measurements for each set of spermatozoa were compared based on different outcome measures. RESULTS: Statistically significant differences were found in VSL, LIN and HMP among those spermatozoa selected for injection (P < 0.001). Additionally, LIN and HMP were found to be significantly different between successful and unsuccessful fertilization (P = 0.038 and P = 0.029, respectively). Additionally, significantly higher SiD scores were found for those spermatozoa that achieved both successful fertilization (P = 0.004) and blastocyst formation (P = 0.013). CONCLUSION: The possibility of carrying out real-time analyses of individual spermatozoa using an automatic tool such as SiD creates the opportunity to assist the embryologist in selecting the better spermatozoon for injection in an ICSI procedure.

A Method for Evaluating the Risk of Exposure to COVID-19 by Using Location Data / Un Método para la Evaluación del Riesgo de Exposición al Virus de COVID-19 Usando los Datos de Locación

Abstract One of the main reasons for the widespread dissemination of COVID-19 is that many infected people are asymptomatic. Consequently, they likely spread the virus to other people as they continue their everyday life. This emphasizes the importance for targeting high-risk groups for the diagnosis of COVID-19 (with real-time PCR techniques). However, the availability of the necessary technology and resources may be limited in certain towns, cities or countries. Thus, the challenge is to determine a criterion in order to prioritize the suspected cases most in need of testing. The aim of the present study was to develop a method for evaluating the risk of exposure to COVID-19 infection based on geolocation data. The risk is expressed as a score that will be instrumental in optimally applying the COVID-19 test to suspected cases representing the highest probability of exposure. It can be easily and quickly implemented with easily accessible open source tools. A simulation was herein conducted with data from four people, assigning infection to one of them. The results show the feasibility of assessing the risk of exposure with the new methodology. Additionally, the data obtained might provide insights into the sometimes complicated patterns of virus propagation.

Resumen Una de las principales razones del esparcimiento del COVID-19 es que muchas de las personas infectadas son asintomáticas. Así entonces, al continuar con su vida diaria estas personas contagiadas son susceptibles a contagiar el virus a otras personas sin siquiera imaginarlo. Actualmente el diagnostico de COVID-19 se lleva a cabo usando técnicas de PCR en tiempo real. Sin embargo, la disponibilidad de dichas pruebas puede ser limitada en algunos países o ciudades. En este sentido determinar un criterio que permita definir a cuáles casos sospechosos deben de ser aplicada la prueba resulta un reto importante. En este artículo se presenta un método que permite evaluar el riesgo de exposición de una persona al COVID-19 que está basado en el uso de los datos locación. El método propuesto puede ser rápida y fácilmente implementada utilizando herramientas de código abierto existentes actualmente. El método propuesto fue probado utilizando datos de cuatro personas simulando a uno de ellos como portador del virus. Los resultados muestran la factibilidad del método propuesto para evaluar el riesgo de exposición. Además, los datos que se obtienen pueden ser potencialmente utilizados para un mejor entendimiento de los patrones de dispersión del virus.

Kinematic Changes in a Mouse Model of Penetrating Hippocampal Injury and Their Recovery After Intranasal Administration of Endometrial Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Locomotion speed changes appear following hippocampal injury. We used a hippocampal penetrating brain injury mouse model to analyze other kinematic changes. We found a significant decrease in locomotion speed in both open-field and tunnel walk tests. We described a new quantitative method that allows us to analyze and compare the displacement curves between mice steps. In the tunnel walk, we marked mice with indelible ink on the knee, ankle, and metatarsus of the left and right hindlimbs to evaluate both in every step. Animals with hippocampal damage exhibit slower locomotion speed in both hindlimbs. In contrast, in the cortical injured group, we observed significant speed decrease only in the right hindlimb. We found changes in the displacement patterns after hippocampal injury. Mesenchymal stem cell-derived extracellular vesicles had been used for the treatment of several diseases in animal models. Here, we evaluated the effects of intranasal administration of endometrial mesenchymal stem cell-derived extracellular vesicles on the outcome after the hippocampal injury. We report the presence of vascular endothelial growth factor, granulocyte-macrophage colony-stimulating factor, and interleukin 6 in these vesicles. We observed locomotion speed and displacement pattern preservation in mice after vesicle treatment. These mice had lower pyknotic cells percentage and a smaller damaged area in comparison with the nontreated group, probably due to angiogenesis, wound repair, and inflammation decrease. Our results build up on the evidence of the hippocampal role in walk control and suggest that the extracellular vesicles could confer neuroprotection to the damaged hippocampus.

Evaluating predictive models in reproductive medicine.

Predictive modeling has become a distinct subdiscipline of reproductive medicine, and researchers and clinicians are just learning the skills and expertise to evaluate artificial intelligence (AI) studies. Diagnostic tests and model predictions are subject to evaluation. Their use offers potential for both harm and benefit in terms of diagnosis, treatment, and prognosis. The performance of AI models and their potential clinical utility hinge on the quality and size of the databases used, the types and distribution of data, and the particular AI method applied. Additionally, when images are involved, the method of capturing, preprocessing, and treatment and accurate labeling of images becomes an important component of AI modeling. Inconsistent image treatment or inaccurate labeling of images can lead to an inconsistent database, resulting in poor AI accuracy. We discuss the critical appraisal of AI models in reproductive medicine and convey the importance of transparency and standardization in reporting AI models so that the risk of bias and the potential clinical utility of AI can be assessed.

Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation.

RESEARCH QUESTION: Can a deep machine learning artificial intelligence algorithm predict ploidy and implantation in a known data set of static blastocyst images, and how does its performance compare against chance and experienced embryologists? DESIGN: A database of blastocyst images with known outcome was applied with an algorithm dubbed ERICA (Embryo Ranking Intelligent Classification Algorithm). It was evaluated against its ability to predict euploidy, compare ploidy prediction against randomly assigned prognosis labels and against senior embryologists, and if it could rank an euploid embryo highly. RESULTS: A total of 1231 embryo images were classed as good prognosis if euploid and implanted or poor prognosis if aneuploid and failed to implant. An accuracy of 0.70 was obtained with ERICA, with positive predictive value of 0.79 for predicting euploidy. ERICA had greater normalized discontinued cumulative gain (ranking metric) than random selection (P = 0.0007), and both embryologists (P = 0.0014 and 0.0242, respectively). ERICA ranked an euploid blastocyst first in 78.9% and at least one euploid embryo within the top two blastocysts in 94.7% of cases, better than random classification and the two senior embryologists. Average embryo ranking time for four blastocysts was under 25 s. CONCLUSION: Artificial intelligence lends itself well to image pattern recognition. We have trained ERICA to rank embryos based on ploidy and implantation potential using single static embryo image. This tool represents a potentially significant advantage to assist embryologists to choose the best embryo, saving time spent annotating and does not require time lapse or invasive biopsy. Future work should be directed to evaluate reproducibility in different data sets.

Fictive Scratching Patterns in Brain Cortex-Ablated, Midcollicular Decerebrate, and Spinal Cats.

Background: The spinal cord's central pattern generators (CPGs) have been explained by the symmetrical half-center hypothesis, the bursts generator, computational models, and more recently by connectome circuits. Asymmetrical models, at odds with the half-center paradigm, are composed of extensor and flexor CPG modules. Other models include not only flexor and extensor motoneurons but also motoneuron pools controlling biarticular muscles. It is unknown whether a preferred model can explain some particularities that fictive scratching (FS) in the cat presents. The first aim of this study was to investigate FS patterns considering the aiming and the rhythmic periods, and second, to examine the effects of serotonin (5HT) on and segmental inputs to FS. Methods: The experiments were carried out first in brain cortex-ablated cats (BCAC), then spinalized (SC), and for the midcollicular (MCC) preparation. Subjects were immobilized and the peripheral nerves were used to elicit the Monosynaptic reflex (MR), to modify the scratching patterns and for electroneurogram recordings. Results: In BCAC, FS was produced by pinna stimulation and, in some cases, by serotonin. The scratching aiming phase (AP) initiates with the activation of either flexor or extensor motoneurons. Serotonin application during the AP produced simultaneous extensor and flexor bursts. Furthermore, WAY 100635 (5HT1A antagonist) produced a brief burst in the tibialis anterior (TA) nerve, followed by a reduction in its electroneurogram (ENG), while the soleus ENG remained silent. In SC, rhythmic phase (RP) activity was recorded in the soleus motoneurons. Serotonin or WAY produced FS bouts. The electrical stimulation of Ia afferent fibers produced heteronymous MRes waxing and waning during the scratch cycle. In MCC, FS began with flexor activity. Electrical stimulation of either deep peroneus (DP) or superficial peroneus (SP) nerves increased the duration of the TA electroneurogram. Medial gastrocnemius (MG) stretching or MG nerve electrical stimulation produced a reduction in the TA electroneurogram and an initial MG extensor burst. MRes waxed and waned during the scratch cycle. Conclusion: Descending pathways and segmental afferent fibers, as well as 5-HT and WAY, can change the FS pattern. To our understanding, the half-center hypothesis is the most suitable for explaining the AP in MCC.

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221). We created a system consisting of different classifiers that is feed with novel morphometric features extracted from the digital micrographs, along with other non-morphometric data to predict pregnancy. It was evaluated using five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cross validation to assess the model's generalization capabilities. In the database A, the SVM classifier achieved an F1 score of 0.74, and AUC of 0.77. In the database B the RF classifier obtained a F1 score of 0.71, and AUC of 0.75. Our results suggest that the system is able to predict a positive pregnancy test from a single digital image, offering a novel approach with the advantages of using a small database, being highly adaptable to different laboratory settings, and easy integration into clinical practice.

Kinematic Locomotion Changes in C57BL/6 Mice Infected with Toxoplasma Strain ME49.

Chronic infection with the intracellular parasite Toxoplasma gondii produces an accumulation of cysts in the brain and muscle, causing tissue damage. The cysts in the brain motor regions affect some kinematic locomotion parameters in the host. To localize the brain cysts from Toxoplasma gondii and study the changes in kinematic locomotion in C57BL/6 mice. Female adult C57BL/6 mice were infected orally with 30 ME-49 Toxoplasma gondii cysts. An uninfected group (n = 7) and two infected groups, examined 15 and 40 days postinfection, were used for this study. To evaluate kinematic locomotion, the mice were marked with indelible ink on the iliac crest, hip, knee, ankle, and phalangeal metatarsus of the left and right hindlimbs. At least three recordings were carried out to obtain videos of the left and right hindlimbs. Mice were video recorded at 90 fps at a resolution of 640 × 480 pixels while walking freely in a transparent Plexiglass tunnel. We measured the hindlimb pendular movement and the hindlimb transfer [linear displacement] curves for each step and evaluated them statistically with Fréchet dissimilarity tests. Afterward, the mice were sacrificed, and the brain, heart, skeletal muscle, lung, liver, and kidney were obtained. The different tissues were stained with hematoxylin and eosin for analysis with optical microscopy. Topographic localization of the cysts was made using bregma coordinates for the mouse brain. The cysts were distributed in several brain regions. In one mouse, cyst accumulation occurred in the hippocampus, coinciding with an alteration in foot displacement. The step length was different among the different studied groups.

Modeling hind-limb kinematics using a bio-inspired algorithm with a local search.

BACKGROUND: Laboratory rats play a critical role in research because they provide a biological model that can be used for evaluating the affectation of diseases and injuries, and for the evaluation of the effectiveness of new drugs and treatments. The analysis of locomotion in laboratory rats facilitates the understanding of motor defects in many diseases, as well as the damage and recovery after peripheral and central nervous system injuries. However, locomotion analysis of rats remains a great challenge due to the necessity of labor intensive manual annotations of video data required to obtain quantitative measurements of the kinematics of the rodent extremities. In this work, we present a method that is based on the use of a bio-inspired algorithm that fits a kinematic model of the hind limbs of rats to binary images corresponding to the segmented marker of images corresponding to the rat's gait. The bio-inspired algorithm combines a genetic algorithm for a group of the optimization variables with a local search for a second group of the optimization variables. RESULTS: Our results indicate the feasibility of employing the proposed approach for the automatic annotation and analysis of the locomotion patterns of the posterior extremities of laboratory rats. CONCLUSIONS: The adjustment of the hind limb kinematic model to markers of the video frames corresponding to rat's gait sequences could then be used to analyze the motion patterns during the steps, which, in turn, can be useful for performing quantitative evaluations of the effect of lesions and treatments on rats models.

Is sex an influential factor in type-1 diabetes neurofunctional development? A preliminary study.

The aim of the study was to evaluate the neurofunctional effect of gender in Type-1 Diabetes Mellitus (T1DM) patients during a Visual Spatial Working Memory (VSWM) task. The study included 28 participants with ages ranging from 17-28 years. Fourteen well-controlled T1DM patients (7 female) and 14 controls matched by age, sex, and education level were scanned performing a block-design VSWM paradigm. Behavioral descriptive analyses and mean comparisons were done, and between-group and condition functional activation patterns were also compared. Whole-brain cumulative BOLD signal (CumBS), voxel-wise BOLD level frequency, Euclidean distance, and divergence indices were also calculated. There were no significant differences between or within-group sex differences for correct responses and reaction times. Functional activation analyses showed that females had activation in more brain regions, and with larger clusters of cortical activations than males. Furthermore, BOLD activation was higher in males. Despite the preliminary nature of the present study given the relatively small sample size, current results acknowledge for the first time that sex might contribute to differences in functional activation in T1DM patients. Findings suggest that sex differences should be considered when studying T1DM-disease development.

Genomic signal processing for DNA sequence clustering.

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

Hind limb motoneurons activity during fictive locomotion or scratching induced by pinna stimulation, serotonin, or glutamic acid in brain cortex-ablated cats.

In brain cortex-ablated cats (BCAC), hind limb motoneurons activity patterns were studied during fictive locomotion (FL) or fictive scratching (FS) induced by pinna stimulation. In order to study motoneurons excitability: heteronymous monosynaptic reflex (HeMR), intracellular recording, and individual Ia afferent fiber antidromic activity (AA) were analyzed. The intraspinal cord microinjections of serotonin or glutamic acid effects were made to study their influence in FL or FS During FS, HeMR amplitude in extensor and bifunctional motoneurons increased prior to or during the respective electroneurogram (ENG). In soleus (SOL) motoneurons were reduced during the scratch cycle (SC). AA in medial gastrocnemius (MG) Ia afferent individual fibers of L6-L7 dorsal roots did not occur during FS Flexor digitorum longus (FDL) and MG motoneurons fired with doublets during the FS bursting activity, motoneuron membrane potential from some posterior biceps (PB) motoneurons exhibits a depolarization in relation to the PB (ENG). It changed to a locomotor drive potential in relation to one of the double ENG, PB bursts. In FDL and semitendinosus (ST) motoneurons, the membrane potential was depolarized during FS, but it did not change during FL Glutamic acid injected in the L3-L4 spinal cord segment favored the transition from FS to FL During FL, glutamic acid produces a duration increase of extensors ENGs. Serotonin increases the ENG amplitude in extensor motoneurons, as well as the duration of scratching episodes. It did not change the SC duration. Segregation and motoneurons excitability could be regulated by the rhythmic generator and the pattern generator of the central pattern generator.

On DNA numerical representations for genomic similarity computation.

Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

A physics-based intravascular ultrasound image reconstruction method for lumen segmentation.

Intravascular ultrasound (IVUS) refers to the medical imaging technique consisting of a miniaturized ultrasound transducer located at the tip of a catheter that can be introduced in the blood vessels providing high-resolution, cross-sectional images of their interior. Current methods for the generation of an IVUS image reconstruction from radio frequency (RF) data do not account for the physics involved in the interaction between the IVUS ultrasound signal and the tissues of the vessel. In this paper, we present a novel method to generate an IVUS image reconstruction based on the use of a scattering model that considers the tissues of the vessel as a distribution of three-dimensional point scatterers. We evaluated the impact of employing the proposed IVUS image reconstruction method in the segmentation of the lumen/wall interface on 40MHz IVUS data using an existing automatic lumen segmentation method. We compared the results with those obtained using the B-mode reconstruction on 600 randomly selected frames from twelve pullback sequences acquired from rabbit aortas and different arteries of swine. Our results indicate the feasibility of employing the proposed IVUS image reconstruction for the segmentation of the lumen.