The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation.

OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms. METHODS: A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children's Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly. RESULTS: Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636-0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759-0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741-0.884). CONCLUSION: We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions.

DNA nanoassembly based turn-on amplification probe for sensitive colorimetric CRISPR/Cas12a-mediated detection of pathogen DNA.

Clustered regularly interspaced short palindromic repeat (CRISPR) system has been explored as an efficient tool for nucleic acid diagnostics. However, it normally needs instrumentation or produces turn-off signals. Herein, a bulged Y-shape DNA (Y-DNA) nanoassembly was designed and synthesized as a novel turn-on probe. A CRISPR/Cas12a and Y-DNA probe mediated colorimetric assay (named as CYMCOA) strategy was developed for visual detection of pathogen DNA. Upon activating Cas12a with pathogen DNA, the Y-DNA bulge is catalytically trans-cleaved, releasing the G-quadruplex sequence embedded in the Y-DNA nanoassembly as a peroxidase-like DNAzyme. Visible signals with chromogen substrates are thus produced. The CYMCOA strategy was combined with recombinase polymerase amplification (RPA), an isothermal amplification technique, in detecting Helicobacter pylori (Hp) bacteria and SARS-CoV-2 N plasmids as two model pathogens. The bioassay has very excellent detection sensitivity and specificity, owing to the triple cascade amplification reactions and the very low mismatch tolerance. The lower limit of detection values were 0.16 cfu⋅mL-1, 1.5 copies⋅µL-1, and 0.17 copies⋅µL-1 for Hp bacteria, Hp plasmids, and SARS-CoV-2 N plasmids respectively. The detection is fast and accurate. The colorimetric bioassay strategy provides to be a simple, accurate, fast and instrumentation-free platform for nucleic acids detections in various settings, including crude and emergent situations.

Superconducting Field-Effect Transistors with PdxTe-Te Intimate Contacts.

Superconducting-based electronic devices have shown great potential for future quantum computing applications. One key building block device is a superconducting field-effect transistor based on a superconductor-semiconductor-superconductor Josephson-junction (JJ) with a gate-tunable semiconducting channel. However, the performance of such devices is highly dependent on the quality of the superconductor to semiconductor interface. In this study, we present an alternative method to obtain a high-quality interface by using intimate contact. We investigate the proximity-induced superconductivity in chiral crystal tellurium (Te) and fabricate a PdxTe-Te-PdxTe JJ with an ambipolar supercurrent that is gate-tunable and exhibits multiple Andreev reflections. The semiconducting two-dimensional Te single crystal is grown hydrothermally and partially converted to superconducting PdxTe by controlled annealing. Our work demonstrates a promising path for realizing controllable superconducting electronic devices with high-quality superconducting interfaces; thus, we can continue to advance the field of quantum computing and other interface-based technologies.

Optically Gated Electrostatic Field-Effect Thermal Transistor.

Dynamic tuning of thermal transport in solids is scientifically intriguing with wide applications for thermal transport control in electronic devices. In this work, we demonstrate a thermal transistor, a device in which heat flow can be regulated using external control, realized in a topological insulator (TI) through the topological surface states. The tuning of thermal transport is achieved by using optical gating of a thin dielectric layer deposited on the TI film. The gate-dependent thermal conductivity is measured using micro-Raman thermometry. The transistor has a large ON/OFF ratio of 2.8 at room temperature and can be continuously and repetitively switched in tens of seconds by optical gating and potentially much faster by electrical gating. Such thermal transistors with a large ON/OFF ratio and fast switching times offer the possibilities of smart thermal devices for active thermal management and control in future electronic systems.

Identification and Verification of Error-Related Potentials Based on Cerebellar Targets.

The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain-computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5-10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.