Subendometrial blood flow detected by Doppler ultrasound associates with pregnancy outcomes of frozen embryo transfer in patients with thin endometrium.

PURPOSE: Multiple factors have been shown to influence the rate of clinical pregnancy after FET in IVF treatment, including embryo quality, synchronization of embryo and endometrium, and endometrial receptivity (ER). The subendometrial blood flow conditions could also contribute potentially major effects toward the establishment and maintenance of pregnancy. We conducted a retrospective cohort study to examine the correlation between subendometrial blood flow, as determined by Doppler ultrasound, and pregnancy outcomes in IVF patients with a thin endometrium (endometrium thickness [EMT] ≤ 0.7 cm). METHODS: This was a retrospective cohort study conducted at a university-affiliated reproductive hospital from January 2017 to April 2023. The EMT and subendometrial blood flows were assessed using transvaginal color Doppler ultrasound and evaluated by experienced clinical ultrasound physicians on the endometrial transformation day. The pregnancy outcomes were followed up and documented in clinical medical records through the IVF cohort study at our center. RESULTS: In the patients with 0.5 cm ≤ EMT ≤ 0.7 cm, the embryo implantation rate was statistically significant increased in the patients with the presence of subendometrial blood flow (OR 1.484; 95% CI, 1.001-2.200; P = 0.049; aOR 1.425; 95% CI, 1.030-2.123; P = 0.003). Patients with discernible subendometrial blood flow have superior live birth (P = 0.028), clinical pregnancy (P = 0.049), and embryo implantation (P = 0.027) compared to the patients without subendometrial blood flow when the EMT is ≤ 0.7 cm. CONCLUSIONS: The presence of subendometrial blood flow detected by ultrasound was positively associated with successful embryo implantation and favorable pregnancy outcomes in patients with thin endometrium undergoing FET.

Perfluoroalkyl-modified covalent organic frameworks for continuous photocatalytic hydrogen peroxide synthesis and extraction in a biphasic fluid system.

H2O2 photosynthesis represents an appealing approach for sustainable and decentralized H2O2 production. Unfortunately, current reactions are mostly carried out in laboratory-scale single-phase batch reactors, which have a limited H2O2 production rate (<100 µmol h-1) and cannot operate in an uninterrupted manner. Herein, we propose continuous H2O2 photosynthesis and extraction in a biphasic fluid system. A superhydrophobic covalent organic framework photocatalyst with perfluoroalkyl functionalization is rationally designed and prepared via the Schiff-base reaction. When applied in a home-built biphasic fluid photo-reactor, the superhydrophobicity of our photocatalyst allows its selective dispersion in the oil phase, while formed H2O2 is spontaneously extracted to the water phase. Through optimizing reaction parameters, we achieve continuous H2O2 photosynthesis and extraction with an unprecedented production rate of up to 968 µmol h-1 and tunable H2O2 concentrations from 2.2 to 38.1 mM. As-obtained H2O2 solution could satisfactorily meet the general demands of household disinfection and wastewater treatments.

Integration of texture analysis based on DCE-MRI Ktrans map and metabolomics of early bone marrow microvascular changes in alloxan-induced diabetic rabbits.

OBJECTIVE: To evaluate early bone marrow microvascular changes in alloxan-induced diabetic rabbits using IDEAL-IQ fat quantification, texture analysis based on DCE-MRI Ktrans map, and metabolomics. MATERIALS AND METHODS: 24 male Japanese rabbits were randomly divided into diabetic (n = 12) and control (n = 12) groups. All rabbits underwent sagittal MRI of the lumbar vertebrae at the 0th,4th, 8th, 12th, and 16th week, respectively. The fat fraction (FF) ratio and quantitative permeability of the lumbar bone marrow was measured. Texture parameters were extracted from DCE-MRI Ktrans map. At 16th week, lumbar vertebrae 5 and 6 were used for histological analysis. Lumbar vertebra 7 was crushed to obtain bone marrow for metabolomics research. RESULTS: The FF ratio and Ktrans of the lumbar bone marrow in diabetic group were increased significantly at 16th week (t = 2.226, P = 0.02; Z = -2.721, P < 0.01). Nine texture feature parameters based on DCE-MRI Ktrans map were significantly different between the groups at the 16th week (all P < 0.05). Pathway analysis showed that diabetic bone marrow microvascular changes were mainly related to linoleic acid metabolism. Differential metabolites were correlated with the number of adipocytes, FF ratio, and permeability parameters. CONCLUSION: The integration of metabolomics with texture analysis based on DCE-MRI Ktrans map may be used to evaluate diabetic bone marrow microvascular changes at an early stage. It remains to be validated in clinical studies whether the integration of metabolomics with texture analysis based on the DCE-MRI Ktrans map can effectively evaluate diabetic bone marrow.

Variation of the Tegmen and Cercus in Sinopodisma rostellocerca (Orthoptera: Acrididae: Melanoplinae) with Proposal of a New Synonym.

Intraspecific variation is ubiquitous from individual traits to population level and plays an important role in a variety of fields. However, it is often ignored by systematists and comparative evolutionary biologists. In view of the limited knowledge of intraspecific variation, morphology-based identification has hindered the recognition of species borders and led to a great number of problems in the field of taxonomy and systematics. In this study, the intraspecific variation of the tegmen and cercus in Sinopodisma rostellocerca was examined, the variation patterns were summarized and the relationship between S. rostellocerca and S. hengshanica was discussed. The results showed that the intraspecific variation in the tegmen and male cercus was mainly manifested in the length and shape of the apical margin and dorso- and ventro-apical angles; this substantial variation occurred not only among intrapopulation individuals but also between the different sides of the same individuals, and all types of variation in S. hengshanica fell into the range of variation in S. rostellocerca, leading to the disappearance of the boundary between the two species. Therefore, S. hengshanica was herein considered as a new junior synonym of S. rostellocerca.

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

BACKGROUND: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. METHODS: This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. RESULTS: The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea-DL was elevated compared to Parea-O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea-DL and Parea-O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea-DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively. CONCLUSIONS: STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

Semi-TSGAN: Semi-Supervised Learning for Highlight Removal Based on Teacher-Student Generative Adversarial Network.

Despite recent notable advancements in highlight image restoration techniques, the dearth of annotated data and the lightweight deployment of highlight removal networks pose significant impediments to further advancements in the field. In this paper, to the best of our knowledge, we first propose a semi-supervised learning paradigm for highlight removal, merging the fusion version of a teacher-student model and a generative adversarial network, featuring a lightweight network architecture. Initially, we establish a dependable repository to house optimal predictions as pseudo ground truth through empirical analyses guided by the most reliable No-Reference Image Quality Assessment (NR-IQA) method. This method serves to assess rigorously the quality of model predictions. Subsequently, addressing concerns regarding confirmation bias, we integrate contrastive regularization into the framework to curtail the risk of overfitting on inaccurate labels. Finally, we introduce a comprehensive feature aggregation module and an extensive attention mechanism within the generative network, considering a balance between network performance and computational efficiency. Our experimental evaluations encompass comprehensive assessments on both full-reference and non-reference highlight benchmarks. The results demonstrate conclusively the substantive quantitative and qualitative enhancements achieved by our proposed algorithm in comparison to state-of-the-art methodologies.

Multi-mode adaptive control strategy for a lower limb rehabilitation robot.

Different patients have different rehabilitation requirements. It is essential to ensure the safety and comfort of patients at different recovery stages during rehabilitation training. This study proposes a multi-mode adaptive control method to achieve a safe and compliant rehabilitation training strategy. First, patients' motion intention and motor ability are evaluated based on the average human-robot interaction force per task cycle. Second, three kinds of rehabilitation training modes-robot-dominant, patient-dominant, and safety-stop-are established, and the adaptive controller can dexterously switch between the three training modes. In the robot-dominant mode, based on the motion errors, the patient's motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and encourage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the controller switches to the safety-stop mode to ensure patient safety. The stabilities of the adaptive controller under three training modes are then proven using Lyapunov theory. Finally, the effectiveness of the multi-mode adaptive controller is verified by simulation results.

The American Cherimoya Genome Reveals Insights into the Intra-Specific Divergence, the Evolution of Magnoliales, and a Putative Gene Cluster for Acetogenin Biosynthesis.

Annona cherimola (cherimoya) is a species renowned for its delectable fruit and medicinal properties. In this study, we developed a chromosome-level genome assembly for the cherimoya 'Booth' cultivar from the United States. The genome assembly has a size of 794 Mb with a N50 = 97.59 Mb. The seven longest scaffolds account for 87.6% of the total genome length, which corresponds to the seven pseudo-chromosomes. A total of 45,272 protein-coding genes (≥30 aa) were predicted with 92.9% gene content completeness. No recent whole genome duplications were identified by an intra-genome collinearity analysis. Phylogenetic analysis supports that eudicots and magnoliids are more closely related to each other than to monocots. Moreover, the Magnoliales was found to be more closely related to the Laurales than the Piperales. Genome comparison revealed that the 'Booth' cultivar has 200 Mb less repeats than the Spanish cultivar 'Fino de Jete', despite their highly similar (>99%) genome sequence identity and collinearity. These two cultivars were diverged during the early Pleistocene (1.93 Mya), which suggests a different origin and domestication of the cherimoya. Terpene/terpenoid metabolism functions were found to be enriched in Magnoliales, while TNL (Toll/Interleukin-1-NBS-LRR) disease resistance gene has been lost in Magnoliales during evolution. We have also identified a gene cluster that is potentially responsible for the biosynthesis of acetogenins, a class of natural products found exclusively in Annonaceae. The cherimoya genome provides an invaluable resource for supporting characterization, conservation, and utilization of Annona genetic resources.