MiR-518c-3p alleviates endometriosis by inhibiting ectopic endometrial migration and epithelial-mesenchymal transition via targeting ZNF608.

PURPOSE: The present study was performed to clarify the regulatory mechanism of miR-518c-3p in the progression of endometriosis (EMs). METHODS: MicroRNAs (miRNAs) potentially acting on EMs were predicted by bioinformatics databases and validated in normal and ectopic endometrium. The miR-518c-3p mimics were transfected into endometrial stromal cells (ESCs), and cell growth, death, and proliferation marker proteins expression were detected. The targeting relationship of miR-518c-3p with zinc finger protein 608 (ZNF608) was validated by luciferase reporter assay. ESCs were incubated with miR-518c-3p mimics alone or co-transfected with pcDNA-ZNF608, and growth, death, as well as proliferation and epithelial-mesenchymal transition (EMT) marker protein expression were detected. A rat model of EMs overexpressing miR-518c-3p alone or ZNF608 simultaneously was constructed to detect ectopic endometrial cell apoptosis and cyst volume in rats. RESULTS: MiR-518c-3p expression was downregulated in ectopic endometrium. MiR-518c-3p mimic inhibited migration, invasion and proliferation of ESCs, and promoted apoptosis. MiR-518c-3p targeted the 3'UTR of ZNF608. ZNF608 expression was upregulated in ESCs and ectopic endometrium, and the regulatory effect of pcDNA-ZNF608 on ESCs was opposite to that of miR-518c-3p mimics. ZNF608 overexpressing rats had greater endometrial cyst weight and volume, and decreased endometrial apoptosis compared with miR-518c-3p overexpressing alone. CONCLUSION: MiR-518c-3p inhibited growth, metastasis and EMT of ESCs and decreased ectopic endometrial area in rats with EMs by targeting ZNF608.

Implementation of Quantum Algorithms via Fast Three-Rydberg-Atom CCZ Gates.

Multiqubit CCZ gates form one of the building blocks of quantum algorithms and have been involved in achieving many theoretical and experimental triumphs. Designing a simple and efficient multiqubit gate for quantum algorithms is still by no means trivial as the number of qubits increases. Here, by virtue of the Rydberg blockade effect, we propose a scheme to rapidly implement a three-Rydberg-atom CCZ gate via a single Rydberg pulse, and successfully apply the gate to realize the three-qubit refined Deutsch-Jozsa algorithm and three-qubit Grover search. The logical states of the three-qubit gate are encoded to the same ground states to avoid an adverse effect of the atomic spontaneous emission. Furthermore, there is no requirement for individual addressing of atoms in our protocol.

Improving the Robustness of Entangled States by Basis Transformation.

In the practical application of quantum entanglement, entangled particles usually need to be distributed to many distant parties or stored in different quantum memories. In these processes, entangled particles unavoidably interact with their surrounding environments, respectively. We here systematically investigate the entanglement-decay laws of cat-like states under independent Pauli noises with unbalanced probability distribution of three kinds of errors. We show that the robustness of cat-like entangled states is not only related to the overall noise strength and error distribution parameters, but also to the basis of qubits. Moreover, we find that whether a multi-qubit state is more robust in the computational basis or transversal basis depends on the initial entanglement and number of qubits of the state as well as the overall noise strength and error distribution parameters of the environment. However, which qubit basis is conductive to enhancing the robustness of two-qubit states is only dependent on the error distribution parameters. These results imply that one could improve the intrinsic robustness of entangled states by simply transforming the qubit basis at the right moment. This robustness-improving method does not introduce extra particles and works in a deterministic manner.

Increased serum resistin levels correlate with psoriasis: a meta-analysis.

BACKGROUND: Recent studies implicate adipokines in the pathogenesis of inflammatory diseases, including psoriasis. In this study we evaluated the significance of serum resistin levels in psoriasis patients using a meta-analysis approach.223 METHODS: Relevant articles were retrieved by searching the following English and Chinese databases: Cochrane Library, PubMed, Springer Link, Chinese Biomedical Database (CBM) and Chinese National Knowledge Infrastructure (CNKI). The retrieved studies were subjected to a thorough screening procedure to identify case-control studies that contained the required data. Data was extracted from each study and Version 12.0 STATA statistical software was employed for statistical analyses. RESULTS: Nine case-control studies, containing 421 psoriasis patients and 348 healthy controls, were included in this study. The major result of the meta-analysis revealed a statistically significant association between serum resistin levels and psoriasis (SMD=2.22, 95%CI: 1.14-3.29, P<0.001). Subgroup analysis based on ethnicity showed that, compared to the healthy controls, serum resistin levels were markedly higher in psoriasis patients in both Asian and Caucasian populations (Asians: SMD=3.27, 95%CI=1.62~4.91, P<0.001; Caucasians: SMD=0.91, 95%CI=0.28~1.54, P<0.001). CONCLUSIONS: Based on our results, we conclude that serum resistin level in psoriasis patients is higher than healthy controls, and raises the possibility that elevated serum resistin levels may be a novel diagnostic marker in psoriasis and may predict the occurrence of co-morbidities in psoriasis patients.

Improved dual-aggregation polyp segmentation network combining a pyramid vision transformer with a fully convolutional network.

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style. Concretely, in the transformer branch, we adopted the B3 variant of pyramid vision transformer v2 (PVTv2-B3) as an image encoder for capturing multi-scale global features and modeling long-distant interdependencies between them whilst designing an innovative multi-stage feature aggregation decoder (MFAD) to highlight critical local feature details and effectively integrate them into global features. In the decoder, the adaptive feature aggregation (AFA) block was constructed for fusing high-level feature representations of different scales generated by the PVTv2-B3 encoder in a stepwise adaptive manner for refining global semantic information, while the ResidualBlock module was devised to mine detailed boundary cues disguised in low-level features. With the assistance of the selective global-to-local fusion head (SGLFH) module, the resulting boundary details were aggregated selectively with these global semantic features, strengthening these hierarchical features to cope with scale variations of polyps. The FCN branch embedded in the designed ResidualBlock module was used to encourage extraction of highly merged fine features to match the outputs of the Transformer branch into full-size segmentation maps. In this way, both branches were reciprocally influenced and complemented to enhance the discrimination capability of polyp features and enable a more accurate prediction of a full-size segmentation map. Extensive experiments on five challenging polyp segmentation benchmarks demonstrated that the proposed Dua-PSNet owned powerful learning and generalization ability and advanced the state-of-the-art segmentation performance among existing cutting-edge methods. These excellent results showed our Dua-PSNet had great potential to be a promising solution for practical polyp segmentation tasks in which wide variations of data typically occurred.

Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images.

Automated fine-grained diabetic retinopathy (DR) grading was of great significance for assisting ophthalmologists in monitoring DR and designing tailored treatments for patients. Nevertheless, it is a challenging task as a result of high intra-class variations, high inter-class similarities, small lesions, and imbalanced data distributions. The pivotal factor for the success in fine-grained DR grading is to discern more subtle associated lesion features, such as microaneurysms (MA), Hemorrhages (HM), soft exudates (SE), and hard exudates (HE). In this paper, we constructed a simple yet effective deep attentive convolutional neural network (DACNN) for DR grading and lesion discovery with only image-wise supervision. Designed as a top-down architecture, our model incorporated stochastic atrous spatial pyramid pooling (sASPP), global attention mechanism (GAM), category attention mechanism (CAM), and learnable connected module (LCM) to better extract lesion-related features and maximize the DR grading performance. To be concrete, we devised sASPP combining randomness with atrous spatial pyramid pooling (ASPP) to accommodate the various scales of the lesions and struggle against the co-adaptation of multiple atrous convolutions. Then, GAM was introduced to extract class-agnostic global attention feature details, whilst CAM was explored for seeking class-specific distinctive region-level lesion feature information and regarding each DR severity grade in an equal way, which tackled the problem of imbalance DR data distributions. Further, the LCM was designed to automatically and adaptively search the optimal connections among layers for better extracting detailed small lesion feature representations. The proposed approach obtained high accuracy of 88.0% and kappa score of 88.6% for multi-class DR grading task on the EyePACS dataset, respectively, while 98.5% AUC, 93.8% accuracy, 87.9% kappa, 90.7% recall, 94.6% precision, and 92.6% F1-score for referral and non-referral classification on the Messidor dataset. Extensive experimental results on three challenging benchmarks demonstrated that the proposed approach achieved competitive performance in DR grading and lesion discovery using retinal fundus images compared with existing cutting-edge methods, and had good generalization capacity for unseen DR datasets. These promising results highlighted its potential as an efficient and reliable tool to assist ophthalmologists in large-scale DR screening.

One-step and one-pot-two-step radiosynthesis of cyclo-RGD-(18)F-aryltrifluoroborate conjugates for functional imaging.

Arylboronates capture aqueous (18)F-fluoride in one step to afford a highly polar (18)F-labeled aryltrifluoroborate anion ((18)F-ArBF(3) (-)) that clears rapidly in vivo. To date however, there is little data to show that a ligand labeled with a prosthetic (18)F-ArBF(3) (-) will provide functional images. RGD, a high-affinity ligand for integrins that are present on the cell surface of numerous tumors, has been labeled in many formats with many different radionuclides, and as such represents a well-established ligand that can be used to evaluate new labeling methods. Herein we have labeled RGD with a prosthetic (18)F-ArBF(3) (-) via two approaches for the first time: 1) a RGD-boronate bioconjugate is directly labeled in one step and 2) an alkyne-modified arylborimidine is first converted to the corresponding (18)F-ArBF(3) (-) which is then conjugated to an RGD-azide via Cu(+)-mediated [2+3] dipolar cycloaddition in one pot over two steps. RGD-(18)F-ArBF(3) (-) bionconjugates were produced in reasonable radiochemical yields using low amounts of (18)F-fluoride anion (10-50 mCi). Despite relatively low specific activities, good tumor images are revealed in each case.