The authors examine research involving organocatalytic asymmetric Morita-Baylis-Hillman/aza-Morita-Baylis-Hillman reactions. The Morita-Baylis-Hillman is a carbon-carbon reaction.
Objective
To assess the accuracy of dynamic computer‐assisted implant surgery.
Materials and methods
An electronic search up to March 2020 was conducted using PubMed, Embase, and the Cochrane Central ...Register of Controlled Trial to identify studies using dynamic navigation in implant surgery, and additional manual search was performed as well. Clinical trials and model studies were selected. The primary outcome was accuracy. A single‐arm meta‐analysis of continuous data was conducted. Meta‐regression was utilized for comparison on study design, guidance method, jaw, and systems.
Results
Ten studies, four randomized controlled trials (RCT) and six prospective studies, met the inclusion criteria. A total of 1,298 drillings and implants were evaluated. The meta‐analysis of the accuracy (five clinical trials and five model studies) revealed average global platform deviation, global apex deviation, and angular deviation were 1.02 mm, 95% CI (0.83, 1.21), 1.33 mm, 95% CI (0.98, 1.67), and 3.59°, 95% CI (2.09, 5.09). Meta‐regression shown no difference between model studies and clinical trials (p = .295, 0.336, 0.185), drilling holes and implant (p = .36, 0.279, 0.695), maxilla and mandible (p = .875, 0.632, 0.281), and five different systems (p = .762, 0.342, 0.336).
Conclusion
Accuracy of dynamic computer‐aided implant surgery reaches a clinically acceptable range and has potential in clinical usage, but more patient‐centered outcomes and socio‐economic benefits should be reported.
The miRNA miR-106b-5p has been previously reported to be increased in hepatocellular carcinoma (HCC) tissues compared to cirrhotic tissues. The purpose of this study was to detect its expression in ...HCC cell lines with distinct metastatic potentials and to explore the molecular mechanisms underlying HCC stemness and migration.
miR-106b-5p expression was studied in HCC tissues and cell lines. In vitro cancer stem cell (CSC)-like properties, cell migration and invasion were compared between HCC cell lines with upregulation or downregulation of miR-106b-5p. In vivo tail vein injection models were established to evaluate the role of miR-106b-5p in lung metastasis. Bioinformatics programs, luciferase reporter assay and rescue experiments were used to validate the downstream targets of miR-106b-5p. The relationship between the expression of the targeted gene and clinicopathological parameters was also analyzed.
miR-106b-5p expression was higher in HCC tissues and cell lines than that in non-tumor tissues and hepatocyte Chang liver, respectively. Upregulation of miR-106b-5p exhibited a promoting role in CSC properties, cell migration and activation of phosphatidylinositol-3 kinase (PI3K)/Akt signaling in vitro, as well as in lung metastasis in vivo. However, downregulation of miR-106b-5p exhibited the opposite effect. Furthermore, PTEN was verified as a direct target of miR-106b-5p. Upon clinicopathological analysis, lower level of PTEN was significantly associated with more aggressive characteristics. Patients with high PTEN expression had longer overall survival and disease-free survival.
miR-106b-5p promotes HCC stemness maintenance and metastasis by targeting PTEN via PI3K/Akt pathway. Inhibition of miR-106b-5p might be effective therapeutic strategies to treat advanced HCC.
Oral squamous cell carcinoma (OSCC), as the most common type of oral cancer, is responsible for almost 3% of all malignant tumors worldwide. Non‐coding RNAs such as lncRNAs and microRNAs have been ...involved in many cancers including OSCC. Recently, lncRNA metastasis‐associated lung adenocarcinoma transcript‐1 (MALAT1) has been reported to play an oncogenic role in OSCC metastasis. However, the underlying mechanism of MALAT1 in regulating OSCC progression remains unclear. The aim of this study was to investigate the specific role of MALAT1 in OSCC development. It was observed that MALAT1 was upregulated in OSCC cell lines. Inhibition of MALAT1 can prevent OSCC proliferation while overexpressing MALAT1 promoted OSCC progression. In addition, bioinformatics search was used to identify that miR‐125b was a direct target of MALAT1, which indicated a negative correlation between MALAT1 and miR‐125b. Besides these, STAT3 was predicted as a binding target of miR‐125b in OSCC. Overexpression of MALAT1 was able to suppress the tumor inhibitory effect of miR‐125b mimics via upregulating STAT3. Moreover, the function of MALAT1 in OSCC development was further investigated by using in vivo assays. The established nude mice models revealed that downregulated MALAT1 greatly inhibited OSCC tumor growth and reversely upregualated MALAT1 promoted OSCC development via miR‐125b/STAT3 axis, respectively. In conclusion, MALAT1 can function as a competing endogenous RNA (ceRNA) to modulate STAT3 expression by absorbing miR‐125b in OSCC and could be used as a novel therapeutic target in OSCC diagnosis and treatment.
Our findings in OSCC cell lines and xenografts suggested MALAT1 as an oncogene, which can promote OSCC development. This was the first report to demonstrate that MALAT1 can function as a ceRNA and modulate STAT3 expression by sponging miR‐125b in OSCC both in vitro and in vivo. Our data indicated that MALAT1 could be used as a therapeutic target in OSCC diagnosis and treatment.
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named
Point Cloud Transformer
(PCT) for ...point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.
Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in ...biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.
We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and Formula: see text are the top 3 best performers on all five datasets.
This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.
Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this ...aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository
https://github.com/MenghaoGuo/Awesome-Vision-Attentions
is dedicated to collecting related work. We also suggest future directions for attention mechanism research.
Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks. Self-attention updates the feature at each position by ...computing a weighted sum of features using pair-wise affinities across all positions to capture the long-range dependency within a single sample. However, self-attention has quadratic complexity and ignores potential correlation between different samples. This article proposes a novel attention mechanism which we call external attention , based on two external, small, learnable, shared memories, which can be implemented easily by simply using two cascaded linear layers and two normalization layers; it conveniently replaces self-attention in existing popular architectures. External attention has linear complexity and implicitly considers the correlations between all data samples. We further incorporate the multi-head mechanism into external attention to provide an all-MLP architecture, external attention MLP (EAMLP), for image classification. Extensive experiments on image classification, object detection, semantic segmentation, instance segmentation, image generation, and point cloud analysis reveal that our method provides results comparable or superior to the self-attention mechanism and some of its variants, with much lower computational and memory costs.
Catalytic asymmetric synthesis has received considerable attention over the past few decades, becoming a highly dynamic area of chemical research with significant contributions to the field of ...organic synthesis. In the development of new catalysts, the concept of multifunctional catalysis described by Shibasaki and co-workers, namely, the combination of more than one functional group within a single molecule to activate the transformation, has proved a powerful strategy in the design of efficient transition metal-containing catalysts. A variety of reactions have since been addressed with multifunctional organocatalysts. One example is the Morita−Baylis−Hillman (MBH) reaction, in which a carbon−carbon bond is created between the α-position of an activated double-bond compound and a carbon electrophile. The seminal report on this reaction in 1972 described the prototypical couplings of (i) ethyl acrylate with acetaldehyde and (ii) acrylonitrile with acetaldehyde; the reaction is promoted by the conjugate addition of a nucleophilic catalyst to the α,β-unsaturated aldehyde. Many variations of the MBH reaction have been reported, such as the aza-MBH reaction, in which an N-tosyl imine stands in for acetaldehyde. Recent innovations include the development of chiral molecules that catalyze the production of asymmetric products. In this Account, we describe the refinement of catalysts for the MBH and related reactions, highlighting a series of multifunctional chiral phosphines that we have developed and synthesized over the past decade. We also review similar catalysts developed by other groups. These multifunctional chiral phosphines, which contain Lewis basic and Brønsted acidic sites within one molecule, provide good-to-excellent reactivities and stereoselectivities in the asymmetric aza-MBH reaction, the MBH reaction, and other related reactions. We demonstrate that the reactivities and enantioselectivies of these multifunctional chiral phosphines can be adjusted by enhancing the reactive center’s nucleophilicity, which can be finely tuned by varying nearby hydrogen-bonding donors. Artificial catalysts now provide highly economic access to many desirable compounds, but the general adaptability and reactivity of these platforms remain problematic, particularly in comparison to nature’s catalysts, enzymes. The multifunctional organocatalysts described in this Account represent another positive step in the synthetic chemist’s efforts to profitably mimic nature’s catalytic platform, helping develop small-molecule catalysts with enzyme-like reactivities and selectivities.
The latest advances in the field of gold-catalyzed divergent synthesis of carbo- and heterocycles are highlighted in this Perspective. These gold-catalyzed reactions, which can provide structurally ...novel carbo- and heterocycles, were achieved with good product selectivities, through subtle choice of substrates, catalysts, ligands, and counterions. The related reaction mechanisms are also discussed.
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