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Neoadjuvant chemoradiotherapy (NACRT) was the standard treatment for patients with locally advanced rectal cancer (LARC) with proficient mismatch repair (pMMR) proteins. In this randomized phase 2 trial (ClinicalTrial.gov: NCT04304209), 134 pMMR LARC patients were randomly (1:1) assigned to receive NACRT or NACRT and the programmed cell death protein 1 (PD-1) antibody sintilimab. As the primary endpoint, the total complete response (CR) rate is 26.9% (18/67, 95% confidence interval [CI] 16.0%-37.8%) and 44.8% (30/67, 95% CI 32.6%-57.0%) in the control and experimental arm, respectively, with significant difference (p = 0.031 for chi-squared test). Response ratio is 1.667 (95% CI 1.035-2.683). Immunohistochemistry shows PD-1 ligand 1 (PD-L1) combined positive score is associated with the synergistic effect. The safety profile is similar between the arms. Adding the PD-1 antibody sintilimab to NACRT significantly increases the CR rate in pMMR LARC, with a manageable safety profile. PD-L1 positivity may help identify patients who might benefit most from the combination therapy.
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Deep learning-based 3D/2D surgical navigation registration techniques achieved excellent results. However, these methods are limited by the occlusion of surgical equipment resulting in poor accuracy. We designed a contrastive learning method that treats occluded and unoccluded X-rays as positive samples, maximizing the similarity between the positive samples and reducing interference from occlusion. The designed registration model has Transformer's residual connection (ResTrans), which enhances the long-sequence mapping capability, combined with the contrast learning strategy, ResTrans can adaptively retrieve the valid features in the global range to ensure the performance in the case of occlusion. Further, a learning-based region of interest (RoI) fine-tuning method is designed to refine the misalignment. We conducted experiments on occluded X-rays that contained different surgical devices. The experiment results show that the mean target registration error (mTRE) of ResTrans is 3.25 mm and the running time is 1.59 s. Compared with the state-of-the-art (SOTA) 3D/2D registration methods, our method offers better performance on occluded 3D/2D registration tasks.
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BACKGROUND: PD-1 blockade is highly efficacious for mismatch repair-deficient colorectal cancer in both metastatic and neoadjuvant settings. We aimed to explore the activity and safety of neoadjuvant therapy with PD-1 blockade plus an angiogenesis inhibitor and the feasibility of organ preservation in patients with locally advanced mismatch repair-deficient colorectal cancer. METHODS: We initiated a single-arm, open-label, phase 2 trial (NEOCAP) at Sun Yat-sen University Cancer Center and the Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China. Patients aged 18-75 years with untreated mismatch repair-deficient or microsatellite instability-high or POLE/POLD1-mutated locally advanced colorectal cancer (cT3 or N+ for rectal cancer, and T3 with invasion ≥5mm or T4, with or without N+ for colon cancer) and an Eastern Cooperative Oncology Group performance score of 0-1 were enrolled and given 200 mg camrelizumab intravenously on day 1 and 250 mg apatinib orally from day 1-14, every 3 weeks for 3 months followed by surgery or 6 months if patients did not have surgery. Patients who had a clinical complete response did not undergo surgery and proceeded with a watch-and-wait approach. The primary endpoint was the proportion of patients with a pathological or clinical complete response. Eligible enrolled patients who received at least one cycle of neoadjuvant treatment and had at least one tumour response assessment following the baseline assessment were included in the activity analysis, and patients who received at least one dose of study drug were included in the safety analysis. The study is registered with ClinicalTrials.gov (NCT04715633) and is ongoing. FINDINGS: Between Sept 29, 2020, and Dec 15, 2022, 53 patients were enrolled; one patient was excluded from the activity analysis because they were found to be mismatch repair-proficient and microsatellite-stable. 23 (44%) patients were female and 29 (56%) were male. The median follow-up was 16·4 (IQR 10·5-23·5) months. 28 (54%; 95% CI 35-68) patients had a clinical complete response and 24 of these patients were managed with a watch-and-wait approach, including 20 patients with colon cancer and multiple primary colorectal cancer. 23 (44%) of 52 patients underwent surgery for the primary tumour, and 14 (61%; 95% CI 39-80) had a pathological complete response. 38 (73%; 95% CI 59-84) of 52 patients had a complete response. Grade 3-5 adverse events occurred in 20 (38%) of 53 patients; the most common were increased aminotransferase (six [11%]), bowel obstruction (four [8%]), and hypertension (four [8%]). Drug-related serious adverse events occurred in six (11%) of 53 patients. One patient died from treatment-related immune-related hepatitis. INTERPRETATION: Neoadjuvant camrelizumab plus apatinib show promising antitumour activity in patients with locally advanced mismatch repair-deficient or microsatellite instability-high colorectal cancer. Immune-related adverse events should be monitored with the utmost vigilance. Organ preservation seems promising not only in patients with rectal cancer, but also in those with colon cancer who have a clinical complete response. Longer follow-up is needed to assess the oncological outcomes of the watch-and-wait approach. FUNDING: The National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, and the Cancer Innovative Research Program of Sun Yat-sen University Cancer Center. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.
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Anticorpos Monoclonais Humanizados , Protocolos de Quimioterapia Combinada Antineoplásica , Neoplasias Colorretais , Reparo de Erro de Pareamento de DNA , Instabilidade de Microssatélites , Terapia Neoadjuvante , Piridinas , Humanos , Pessoa de Meia-Idade , Feminino , Masculino , Terapia Neoadjuvante/efeitos adversos , Neoplasias Colorretais/tratamento farmacológico , Neoplasias Colorretais/genética , Neoplasias Colorretais/patologia , Piridinas/administração & dosagem , Piridinas/efeitos adversos , Piridinas/uso terapêutico , Idoso , Adulto , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Anticorpos Monoclonais Humanizados/administração & dosagem , Anticorpos Monoclonais Humanizados/efeitos adversos , Anticorpos Monoclonais Humanizados/uso terapêutico , Adulto Jovem , Inibidores de Checkpoint Imunológico/efeitos adversos , Inibidores de Checkpoint Imunológico/uso terapêutico , Inibidores de Checkpoint Imunológico/administração & dosagem , AdolescenteRESUMO
BACKGROUND: It remains unclear whether intensification of the chemotherapy backbone in tandem with an anti-EGFR can confer superior clinical outcomes in a cohort of RAS/BRAF wild-type colorectal cancer (CRC) patients with initially unresectable colorectal liver metastases (CRLM). To that end, we sought to comparatively evaluate the efficacy and safety of cetuximab plus FOLFOXIRI (triplet arm) versus cetuximab plus FOLFOX (doublet arm) as a conversion regimen (i.e., unresectable to resectable) in CRC patients with unresectable CRLM. METHODS AND FINDINGS: This open-label, randomized clinical trial was conducted from April 2018 to December 2022 in 7 medical centers across China, enrolling 146 RAS/BRAF wild-type CRC patients with initially unresectable CRLM. A stratified blocked randomization method was utilized to assign patients (1:1) to either the cetuximab plus FOLFOXIRI (n = 72) or cetuximab plus FOLFOX (n = 74) treatment arms. Stratification factors were tumor location (left versus right) and resectability (technically unresectable versus ≥5 metastases). The primary outcome was the objective response rate (ORR). Secondary outcomes included the median depth of tumor response (DpR), early tumor shrinkage (ETS), R0 resection rate, progression-free survival (PFS), overall survival (not mature at the time of analysis), and safety profile. Radiological tumor evaluations were conducted by radiologists blinded to the group allocation. Primary efficacy analyses were conducted based on the intention-to-treat population, while safety analyses were performed on patients who received at least 1 line of chemotherapy. A total of 14 patients (9.6%) were lost to follow-up (9 in the doublet arm and 5 in the triplet arm). The ORR was comparable following adjustment for stratification factors, with 84.7% versus 79.7% in the triplet and doublet arms, respectively (odds ratio [OR] 0.70; 95% confidence intervals [CI] [0.30, 1.67], Chi-square p = 0.42). Moreover, the ETS rate showed no significant difference between the triplet and doublet arms (80.6% (58/72) versus 77.0% (57/74), OR 0.82, 95% CI [0.37, 1.83], Chi-square p = 0.63). Although median DpR was higher in the triplet therapy group (59.6%, interquartile range [IQR], [50.0, 69.7] versus 55.0%, IQR [42.8, 63.8], Mann-Whitney p = 0.039), the R0/R1 resection rate with or without radiofrequency ablation/stereotactic body radiation therapy was comparable with 54.2% (39/72) of patients in the triplet arm versus 52.7% (39/74) in the doublet arm. At a median follow-up of 26.2 months (IQR [12.8, 40.5]), the median PFS was 11.8 months in the triplet arm versus 13.4 months in the doublet arm (hazard ratio [HR] 0.74, 95% CI [0.50, 1.11], Log-rank p = 0.14). Grade ≥ 3 events were reported in 47.2% (35/74) of patients in the doublet arm and 55.9% (38/68) of patients in the triplet arm. The triplet arm was associated with a higher incidence of grade ≥ 3 neutropenia (44.1% versus 27.0%, p = 0.03) and diarrhea (5.9% versus 0%, p = 0.03). The primary limitations of the study encompass the inherent bias in subjective surgical decisions regarding resection feasibility, as well as the lack of a centralized assessment for ORR and resection. CONCLUSIONS: The combination of cetuximab with FOLFOXIRI did not significantly improve ORR compared to cetuximab plus FOLFOX. Despite achieving an enhanced DpR, this improvement did not translate into improved R0 resection rates or PFS. Moreover, the triplet arm was associated with an increase in treatment-related toxicity. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03493048.
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Protocolos de Quimioterapia Combinada Antineoplásica , Camptotecina , Cetuximab , Neoplasias Colorretais , Fluoruracila , Leucovorina , Neoplasias Hepáticas , Compostos Organoplatínicos , Proteínas Proto-Oncogênicas B-raf , Humanos , Cetuximab/administração & dosagem , Cetuximab/uso terapêutico , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Masculino , Pessoa de Meia-Idade , Neoplasias Hepáticas/secundário , Neoplasias Hepáticas/tratamento farmacológico , Feminino , Neoplasias Colorretais/tratamento farmacológico , Neoplasias Colorretais/patologia , Leucovorina/uso terapêutico , Leucovorina/administração & dosagem , Fluoruracila/uso terapêutico , Fluoruracila/administração & dosagem , Compostos Organoplatínicos/uso terapêutico , Compostos Organoplatínicos/administração & dosagem , Proteínas Proto-Oncogênicas B-raf/genética , Idoso , Adulto , Camptotecina/análogos & derivados , Camptotecina/uso terapêutico , Camptotecina/administração & dosagem , Resultado do Tratamento , Proteínas ras/genéticaRESUMO
BACKGROUND: We aimed to compare combined intraoperative chemotherapy and surgical resection with curative surgical resection alone in colorectal cancer patients. METHODS: We performed a multicenter, open-label, randomized, phase III trial. All eligible patients were randomized and assigned to intraoperative chemotherapy and curative surgical resection or curative surgical resection alone (1:1). Survival actualization after long-term follow-up was performed in patients analyzed on an intention-to-treat basis. RESULTS: From January 2011 to January 2016, 696 colorectal cancer patients were enrolled and randomly assigned to intraoperative chemotherapy and radical surgical resection (n=341) or curative surgical resection alone (n=344). Intraoperative chemotherapy with surgical resection showed no significant survival benefit over surgical resection alone in colorectal cancer patients (3-year DFS: 91.1% vs. 90.0%, P=0.328; 3-year OS: 94.4% vs. 95.9%, P=0.756). However, colon cancer patients benefitted from intraoperative chemotherapy, with a relative 4% reduction in liver and peritoneal metastasis (HR=0.336, 95% CI: 0.148-0.759, P=0.015) and a 6.5% improvement in 3-year DFS (HR=0.579, 95% CI: 0.353-0.949, P=0.032). Meanwhile, patients with colon cancer and abnormal pretreatment CEA levels achieved significant survival benefits from intraoperative chemotherapy (DFS: HR=0.464, 95% CI: 0.233-0.921, P=0.029 and OS: (HR=0.476, 95% CI: 0.223-1.017, P=0.049). CONCLUSIONS: Intraoperative chemotherapy showed no significant extra prognostic benefit in total colorectal cancer patients who underwent radical surgical resection; however, in colon cancer patients with abnormal pretreatment serum CEA levels (> 5 ng/ml), intraoperative chemotherapy could improve long-term survival.
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Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple structure and excellent performance, is widely used in medical image segmentation. In this paper, to further improve the performance of U-Net, we propose a channel and space compound attention (CSCA) convolutional neural network, CSCA U-Net in abbreviation, which increases the network depth and employs a double squeeze-and-excitation (DSE) block in the bottleneck layer to enhance feature extraction and obtain more high-level semantic features. Moreover, the characteristics of the proposed method are three-fold: (1) channel and space compound attention (CSCA) block, (2) cross-layer feature fusion (CLFF), and (3) deep supervision (DS). Extensive experiments on several available medical image datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS, CVC-T, 2018 Data Science Bowl (2018 DSB), ISIC 2018, and JSUAH-Cerebellum, show that CSCA U-Net achieves competitive results and significantly improves generalization performance. The codes and trained models are available at https://github.com/xiaolanshu/CSCA-U-Net.
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Ciência de Dados , Aprendizagem , Humanos , Redes Neurais de Computação , Semântica , Processamento de Imagem Assistida por ComputadorRESUMO
By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.
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A BF3·OEt2-catalyzed synthesis of carboranylated dihydropyrrolo[1,2-a]quinoxalines and dihydroindolo[1,2-a]quinoxalines in 30-99% yields is presented through the heterocyclization of various C-modified C-formyl-o-carboranes with 1-(2-aminophenyl)-pyrroles/indoles. A systematic comparative investigation of their oxidation stability in air confirmed that 4-carboranyl-4,5-dihydropyrrolo[1,2-a]quinoxaline had better stability than the 4-phenyl analogue. A cage-deboronation reaction for N-acetyl-substituted carboranylated dihydropyrrolo[1,2-a]quinoxaline produced the corresponding 7,8-nido-carborane cesium salt. A kinetic resolution was also realized to obtain an optically pure carboranylated N-heterocycle scaffold bearing a carborane cage carbon-bonded chiral stereocenter.
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Multiple sclerosis (MS) is a neuroinflammatory disease characterized by CD4[Formula: see text] T cell-mediated immune cell infiltration and demyelination in the central nervous system (CNS). The subtypes of CD4[Formula: see text] T cells are T helper cells 1 (Th1), Th2, Th17, and regulatory T cells (Treg), while three other types of cells besides Th2 play a key role in MS and its classic animal model, experimental autoimmune encephalomyelitis (EAE). Tregs are responsible for immunosuppression, while pathogenic Th1 and Th17 cells cause autoimmune-associated demyelination. Therefore, suppressing Th1 and Th17 cell differentiation and increasing the percentage of Treg cells may contribute to the treatment of EAE/MS. Astragali Radix (AR) is a representative medicine with immunoregulatory, anti-inflammatory, antitumor, and neuroprotective effects.The active ingredients in AR include astragalus flavones, polysaccharides, and saponins. In this study, it was found that the total flavonoids of Astragus (TFA) could effectively treat EAE in mice by ameliorating EAE motor disorders, reducing inflammatory damage and demyelination, inhibiting the proportion of Th17 and Th1 cells, and promoting Tregs differentiation by regulating the JAK/STAT and NF[Formula: see text]B signaling pathways. This novel finding may increase the possibility of using AR or TFA as a drug with immunomodulatory effects for the treatment of autoimmune diseases.
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Encefalomielite Autoimune Experimental , Camundongos , Animais , Encefalomielite Autoimune Experimental/tratamento farmacológico , Encefalomielite Autoimune Experimental/metabolismo , Linfócitos T Reguladores , Flavonoides/farmacologia , Flavonoides/uso terapêutico , Células Th17 , Transdução de Sinais , Células Th1 , Diferenciação Celular , Camundongos Endogâmicos C57BLRESUMO
The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range dependencies are directly neglected in existing CNN fusion approaches, impeding balancing the entire image-level perception for complex scenario fusion. In this paper, therefore, we propose an infrared and visible image fusion algorithm based on the transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. In particular, shallow features extracted by CNN are interacted in the proposed transformer fusion module to refine the fusion relationship within the spatial scope and across channels simultaneously. Besides, adversarial learning is designed in the training process to improve the output discrimination via imposing competitive consistency from the inputs, reflecting the specific characteristics in infrared and visible images. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art, generalising a novel paradigm via transformer and adversarial learning in the fusion task.
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Advanced Siamese visual object tracking architectures are jointly trained using pair-wise input images to perform target classification and bounding box regression. They have achieved promising results in recent benchmarks and competitions. However, the existing methods suffer from two limitations: First, though the Siamese structure can estimate the target state in an instance frame, provided the target appearance does not deviate too much from the template, the detection of the target in an image cannot be guaranteed in the presence of severe appearance variations. Second, despite the classification and regression tasks sharing the same output from the backbone network, their specific modules and loss functions are invariably designed independently, without promoting any interaction. Yet, in a general tracking task, the centre classification and bounding box regression tasks are collaboratively working to estimate the final target location. To address the above issues, it is essential to perform target-agnostic detection so as to promote cross-task interactions in a Siamese-based tracking framework. In this work, we endow a novel network with a target-agnostic object detection module to complement the direct target inference, and to avoid or minimise the misalignment of the key cues of potential template-instance matches. To unify the multi-task learning formulation, we develop a cross-task interaction module to ensure consistent supervision of the classification and regression branches, improving the synergy of different branches. To eliminate potential inconsistencies that may arise within a multi-task architecture, we assign adaptive labels, rather than fixed hard labels, to supervise the network training more effectively. The experimental results obtained on several benchmarks, i.e., OTB100, UAV123, VOT2018, VOT2019, and LaSOT, demonstrate the effectiveness of the advanced target detection module, as well as the cross-task interaction, exhibiting superior tracking performance as compared with the state-of-the-art tracking methods.
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The dictionary pair learning (DPL) model aims to design a synthesis dictionary and an analysis dictionary to accomplish the goal of rapid sample encoding. In this article, we propose a novel structured representation learning algorithm based on the DPL for image classification. It is referred to as discriminative DPL with scale-constrained structured representation (DPL-SCSR). The proposed DPL-SCSR utilizes the binary label matrix of dictionary atoms to project the representation into the corresponding label space of the training samples. By imposing a non-negative constraint, the learned representation adaptively approximates a block-diagonal structure. This innovative transformation is also capable of controlling the scale of the block-diagonal representation by enforcing the sum of within-class coefficients of each sample to 1, which means that the dictionary atoms of each class compete to represent the samples from the same class. This implies that the requirement of similarity preservation is considered from the perspective of the constraint on the sum of coefficients. More importantly, the DPL-SCSR does not need to design a classifier in the representation space as the label matrix of the dictionary can also be used as an efficient linear classifier. Finally, the DPL-SCSR imposes the l2,p -norm on the analysis dictionary to make the process of feature extraction more interpretable. The DPL-SCSR seamlessly incorporates the scale-constrained structured representation learning, within-class similarity preservation of representation, and the linear classifier into one regularization term, which dramatically reduces the complexity of training and parameter tuning. The experimental results on several popular image classification datasets show that our DPL-SCSR can deliver superior performance compared with the state-of-the-art (SOTA) dictionary learning methods. The MATLAB code of this article is available at https://github.com/chenzhe207/DPL-SCSR.
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Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the complementary information conveyed by multiple views of objects to be clustered. Recently, view-shared anchor learning based multi-view clustering methods have been developed to speed up the learning of common data representation. Although widely applied to large-scale scenarios, most of the existing approaches are still faced with two limitations. First, they do not pay sufficient consideration on the negative impact caused by certain noisy views with unclear clustering structures. Second, many of them only focus on the multi-view consistency, yet are incapable of capturing the cross-view diversity. As a result, the learned complementary features may be inaccurate and adversely affect clustering performance. To solve these two challenging issues, we propose a Fast Self-guided Multi-view Subspace Clustering (FSMSC) algorithm which skillfully integrates the view-shared anchor learning and global-guided-local self-guidance learning into a unified model. Such an integration is inspired by the observation that the view with clean clustering structures will play a more crucial role in grouping the clusters when the features of all views are concatenated. Specifically, we first learn a locally-consistent data representation shared by all views in the local learning module, then we learn a globally-discriminative data representation from multi-view concatenated features in the global learning module. Afterwards, a feature selection matrix constrained by the l2,1 -norm is designed to construct a guidance from global learning to local learning. In this way, the multi-view consistent and diverse information can be simultaneously utilized and the negative impact caused by noisy views can be overcame to some extent. Extensive experiments on different datasets demonstrate the effectiveness of our proposed fast self-guided learning model, and its promising performance compared to both, the state-of-the-art non-deep and deep multi-view clustering algorithms. The code of this paper is available at https://github.com/chenzhe207/FSMSC.
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Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion networks is still a black art, rather than science. To address this problem, we formulate the fusion task mathematically, and establish a connection between its optimal solution and the network architecture that can implement it. This approach leads to a novel method proposed in the paper of constructing a lightweight fusion network. It avoids the time-consuming empirical network design by a trial-and-test strategy. In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model. The low-rank representation (LRR) objective is the foundation of our learnable model. The matrix multiplications, which are at the heart of the solution are transformed into convolutional operations, and the iterative process of optimisation is replaced by a special feed-forward network. Based on this novel network architecture, an end-to-end lightweight fusion network is constructed to fuse infrared and visible light images. Its successful training is facilitated by a detail-to-semantic information loss function proposed to preserve the image details and to enhance the salient features of the source images. Our experiments show that the proposed fusion network exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Interestingly, our network requires a fewer training parameters than other existing methods.
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Despite the great interest in carborane-containing molecules, there is a lack of literature on the generation of central chiralities, via catalytic asymmetric transformations using prochiral carboranyl substrates. Herein, we have synthesized novel optically active icosahedral carborane-containing diols via Sharpless catalytic asymmetric dihydroxylation of carborane-derived alkenes, under mild conditions. The reaction showed a good substrate scope with 74-94% yields and 92->99% ee. This synthetic approach facilitated the creation of two adjacent stereocenters respectively located at the α,ß-position of o-carborane cage carbon, with a single syn-diastereoisomer. In addition, the obtained chiral carborane-containing diol product can be transformed to cyclic sulfate and can subsequently undergo a nucleophilic substitution and reduction to obtain the unexpected nido-carboranyl derivatives of chiral amino alcohols in the form of zwitterions.
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With the development of neural networking techniques, several architectures for symmetric positive definite (SPD) matrix learning have recently been put forward in the computer vision and pattern recognition (CV&PR) community for mining fine-grained geometric features. However, the degradation of structural information during multi-stage feature transformation limits their capacity. To cope with this issue, this paper develops a U-shaped neural network on the SPD manifolds (U-SPDNet) for visual classification. The designed U-SPDNet contains two subsystems, one of which is a shrinking path (encoder) making up of a prevailing SPD manifold neural network (SPDNet (Huang and Van Gool, 2017)) for capturing compact representations from the input data. Another is a constructed symmetric expanding path (decoder) to upsample the encoded features, trained by a reconstruction error term. With this design, the degradation problem will be gradually alleviated during training. To enhance the representational capacity of U-SPDNet, we also append skip connections from encoder to decoder, realized by manifold-valued geometric operations, namely Riemannian barycenter and Riemannian optimization. On the MDSD, Virus, FPHA, and UAV-Human datasets, the accuracy achieved by our method is respectively 6.92%, 8.67%, 1.57%, and 1.08% higher than SPDNet, certifying its effectiveness.
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Algoritmos , Redes Neurais de Computação , Humanos , Inteligência ArtificialRESUMO
AIM: To investigate the treatment pattern and safety of tafluprost for glaucoma and ocular hypertension (OH) in clinical practice in China. METHODS: This post-marketing observational study included patients who received tafluprost to lower intraocular pressure (IOP) within 30d between September 2017 and March 2020 in 20 hospitals in China. Adverse drug reactions (ADRs) during tafluprost treatment and within 30d after the treatment were collected. RESULTS: A total of 2544 patients were included in this study, of them 58.5% (1488/2544) had primary open angle glaucoma (POAG), 21.9% (556/2544) had OH and 19.7% (500/2544) used tafluprost for other reasons. Of 359 ADRs occurred in 10.1% (258/2544) patients, and no serious adverse event occurred. The most common ADR was conjunctival hyperemia (128 ADRs in 124 patients, 4.9%). Totally 1670 participants (65.6%) combined tafluprost with carbonic anhydrase inhibitors (CAIs; 37.1%, 620/1670), sympathomimetics (33.5%, 559/1670), ß-blockers (33.2%, 555/1670), other prostaglandin analogs (PGAs; 15.6%, 260/1670) and other eye drops (15.1%, 253/1670). The highest incidence of conjunctival hyperemia was noted in patients who received tafluprost in combination with other PGAs (23 ADRs in 23 patients, 8.8%, 23/260) and the lowest was in combination with CAIs (16 ADRs in 16 patients, 2.6%, 16/620). Tafluprost was applied in primary angle-closure glaucoma (41.6%, 208/500), after glaucoma surgery (17.8%, 89/500) and after non-glaucoma surgery (15.8%, 79/500). CONCLUSION: Tafluprost is safe for POAG and OH, and tolerable when combined with other eye drops and under various clinical circumstances.
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OBJECTIVE: To compare neoadjuvant chemotherapy (nCT) with CAPOX alone versus neoadjuvant chemoradiotherapy (nCRT) with capecitabine in locally advanced rectal cancer (LARC) with uninvolved mesorectal fascia (MRF). BACKGROUND DATA: nCRT is associated with higher surgical complications, worse long-term functional outcomes, and questionable survival benefits. Comparatively, nCT alone seems a promising alternative treatment in lower-risk LARC patients with uninvolved MRF. METHODS: Patients between June 2014 and October 2020 with LARC within 12 cm from the anal verge and uninvolved MRF were randomly assigned to nCT group with 4 cycles of CAPOX (Oxaliplatin 130 mg/m2 IV day 1 and Capecitabine 1000 mg/m2 twice daily for 14 d. Repeat every 3 wk) or nCRT group with Capecitabine 825 mg/m² twice daily administered orally and concurrently with radiation therapy (50 Gy/25 fractions) for 5 days per week. The primary end point is local-regional recurrence-free survival. Here we reported the results of secondary end points: histopathologic response, surgical events, and toxicity. RESULTS: Of the 663 initially enrolled patients, 589 received the allocated treatment (nCT, n=300; nCRT, n=289). Pathologic complete response rate was 11.0% (95% CI, 7.8-15.3%) in the nCT arm and 13.8% (95% CI, 10.1-18.5%) in the nCRT arm ( P =0.33). The downstaging (ypStage 0 to 1) rate was 40.8% (95% CI, 35.1-46.7%) in the nCT arm and 45.6% (95% CI, 39.7-51.7%) in the nCRT arm ( P =0.27). nCT was associated with lower perioperative distant metastases rate (0.7% vs. 3.1%, P =0.03) and preventive ileostomy rate (52.2% vs. 63.6%, P =0.008) compared with nCRT. Four patients in the nCT arm received salvage nCRT because of local disease progression after nCT. Two patients in the nCT arm and 5 in the nCRT arm achieved complete clinical response and were treated with a nonsurgical approach. Similar results were observed in subgroup analysis. CONCLUSIONS: nCT achieved similar pCR and downstaging rates with lower incidence of perioperative distant metastasis and preventive ileostomy compared with nCRT. CAPOX could be an effective alternative to neoadjuvant therapy in LARC with uninvolved MRF. Long-term follow-up is needed to confirm these results.
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Terapia Neoadjuvante , Neoplasias Retais , Humanos , Terapia Neoadjuvante/métodos , Resultado do Tratamento , Capecitabina/uso terapêutico , Neoplasias Retais/patologia , Quimiorradioterapia/métodos , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Estadiamento de NeoplasiasRESUMO
Metabolic disorder is highly related to obesity, insulin resistance, hypertension, and hyperlipidemia. The present study found that astragaloside IV (ASI) attenuated metabolic disorder related symptoms and modulated hepatic lipid metabolism associated gene mRNA expression in db/db mice. ASI inhibited rosiglitazone-induced adipocyte differentiation of 3T3-L1 cells, and lipid accumulation in palmitic acid (PA)-induced HepG2 cells with down-regulated mRNA expression of lipogenesis-related genes. In addition, it was predicted to bind to the ligand binding domain (LBD) of PPARγ and inhibit its transactivity. Collectively, our study suggested that ASI improves lipid metabolism in obese mice probably through suppressing PPARγ activity.
Assuntos
Obesidade , PPAR gama , Camundongos , Animais , PPAR gama/genética , PPAR gama/metabolismo , Camundongos Obesos , Obesidade/tratamento farmacológico , Obesidade/metabolismo , RNA Mensageiro , Células 3T3-L1 , Camundongos Endogâmicos C57BLRESUMO
Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.