Image retrieval based on colour and improved NMI texture features

This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion

Building a strong pharmaceutical system for China

The world’s most populous country is facing a double healthcare crunch with a rapidly aging population and an explosion in the rate of non-communicable diseases such as diabetes, heart disease, and lung disease. Addressing these diseases will require a robust pharmaceutical system that is able to produce quality, effective, and affordable medicines

Combined early palliative care for non-small-cell lung cancer patients: a randomized controlled trial in Chongqing, China

PurposeMore effective approaches are needed to improve the prognosis of non-small-cell lung cancer (NSCLC) patients. Thus, we used the E-warm model to assess how early integration of interdisciplinary palliative care was related to the quality of life (QoL), psychological functioning, pain management, and nutrition factors of NSCLC patients.MethodsThis randomized controlled trial enrolled 280 newly diagnosed NSCLC patients, which were randomly divided (1:1) into combined early palliative care (CEPC) and standard oncological care (SC) groups. At baseline and after 24 weeks, the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale, Hospital Anxiety and Depression Scale (HADS), and the Patient Health Questionnaire-9 (PHQ-9) were used to assess QoL and psychological function, respectively. The Numerical Rating Scale (NRS) and Patient-Generated Subjective Global Assessment (PG-SGA) were used to assess cancer patients’ pain and nutrition levels. The primary outcome was overall survival (OS). Secondary outcomes comprised changes in the QoL, psychological functioning, pain, and nutrition state. The intention-to-treat method was applied for analysis. This study was registered at www.chictr.org.cn (ChiCTR2200062617).ResultsOf the 140 patients enrolled in the CEPC and SC groups, 102 and 82 completed the research. The CEPC group presented higher QoL than the SC group (p < 0.05). Additionally, fewer patients presented depressive symptoms in the CEPC group than in the SC group (p < 0.05), as well as better nutritional status (p = 0.007) and pain management (p = 0.003). Compared to the SC group, CEPC patients had significantly longer OS (20.4 vs. 24.6 months, p = 0.042; HR: 0.19; 95% CI: 0.04-0.85, p = 0.029).ConclusionWith combined early palliative care, NSCLC patients lived longer, had better QoL, were psychologically stable, were in less pain, and were more nutritionally satisfied

JK5G postbiotics attenuate immune-related adverse events in NSCLC patients by regulating gut microbiota: a randomized controlled trial in China

ScopeThis study aimed to evaluate the effects of JK5G postbiotics to regulate imbalanced gut microbiota and its impacts on the efficacy and incidence rate of immune-related adverse events (irAEs) in non-small-cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).MethodsThis randomized, double-blind, placebo-controlled trial was conducted in China and included non-squamous or squamous NSCLC patients without EGFR, ROS1, and ALK alteration, treatment-naive, and stage IIIb-IV. Patients were randomly (1:1) divided into two groups to receive four cycles (three weeks for each cycle) of programmed cell death-1 (PD-1) plus chemotherapy plus placebo (control group, n = 30) or to receive PD-1 plus chemotherapy plus JK5G postbiotics (JK5G group, n = 30). The primary endpoint was objective response rate. The secondary endpoints were quality of life (QoL), adverse effects, and the 16S DNA sequencing of gut microbiota, blood inflammatory cytokines, and lymphocyte subsets. This study was registered at www.chictr.org.cn (ChiCTR2200064690).ResultsSixty patients were enrolled. The objective response rate was 36.67% (11/30) in the control group and 50.00% (15/30) in the JK5G group (p = 0.297). The JK5G group had better QoL and nutritional levels, as well as lower depression symptoms than the control group (all p < 0.05). Moreover, the JK5G group had a lower incidence of anemia (63.33% vs. 13.33%, p < 0.001), decreased lymphocyte count (20.00% vs. 0%, p = 0.010), decreased appetite (53.33% vs. 16.67%, p = 0.003), nausea (33.33% vs. 6.67%, p = 0.010), and asthenia (30.00% vs. 6.67%, p = 0.017) than the control group. Moreover, JK5G attenuated gut microbiota imbalance, accompanied by increased Faecalibacterium, Ruminococcaceae, and fecal butyrate concentration, and diminished Escherichia-Shigella. Furthermore, JK5G administration significantly decreased the levels of pro-inflammatory markers, including TNF-α, IL-2, and C-reactive protein (CRP) (all p < 0.05). Significant increases in CD3+CD4+ T cells and CD4/CD8 ratio were observed in the peripheral blood of JK5G group patients (all p < 0.05). The enterotype data showed that patients were clustered into Blautia (E1) and Escherichia-Shigella (E2) enterotypes, and JK5G postbiotics intervention might be related to enterotype modulations.ConclusionOur current findings indicated that JK5G postbiotics might attenuate irAEs, and enhance the QoL and nutrition levels of advanced NSCLC patients who received ICIs. JK5G postbiotics could also improve the gut microbiota structures and ameliorate the tumor microenvironment and inflammation.Clinical trial registrationwww.chictr.org.cn, identifier ChiCTR2200064690

Image Super-Resolution via Dual-Level Recurrent Residual Networks

Recently, the feedforward architecture of a super-resolution network based on deep learning was proposed to learn the representation of a low-resolution (LR) input and the non-linear mapping from these inputs to a high-resolution (HR) output, but this method cannot completely solve the interdependence between LR and HR images. In this paper, we retain the feedforward architecture and introduce residuals to a dual-level; therefore, we propose the dual-level recurrent residual network (DLRRN) to generate an HR image with rich details and satisfactory vision. Compared with feedforward networks that operate at a fixed spatial resolution, the dual-level recurrent residual block (DLRRB) in DLRRN utilizes both LR and HR space information. The circular signals in DLRRB enhance spatial details by the mutual guidance between two directions (LR to HR and HR to LR). Specifically, the LR information of the current layer is generated by the HR and LR information of the previous layer. Then, the HR information of the previous layer and LR information of the current layer jointly generate the HR information of the current layer, and so on. The proposed DLRRN has a strong ability for early reconstruction and can gradually restore the final high-resolution image. An extensive quantitative and qualitative evaluation of the benchmark dataset was carried out, and the experimental results proved that our network achieved good results in terms of network parameters, visual effects and objective performance metrics

Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks