Search

Objective To explore the therapeutic effect of "jian pi yi qi yang xue zhi tong" for treating bone metastasis of breast cancer and the clinical effect of improving pain symptoms. Methods A total of 80 patients with bone metastases from breast cancer were admitted. They were randomly divided into an observation group and a control group according to the random-number-table method. The control group was treated with zoledronic acid, whereas the observation group was treated with jian pi yi qi yang xue zhi tong prescriptions based on the control group. We compared the differences in the effects of different treatment plans on patients' pain symptoms, physical condition, and quality of life, as well as TNF-α, IL-6, and CRP levels. Results No significant difference was found in pain scores, physical condition scores, sleep quality scores, and quality of life scores, as well as CRP, IL-6, and TNF-α levels between the two groups of patients before treatment (all P > 0.05). At one, two, and four months after treatment, the pain scores of both groups of patients decreased, with the observation group having lower scores than the control group (P < 0.05). The total pain-relief rate of the observation group was higher than that of the control group (P < 0.05). After treatment, the sleep quality scores and the levels of CRP, IL-6, and TNF-α decreased, with the observation group having lower values than the control group (P < 0.05). The physical condition scores and the quality of life scores of both groups improved, with the observation group having higher values than the control group (P < 0.05). Conclusion In patients with bone metastases from breast cancer, oral treatment with jian pi yi qi yang xue zhi tong prescription has a significant effect. It substantially improves the pain symptoms, enhances the quality of sleep and life of patients, and reduces the levels of CRP, IL-6, and TNF-α.

Objective To understand the psychological status among nurses during the closed-off management for prevention and control of COVID-19, and provide references for improving quality of management in hemodialysis center during the period of closed-off management. Methods Semi-structure interview were carried out among nurses from a certain hospital of Shanghai which conducted the closed-off management for prevention and control of COVID-19. The date were analyzed by thematic analysis method and then extracted the themes. Results Two themes were extracted: thought transformation and fluctuation of emotion, coping style. Conclusion Nurses are vulnerable to developing negative emotions when facing the epidemic situation. Efforts should be made to improve coping mechanisms, provide emotional guidance and build nurses’ confidence in overcoming the epidemic. (目的 了解新型冠状病毒肺炎疫情防控医院闭环管理期间血透室护理人员的心理状态, 为进一步完善闭环管理期间血透室管理模式提供参考意见。方法 对上海市某一所因新型冠状病毒肺炎疫情防控采取闭环管理医院的血透室护理人员进行半结构式访谈, 采用主题分析法对资料进行整理分析, 生成主题。结果 最终析出主题两个: 思想情绪波动及应对方式。结论 面对疫情, 护理人员易产生多种负面情绪。因此, 护理人员及管理者应完善应对机制, 做好情绪疏导, 树立战胜疫情的信心。)

Clustering algorithms have the characteristics of being simple and efficient and can complete calculations without a large number of datasets, making them suitable for application in noise reduction processing for audio module mass production testing. In order to solve the problems of the NMF algorithm easily getting stuck in local optimal solutions and difficult feature signal extraction, an improved NMF audio denoising algorithm combined with K-means initialization was designed. Firstly, the Euclidean distance formula of K-means has been improved to extract audio signal features from multiple dimensions. Combined with the initialization strategy of K-means decomposition, the initialization dictionary matrix of the NMF algorithm has been optimized to avoid getting stuck in local optimal solutions and effectively improve the robustness of the algorithm. Secondly, in the sparse coding part of the NMF algorithm, feature extraction expressions are added to solve the problem of noise residue and partial spectral signal loss in audio signals during the operation process. At the same time, the size of the coefficient matrix is limited to reduce operation time and improve the accuracy of feature extraction in high-precision audio signals. Then, comparative experiments were conducted using the NOIZEUS and NOISEX-92 datasets, as well as random noise audio signals. This algorithm improved the signal-to-noise ratio by 10–20 dB and reduced harmonic distortion by approximately −10 dB. Finally, a high-precision audio acquisition unit based on FPGA was designed, and practical applications have shown that it can effectively improve the signal-to-noise ratio of audio signals and reduce harmonic distortion. [ABSTRACT FROM AUTHOR]

In view of the complexity and diversity of hyperspectral images (HSIs), the classification task has been a major challenge in the field of remote sensing image processing. Hyperspectral classification (HSIC) methods based on neural architecture search (NAS) is a current attractive frontier that not only automatically searches for neural network architectures best suited to the characteristics of HSI data, but also avoids the possible limitations of manual design of neural networks when dealing with new classification tasks. However, the existing NAS-based HSIC methods have the following limitations: (1) the search space lacks efficient convolution operators that can fully extract discriminative spatial–spectral features, and (2) NAS based on traditional differentiable architecture search (DARTS) has performance collapse caused by unfair competition. To overcome these limitations, we proposed a neural architecture search method with receptive field spatial–spectral attention (RFSS-NAS), which is specifically designed to automatically search the optimal architecture for HSIC. Considering the core needs of the model in extracting more discriminative spatial–spectral features, we designed a novel and efficient attention search space. The core component of this innovative space is the receptive field spatial–spectral attention convolution operator, which is capable of precisely focusing on the critical information in the image, thus greatly enhancing the quality of feature extraction. Meanwhile, for the purpose of solving the unfair competition issue in the traditional differentiable architecture search (DARTS) strategy, we skillfully introduce the Noisy-DARTS strategy. The strategy ensures the fairness and efficiency of the search process and effectively avoids the risk of performance crash. In addition, to further improve the robustness of the model and ability to recognize difficult-to-classify samples, we proposed a fusion loss function by combining the advantages of the label smoothing loss and the polynomial expansion perspective loss function, which not only smooths the label distribution and reduces the risk of overfitting, but also effectively handles those difficult-to-classify samples, thus improving the overall classification accuracy. Experiments on three public datasets fully validate the superior performance of RFSS-NAS. [ABSTRACT FROM AUTHOR]

Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P

Medical 3D printing holds the potential of transforming personalized medicine by enabling the fabrication of patient-specific implants, reimagining prostheses, developing surgical guides to expedite and transform surgical interventions, and enabling a growing multitude of specialized applications. In order to realize this tremendous potential in frontline medicine, an understanding of the basic principles of 3D printing by the medical professionals is required. This primer underlines the basic approaches and tools in 3D printing, starting from patient anatomy acquired through cross-sectional imaging, in this case Computed Tomography (CT). We describe the basic principles using the relatively simple task of separation of the relevant anatomy to guide aneurysm repair. This is followed by exploration of more advanced techniques in the creation of patient-specific surgical guides and prostheses for a patient with extensive pleomorphic sarcoma using Computer Aided Design (CAD) software.

Although the collaborative use of hyperspectral images (HSIs) and LiDAR data in land cover classification tasks has demonstrated significant importance and potential, several challenges remain. Notably, the heterogeneity in cross-modal information integration presents a major obstacle. Furthermore, most existing research relies heavily on category names, neglecting the rich contextual information from language descriptions. Visual-language pretraining (VLP) has achieved notable success in image recognition within natural domains by using multimodal information to enhance training efficiency and effectiveness. VLP has also shown great potential for land cover classification in remote sensing. This paper introduces a dual-sensor multimodal semantic collaborative classification network (DSMSC2N). It uses large language models (LLMs) in an instruction-driven manner to generate land cover category descriptions enriched with domain-specific knowledge in remote sensing. This approach aims to guide the model to accurately focus on and extract key features. Simultaneously, we integrate and optimize the complementary relationship between HSI and LiDAR data, enhancing the separability of land cover categories and improving classification accuracy. We conduct comprehensive experiments on benchmark datasets like Houston 2013, Trento, and MUUFL Gulfport, validating DSMSC2N's effectiveness compared to various baseline methods. [ABSTRACT FROM AUTHOR]

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model's detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model's mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection. [ABSTRACT FROM AUTHOR]

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A "sandwich" capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays' benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. [ABSTRACT FROM AUTHOR]