Effect of skinfold thickness on arm venous access port in cancer patients.

BACKGROUND: Currently, skinfold thickness in studies on arm venous access ports and the effect of venous access port application are unknown. MATERIALS AND METHODS: A total of 256 cancer patients who underwent primary venous access port placement in the Fourth Hospital of Hebei Medical University from September 2022 to March 2023 were selected as the study subjects. Two hundred fifty-six patients were divided into normal skinfold thickness group and high skinfold thickness group according to skinfold thickness. The success rate of primary catheterization of arm venous port catheterization, catheterization operation time, catheterization length and incidence rate of adverse reactions were compared. RESULTS: There was no significant difference in the basic data between the two groups. There was no significant difference in the success rate of primary catheterization between the two groups (p > 0.05), the catheterization operation time in the normal skinfold thickness group was significantly lower than that in the high skinfold thickness group (p < 0.05), the total length of the implanted catheter in the normal skinfold thickness group was significantly lower than that in the high skinfold thickness group (p < 0.05), and the incidence of adverse reactions in the normal skinfold thickness group was significantly lower than that in the high skinfold thickness group (p < 0.05). CONCLUSION: In cancer patients, skinfold thickness can significantly affect the application effect of arm venous port, and normal skinfold thickness for arm venous port has shorter operation time, total length of implanted catheter and lower incidence of adverse reactions.

Mucosa-associated lymphoid tissue lymphoma of the trachea treated with radiotherapy: A case report.

BACKGROUND: Mucosa-associated lymphoid tissue (MALT) lymphoma originates in the marginal zone of lymphoid tissue. lung is one of the most frequent non-gastrointestinal organs involved, here known as bronchus-associated lymphoid tissue (BALT) lymphoma. BALT lymphoma of unknown etiology, and most patients are asymptomatic. The treatment of BALT lymphoma is controversial. CASE SUMMARY: A 55-year-old man admitted to hospital had a three-month history of progressively coughing up yellow sputum, chest stuffiness, and shortness of breath. Fiberoptic bronchoscopy revealed mucosal visible beaded bumps 4 cm from the tracheal carina at 9 o 'clock and 3 o 'clock, the right main bronchus, and the right upper lobe bronchus. Biopsy specimens showed MALT lymphoma. Computed tomography virtual bronchoscopy (CTVB) showed uneven main bronchial wall thickening and multiple nodular protrusion. BALT lymphoma stage IE was diagnosed after a staging examination. We treated the patient with radiotherapy (RT) alone. A total dose of 30.6 Gy/17 f/25 d was given. The patient had no obvious adverse reactions during RT. The CTVB was repeated after RT and showed that the right side of the trachea was slightly thickened. CTVB was repeated 1.5 mo after RT and again showed that the right side of the trachea was slightly thickened. Annual CTVB showed no signs of recurrence. The patient now has no symptoms. CONCLUSION: BALT lymphoma is an uncommon disease and shows good prognosis. The treatment of BALT lymphoma is controversial. In recent years, less invasive diagnostic and therapeutic approaches have been emerging. RT was effective and safe in our case. The use of CTVB could provide a noninvasive, repeatable, and accurate method in diagnosis and follow-up.

Clinical Application Value of Multimedia Education and Nursing Intervention in a Coronary Computed Tomography Angiography.

To explore the application value of multimedia education and nursing intervention in a coronary computed tomography angiography (CCTA). A total of 120 patients who underwent a 256-slice spiral CCTA examination in our hospital from April 2019 to April 2020 were selected. Patients were divided into two groups of 60 patients each, that is, the control group and the observation group, using a random number table method. The control group received traditional education before an examination, and patients were given routine breathing training. The observation group was given multimedia education and nursing intervention. The heart rate (HR), diastolic blood pressure (DBP), systolic blood pressure (SBP), and respiratory rate in the two groups were observed. The psychological status, imaging quality, and incidence of adverse reactions were compared between the two groups. The HR, DBP, SBP, and respiratory rate of the observation group were all lower compared to those in control group, and the differences were statistically significant (p < 0.05). Following multimedia education and nursing intervention, the anxiety and depression scores of patients in the observation group were considerably lower compared with those in the control group, and the differences were statistically significant (p < 0.05). Observation group image quality I level higher than the control group, and the proportion of patients with the difference was statistically significant (p < 0.05). The proportion of grade II to IV patients was lower in the observation group than in the control group; however, the difference was not statistically significant (p > 0.05). There was a significant difference in the incidence of adverse reactions between the two groups (χ2, p = .031). For patients undergoing a CCTA examination, multimedia education and nursing intervention can effectively improve their immediate psychological state, control their heart rate, and blood pressure before the examination, reduce the incidence of adverse reactions and improve imaging quality, thereby improving the overall reliability of a clinical diagnosis.

Evaluation of an Access-Risk-Knowledge (ARK) Platform for Governance of Risk and Change in Complex Socio-Technical Systems.

Three key challenges to a whole-system approach to process improvement in health systems are the complexity of socio-technical activity, the capacity to change purposefully, and the consequent capacity to proactively manage and govern the system. The literature on healthcare improvement demonstrates the persistence of these problems. In this project, the Access-Risk-Knowledge (ARK) Platform, which supports the implementation of improvement projects, was deployed across three healthcare organisations to address risk management for the prevention and control of healthcare-associated infections (HCAIs). In each organisation, quality and safety experts initiated an ARK project and participated in a follow-up survey and focus group. The platform was then evaluated against a set of fifteen needs related to complex system transformation. While the results highlighted concerns about the platform's usability, feedback was generally positive regarding its effectiveness and potential value in supporting HCAI risk management. The ARK Platform addresses the majority of identified needs for system transformation; other needs were validated in the trial or are undergoing development. This trial provided a starting point for a knowledge-based solution to enhance organisational governance and develop shared knowledge through a Community of Practice that will contribute to sustaining and generalising that change.

The effects of the renin inhibitor aliskiren on neuronal toxic damage.

The neurotoxicity of amyloid-ß (Aß) and its deposition in neurons plays a critical role in the occurrence and development of Alzheimer's disease (AD). Several preclinical experiments have found that the renin inhibitor aliskiren has a wide range of physiological effects, including hindering the progression of atherosclerosis and anti-inflammatory. This study is aimed to explore the effect of aliskiren on neuronal toxic damage and the underlying mechanism. This study established an in vitro nerve injury model through Aß 1-42 induction; the effects of aliskiren on the viability, inflammatory damage and apoptosis of SH-SY5Y cells were examined. For the sake of explore the underlying mechanism; SwissTargetPrediction website and molecular docking were utilized to predict the target of aliskiren. Then the impacts of the target protein overexpression were determined to verify its mediation. The results of the current study demonstrate that aliskiren has no effect on the viability of SH-SY5Y cells while Aß1-42accumulation could significantly downregulate cell viability. In addition, aliskiren could alleviate neuronal inflammatory damage and apoptosis arise from Aß 1-42accumulation. Following confirming the high expression level of the predicted target PDE4B in damaged cells, it was found that PDE4B overexpression can reverse the influence of aliskiren on cell viability, inflammatory damage and apoptosis. In conclusion, aliskiren upregulates cell viability, reduces inflammatory damage and apoptosis induced by Aß accumulation in AD via inhibiting PDE4B. These findings have expanded the scope of future application of aliskiren and provided a theoretical basis.

Robust Ellipse Fitting With Laplacian Kernel Based Maximum Correntropy Criterion.

The performance of ellipse fitting may significantly degrade in the presence of outliers, which can be caused by occlusion of the object, mirror reflection or other objects in the process of edge detection. In this paper, we propose an ellipse fitting method that is robust against the outliers, and thus maintaining stable performance when outliers can be present. We formulate an optimization problem for ellipse fitting based on the maximum entropy criterion (MCC), having the Laplacian as the kernel function from the well-known fact that the l1 -norm error measure is robust to outliers. The optimization problem is highly nonlinear and non-convex, and thus is very difficult to solve. To handle this difficulty, we divide it into two subproblems and solve the two subproblems in an alternate manner through iterations. The first subproblem has a closed-form solution and the second one is cast as a convex second-order cone program (SOCP) that can reach the global solution. By so doing, the alternate iterations always converge to an optimal solution, although it can be local instead of global. Furthermore, we propose a procedure to identify failed fitting of the algorithm caused by local convergence to a wrong solution, and thus, it reduces the probability of fitting failure by restarting the algorithm at a different initialization. The proposed robust ellipse fitting method is next extended to the coupled ellipses fitting problem. Both simulated and real data verify the superior performance of the proposed ellipse fitting method over the existing methods.

[Increased expressions of programmed death 1 (PD-1) and its ligands in peripheral CD3(+) T cells and CD19(+) B cells in patients with hepatocellular carcinoma].

Objective To investigate the changes of programmed death 1 (PD-1) and ligands, as well as interferon-γ (IFN-γ) in peripheral blood mononuclear cells (PBMCs) of patients with hepatocellular carcinoma (HCC). Methods The peripheral blood was collected from 15 early HCC patients, 13 progressive HCC patients and 12 healthy volunteers. PBMCs was isolated from the peripheral blood. The expressions of PD-1, PD-L1 and PD-L2 in PBMCs were detected by flow cytometry; the serum level of IFN-γ was determined by ELISA; the correlation of PD-1 and IFN-γ was analyzed with Pearson's correlation and One-way ANOVA. Results The expression levels of PD-1, PD-L1 and PD-L2 in CD3(+) T cells and CD19(+) B cells and serum IFN-γ level in progressive HCC patients were significantly higher than those in the healthy group and early HCC patients. The expression levels of PD-1, PD-L1 and PD-L2 in the CD3(+) T cells and CD19(+) B cells of the HCC patients were positively correlated with IFN-γ. Conclusion The expression levels of PD-1, PD-L1 and PD-L2 increase in the PBMCs of HCC patients; PD-1 and PD-L1 are correlated with IFN-γ level.

Decentralized Dimensionality Reduction for Distributed Tensor Data Across Sensor Networks.

This paper develops a novel decentralized dimensionality reduction algorithm for the distributed tensor data across sensor networks. The main contributions of this paper are as follows. First, conventional centralized methods, which utilize entire data to simultaneously determine all the vectors of the projection matrix along each tensor mode, are not suitable for the network environment. Here, we relax the simultaneous processing manner into the one-vector-by-one-vector (OVBOV) manner, i.e., determining the projection vectors (PVs) related to each tensor mode one by one. Second, we prove that in the OVBOV manner each PV can be determined without modifying any tensor data, which simplifies corresponding computations. Third, we cast the decentralized PV determination problem as a set of subproblems with consensus constraints, so that it can be solved in the network environment only by local computations and information communications among neighboring nodes. Fourth, we introduce the null space and transform the PV determination problem with complex orthogonality constraints into an equivalent hidden convex one without any orthogonality constraint, which can be solved by the Lagrange multiplier method. Finally, experimental results are given to show that the proposed algorithm is an effective dimensionality reduction scheme for the distributed tensor data across the sensor networks.

Robust Ellipse Fitting via Half-Quadratic and Semidefinite Relaxation Optimization.

Ellipse fitting is widely applied in the fields of computer vision and automatic manufacture. However, the introduced edge point errors (especially outliers) from image edge detection will cause severe performance degradation of the subsequent ellipse fitting procedure. To alleviate the influence of outliers, we develop a robust ellipse fitting method in this paper. The main contributions of this paper are as follows. First, to be robust against the outliers, we introduce the maximum correntropy criterion into the constrained least-square (CLS) ellipse fitting method, and apply the half-quadratic optimization algorithm to solve the nonlinear and nonconvex problem in an alternate manner. Second, to ensure that the obtained solution is related to an ellipse, we introduce a special quadratic equality constraint into the aforementioned CLS model, which results in the nonconvex quadratically constrained quadratic programming problem. Finally, we derive the semidefinite relaxation version of the aforementioned problem in terms of the trace operator and thus determine the ellipse parameters using semidefinite programming. Some simulated and experimental examples are presented to illustrate the effectiveness of the proposed ellipse fitting approach.

Salvage radiotherapy in patients with local recurrent esophageal cancer after radical radiochemotherapy.

PURPOSE: The aim of this study was to evaluate the salvage radiotherapy outcome in patients with local recurrent esophageal cancer after radical radiochemotherapy (RCT). METHODS: A total of 114 patients with local recurrent esophageal squamous cell carcinoma after initial radical RCT were retrospectively analyzed. Fifty-five (55) patients belonged to the salvage radiotherapy group (SR group) and 59 patients to the non-salvage radiotherapy group (NSR group). RESULTS: The median survival time after-recurrence was 4 months in all patients. The 1, 2, 3 year overall survival (OS) rates were 83.6%, 41.8% and 21.8% respectively in the SR group, and 57.6%, 16.9%, and 8.5% in the NSR group. The 6-month and 1-year survival rates after-recurrence were 41.8% and 16.4% respectively in the SR group, and 11.9% and 3.4% respectively in the NSR group. A salvage radiation dose > 50 Gy after initial radical RCT, improved the survival of patients with local recurrent esophageal cancer. Three patients (5.45%) from the SR group showed more than 3-grade radiation pneumonitis. In addition, esophageal fistula/perforation was observed in 11 cases (20.0%) in the SR group and in 8 cases (13.6%) in the NSR group. CONCLUSIONS: Salvage treatment after definitive RCT may improve the overall survival and survival after-recurrence of patients with local recurrent esophageal cancer.

Effects of broth composition and light condition on antimicrobial susceptibility testing of ionic silver.

In this study, the minimum inhibitory concentrations (MICs) of AgNO3 against bacteria were investigated in a variety of microorganism culture broths. Broth- and light-dependent MIC values were observed and correlated negatively with nano-Ag speciation development. We advocate here the importance of broth and light standardization in Ag antimicrobial test.

Asymmetric double-image encryption based on cascaded discrete fractional random transform and logistic maps.

A double-image encryption is proposed based on the discrete fractional random transform and logistic maps. First, an enlarged image is composited from two original images and scrambled in the confusion process which consists of a number of rounds. In each round, the pixel positions of the enlarged image are relocated by using cat maps which are generated based on two logistic maps. Then the scrambled enlarged image is decomposed into two components. Second, one of two components is directly separated into two phase masks and the other component is used to derive the ciphertext image with stationary white noise distribution by using the cascaded discrete fractional random transforms generated based on the logistic map. The cryptosystem is asymmetric and has high resistance against to the potential attacks such as chosen plaintext attack, in which the initial values of logistic maps and the fractional orders are considered as the encryption keys while two decryption keys are produced in the encryption process and directly related to the original images. Simulation results and security analysis verify the feasibility and effectiveness of the proposed encryption scheme.

Treating advanced non-small-cell lung cancer in Chinese patients: focus on icotinib.

Icotinib hydrochloride is an orally administered small-molecule reversible tyrosine kinase inhibitor that has been independently researched and developed and has independent intellectual property rights in the People's Republic of China. Clinical trials have demonstrated that the response to icotinib among advanced non-small-cell lung cancer (NSCLC) patients who received at least one platinum-based chemotherapy regimen was not inferior to gefitinib. Since being launched August 2011 in the People's Republic of China, icotinib has been widely used in clinics, and has become an important treatment option for Chinese patients with advanced NSCLC. The present study presents the Phase I, II, and III clinical trials of icotinib and discusses current clinical applications in the People's Republic of China and future research directions.

Distributed dictionary learning for sparse representation in sensor networks.

This paper develops a distributed dictionary learning algorithm for sparse representation of the data distributed across nodes of sensor networks, where the sensitive or private data are stored or there is no fusion center or there exists a big data application. The main contributions of this paper are: 1) we decouple the combined dictionary atom update and nonzero coefficient revision procedure into two-stage operations to facilitate distributed computations, first updating the dictionary atom in terms of the eigenvalue decomposition of the sum of the residual (correlation) matrices across the nodes then implementing a local projection operation to obtain the related representation coefficients for each node; 2) we cast the aforementioned atom update problem as a set of decentralized optimization subproblems with consensus constraints. Then, we simplify the multiplier update for the symmetry undirected graphs in sensor networks and minimize the separable subproblems to attain the consistent estimates iteratively; and 3) dictionary atoms are typically constrained to be of unit norm in order to avoid the scaling ambiguity. We efficiently solve the resultant hidden convex subproblems by determining the optimal Lagrange multiplier. Some experiments are given to show that the proposed algorithm is an alternative distributed dictionary learning approach, and is suitable for the sensor network environment.

Enhanced multistatic active sonar signal processing.

Multistatic active sonar systems involve the transmission and reception of multiple probing sequences and can achieve significantly enhanced performance of target detection and localization through exploiting spatial diversity. This paper mainly focuses on two signal processing aspects of such systems, namely, enhanced range-Doppler imaging and improved target parameter estimation. The main contributions of this paper are (1) a hybrid dense-sparse method is proposed to generate range-Doppler images with both low sidelobe levels and high accuracy; (2) a generalized K-Means clustering (GKC) method for target association is developed to associate the range measurements from different transmitter-receiver pairs, which is actually a range fitting procedure; (3) the extended invariance principle-based weighted least-squares method is developed for accurate target position and velocity estimation. The effectiveness of the proposed multistatic active sonar signal processing techniques is verified using numerical examples.

Robust ellipse fitting based on sparse combination of data points.

Ellipse fitting is widely applied in the fields of computer vision and automatic industry control, in which the procedure of ellipse fitting often follows the preprocessing step of edge detection in the original image. Therefore, the ellipse fitting method also depends on the accuracy of edge detection besides their own performance, especially due to the introduced outliers and edge point errors from edge detection which will cause severe performance degradation. In this paper, we develop a robust ellipse fitting method to alleviate the influence of outliers. The proposed algorithm solves ellipse parameters by linearly combining a subset of ("more accurate") data points (formed from edge points) rather than all data points (which contain possible outliers). In addition, considering that squaring the fitting residuals can magnify the contributions of these extreme data points, our algorithm replaces it with the absolute residuals to reduce this influence. Moreover, the norm of data point errors is bounded, and the worst case performance optimization is formed to be robust against data point errors. The resulting mixed l1-l2 optimization problem is further derived as a second-order cone programming one and solved by the computationally efficient interior-point methods. Note that the fitting approach developed in this paper specifically deals with the overdetermined system, whereas the current sparse representation theory is only applied to underdetermined systems. Therefore, the proposed algorithm can be looked upon as an extended application and development of the sparse representation theory. Some simulated and experimental examples are presented to illustrate the effectiveness of the proposed ellipse fitting approach.

Image fusion using higher order singular value decomposition.

A novel higher order singular value decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.