Head-to-Head Comparison of Fibroblast Activation Protein Inhibitors (FAPI) Radiotracers versus [18F]F-FDG in Oncology: A Systematic Review

Several recent studies comparing radiolabeled fibroblast activation protein inhibitors (FAPI) and fluorine-18 fluorodeoxyglucose ([¹⁸F]F-FDG) as positron emission tomography (PET) radiotracers in oncology have been published. The aim of this systematic review is to perform an updated evidence-based summary about the comparison of these PET radiotracers in oncology to better address further research in this setting. Studies or subsets of studies comparing radiolabeled FAPI and [¹⁸F]F-FDG as PET radiotracers in oncology were eligible for inclusion in this systematic review. A systematic literature search of PubMed/MEDLINE and Cochrane library databases was performed until August 2021. Literature data about the comparison of [¹⁸F]F-FDG and radiolabeled FAPI are rapidly increasing. Overall, taking into account radiotracer uptake and tumor-to-background uptake ratio, compared to [¹⁸F]F-FDG PET, an equal or higher detection of primary tumors and/or metastatic lesions was usually demonstrated by using radiolabeled FAPI PET. In particular, the cancer entities with better detection rate of tumor lesions by using radiolabeled FAPI PET, compared to [¹⁸F]F-FDG PET, were gastrointestinal tumors, liver tumors, breast cancer and nasopharyngeal carcinoma. Further comparison studies are needed to better evaluate the best field of application of radiolabeled FAPI PET.     

Photodegradation of methylene blue with a titanium dioxide/polyacrylamide photocatalyst under sunlight

Hydrogels containing TiO₂ nanoparticles (NPs) have photocatalytic properties and degrade pollutants under light. In this study, a polyacrylamide (PAAm) hydrogel was synthesized with TiO₂ P25 NPs as the initiator, acrylamide as a monomer, and N,N′‐methylene bisacrylamide as a crosslinker in aqueous media under sunlight. Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy, and transmission electron microscopy were applied to characterize the TiO₂/PAAm hydrogel. The effects of different synthetic conditions, such as the initiator concentration, crosslinker, and dilution, on swelling were investigated. The maximum swelling of the TiO₂/PAAm hydrogel was 45 g/g in the hydrogel synthesized with optimum conditions by 0.2% TiO₂. The photocatalytic degradability of the hydrogel was investigated with methylene blue (MB) as the pollutant target. Also, the effects of the pH and MB concentration were studied. Under optimum conditions, 95.00% of the MB was degraded by the TiO₂/PAAm photocatalyst after 5 h of irradiation under sunlight. The comparison of the results with those of the TiO₂ P25 powder showed that the TiO₂ NPs had better activity than the hydrogel, but unlike the hydrogel, the activity of these NPs decreased in each recycling time because of the aggregation of NPs. Finally, the hydrogel was recycled seven times without a considerable reduction in the degradation efficiency. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43386.     

Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACT

A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.     

Variable neighborhood search for the cost constrained minimum label spanning tree and label constrained minimum spanning tree problems

Given an undirected graph whose edges are labeled or colored, edge weights indicating the cost of an edge, and a positive budget B, the goal of the cost constrained minimum label spanning tree (CCMLST) problem is to find a spanning tree that uses the minimum number of labels while ensuring its cost does not exceed B. The label constrained minimum spanning tree (LCMST) problem is closely related to the CCMLST problem. Here, we are given a threshold K on the number of labels. The goal is to find a minimum weight spanning tree that uses at most K distinct labels. Both of these problems are motivated from the design of telecommunication networks and are known to be NP-complete [15].In this paper, we present a variable neighborhood search (VNS) algorithm for the CCMLST problem. The VNS algorithm uses neighborhoods defined on the labels. We also adapt the VNS algorithm to the LCMST problem. We then test the VNS algorithm on existing data sets as well as a large-scale dataset based on TSPLIB [12] instances ranging in size from 500 to 1000 nodes. For the LCMST problem, we compare the VNS procedure to a genetic algorithm (GA) and two local search procedures suggested in [15]. For the CCMLST problem, the procedures suggested in [15] can be applied by means of a binary search procedure. Consequently, we compared our VNS algorithm to the GA and two local search procedures suggested in [15]. The overall results demonstrate that the proposed VNS algorithm is of high quality and computes solutions rapidly. On our test datasets, it obtains the optimal solution in all instances for which the optimal solution is known. Further, it significantly outperforms the GA and two local search procedures described in [15].     

Design of 3‐aminophenol‐grafted polymer‐modified zinc sulphide nanoparticles as drug delivery system

Zinc sulphide (ZnS) nanoparticles were synthesized by the coprecipitation method. The ZnS nanoparticle surface was polymerized with allyl glycidyl ether (AGE), and 3‐aminophenol was then deposited as a ligand on nanosorbent. The modified nanosorbent was investigated with Fourier transform infrared spectroscopy and thermogravimetric analysis. The particle size of the modified nanosorbent was studied with scanning electron microscopy. Some characteristic factors of the adsorption process such as pH and time were investigated for famotidine using the modified nanosorbent. The equilibrium adsorption study of famotidine by 3‐aminophenol‐grafted AGE/ZnS was analysed by adsorption isotherms of the Langmuir, Freundlich, and Temkin models. The famotidine‐releasing process was investigated in simulated biological fluids (intestinal fluid at pH of 7.4 and gastric fluid at pH of 1.2) and demonstrated 65% and 73% famotidine release during periods of 30 h (pH = 7.4) and 60 min (pH = 1.2), respectively. These results reveal the optimal performance of 3‐aminophenol‐grafted AGE/ZnS for sustained drug delivery.     

Experimental and computational analysis of acoustic characteristics of warp-knitted spacer fabrics

Abstract

As a kind of porous medium, textiles have recently received increased attention for acoustic applications due to their high profitability and low cost and environmental impact. 3D knitted spacer fabrics stand out as a unique class of textile materials. These materials simultaneously are soft, voluminous and highly porous and therefore are expected to be suitable for sound absorbing applications. This article aims to investigate the acoustic performance of 3D warp-knitted spacer fabrics. To this end, acoustic behavior of warp-knitted spacer fabrics was experimentally measured using the impedance tube method. In order to predict the sound absorption behavior of fabrics, a simple geometrical model was created. Flow resistivity was calculated by numerically solving incompressible laminar Newtonian flow through the 3D pore space of generated structure. The frequency-dependent sound absorption coefficient of the warp-knitted spacer fabric was predicted using the empirical models of Delany and Bazley, Garai and Pompoli and Dunn and Davern. The results showed that the Dunn and Davern model can predict sound absorption characteristics of warp-knitted spacer fabrics in the mid-to high-frequency ranges. At low frequency ranges; however, none of the empirical models can reasonably predict sound absorption behavior of the fabrics.