Khalid A. Al-Gaadi^1,2, Ahmed M. Zeyada¹, ElKamil Tola^2,*, Abdullah M. Alhamdan^1,3, Khalid A.M. Ahmed^3,4, Rangaswamy Madugundu², Mohamed K. Edrris²

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1311-1323, 2024, DOI:10.32604/phyton.2024.049535

Abstract From economic and nutritional points of view, tomato is, historically, considered one of the most important crops. Without significant yield reduction, most commercial cultivars of tomato crops are sensitive to moderate levels of salinity. However, high levels of salt stress can negatively affect the yield and quality of tomato fruits. Therefore, this study was conducted to evaluate the yield and fruit quality of three tomato cultivars (Forester, Ghandowra-F1, and Feisty-Red) cultivated hydroponically, under three different levels of nutrient solution salinity. Evaluation of tomato fruits was performed based on quantity (number and weight of fruits, and… More >

Susan Azizmohammadi^*, Sima Azizmohammadi^*, Aghdas Safari^†, Maria Kaghazian^‡, Mina Sadrkhanlo^§, Vahid Behnod^¶, Mehri Seifoleslami^#

Oncology Research, Vol.25, No.4, pp. 495-501, 2017, DOI:10.3727/096504016X14749735594687

Abstract The investigation of specific genes will establish more useful biomarkers for accurate detection and management of gynecological cancers, especially patients with cervical cancer (CCP). The aim of this study was to evaluate the expression level of RIPK4 and EZH2 messenger RNA (RIPK4 and EZH2 mRNA) in CCP. Expression of RIPK4 and EZH2 in the tissues was determined by immunohistochemistry and qRT-PCR methods. Correlations of RIPK4 and EZH2 mRNA with clinical and pathological parameters were analyzed using the Fisher’s exact test. The mRNA level of RIPK4 was significantly upregulated in tumor tissues compared with matched adjacent… More >

Samaneh Kouzegaran^*, Kourosh Shahraki^†, Ali Makateb^‡, Farkhondeh Shahri^§, Negin Hatami^¶, Vahid Behnod^#, Amir Saber Tanha^**

Oncology Research, Vol.25, No.4, pp. 471-478, 2017, DOI:10.3727/096504016X14721217330657

Abstract In this study, expression of FasL and Ki-67 messenger RNA (FasL and Ki-67 mRNA) in human retinoblastoma (HRB) was examined by the immunohistochemistry method and quantitative real-time PCR. Positive expression of Ki-67 in tumor cells was detected in 16 of 30 patients (53.33%), and only 9 (30%) of the tissues from patients with retinoblastoma showed positive staining for FasL. Our results revealed that FasL expression was significantly higher in tumor tissue with invasion compared with the noninvasion form (p = 0.033). Ki-67 expression was markedly increased in tumor tissues with invasion compared with the noninvasion group… More >

Roberta Milani^*, Eleonora Brognara^*, Enrica Fabbri^*, Alessia Finotti^*, Monica Borgatti^*, Ilaria Lampronti^*, Giovanni Marzaro^†, Adriana Chilin^†, Kenneth Ka-Ho Lee^‡, Stanton Hon-Lung Kok^‡, Chung-Hin Chui^§, Roberto Gambari^*

Oncology Research, Vol.26, No.9, pp. 1307-1315, 2018, DOI:10.3727/096504017X14928634401187

Abstract Glioblastoma multiforme (GBM), a malignant tumor of the central nervous system, has a high mortality rate. No curative treatment is presently available, and the most commonly used chemotherapeutic drug, the alkylating agent temozolomide (TMZ), is only able to increase life expectancy and is often associated with drug resistance. Therefore, an urgent need does exist for novel drugs aimed at treating gliomas. In the present study, we obtained three major results using corilagin: (a) demonstrated that it inhibits the growth of U251 glioma cells through activation of the apoptotic pathway; (b) demonstrated that it is also More >

Fadl Dahan^*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >

Yuancong Chai, Yuefei Zhu^*, Wei Lin, Ding Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904

Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.… More >

Yan Dong^1,2, Kang Zhao¹, Liang Gao¹, Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1-18, 2024, DOI:10.32604/cmc.2024.048870

Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces.… More >

Yuhang Wang^1,*, Zhiqin He¹, Qinmu Wu¹, Tingsheng Lu², Yu Tang¹, Maoyun Zhu¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1377-1398, 2024, DOI:10.32604/cmc.2024.047379

Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and… More >

MARKO SKELIN^1,2,3,*, BRUNA PERKOV-STIPIČIN¹, SANJA VUŠKOVIĆ⁴, MARINA ŠANDRK PLEHAČEK⁵, ANE BAŠIĆ⁶, DAVID ŠARČEVIĆ⁷, MAJA ILIĆ⁸, IVAN KREČAK^2,3,9

Oncology Research, Vol.32, No.5, pp. 807-815, 2024, DOI:10.32604/or.2024.048948

Abstract Background: The European Society for Medical Oncology (ESMO) guidelines are among the most comprehensive and widely used clinical practice guidelines (CPGs) globally. However, the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated. This study assessed ESMO CPG levels of evidence (LOE) and grades of recommendations (GOR), as well as their trends over time across various cancer settings. Methods: We manually extracted every recommendation with the Infectious Diseases Society of America (IDSA) classification from each CPG. We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across… More >

Wei Wu^*, Yuan Zhang, Yunpeng Li, Chuanyang Li, Yan Hao

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174

Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an… More > Graphic Abstract