arXiv subjects

Najeeb Ullah

Publications and source records attributed to Najeeb Ullah.

3 recordsLinked to original sources

arXiv2025

Research on Cumin Peptides Using PBS Extraction and Their Multifunctional Bioactivities

This study employed neutral PBS buffer combined with ammonium sulfate fractionation to isolate peptide-active fractions (PD-30, PD-50, PD-80) from Cuminum cyminum L.seeds (cumin) and systematically evaluated their antimicrobial, antioxidant, hypoglycemic, and anticancer activities. The results demonstrated that the PD-80 fraction exhibited potent antifungal activity against Candida albicans (inhibition zone diameter: 12.5 mm) and significant antioxidant capacity, with DPPH and ABTS radical scavenging rates of 72.4% and 78.9%. The PD-50 fraction showed the strongest antibacterial effect against Escherichia coli (inhibition zone diameter: 11.7mm), while PD-30 displayed the highest inhibitory activity against PTP1B (IC50=18.39 ug/mL), indicating its potential for hypoglycemic applications. Through mass spectrometry and database alignment, 414 peptides were identified for the first time in cumin-derived PBS extracts, including 18 structurally novel monomers comprising 11 antimicrobial peptides, 7 anticancer peptides, and 6 hypoglycemic peptides. Notably, peptide CK12 shares sequence homology (59% similarity) with the HIV fusion inhibitor T20, suggesting potential antiviral activity. This study elucidates the multifunctionality and structural basis of cumin peptides, providing a theoretical foundation for their application in natural pharmaceuticals and functional foods. Future research will focus on chemical modification and in vivo validation to advance their utilization in precision medicine and the modernization of traditional Chinese medicine.

q-bio.OT↗

arXiv2023

ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.

eess.IV↗

arXiv2022

Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

Android is one of the leading operating systems for smart phones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts' intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce; (1) a novel technique for opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we have developed a dataset for end-to-end detection of android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted features extraction, and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979.

cs.CR↗