A High-Performance, Low-Cost, and Integrated Hairpin Topology RF Switched Filter Bank for Radar Applications.

Switched filter banks find widespread application in frequency-hopping radar systems and communication networks with multiple operating frequencies, especially in situations demanding elevated filter element isolation. In this paper, the design and implementation of a highly isolated switchable narrow-bandpass filter bank architecture using hairpin microstrip topology is presented. The filter bank has four discrete bandpass filters with passbands of 2.0-2.2 GHz, 2.3-2.5 GHz, 3.1-3.3 GHz, and 3.9-4.1 GHz. These filters span the radar S-frequency band (2.0-4.0 GHz). In order to switch between channels with a switching speed of nanoseconds, low-loss and highly isolated SP4T switches are implemented. Advanced design system (ADS) software is used to design the various filter functionalities, and the entire system is tested on a vector network analyzer (VNA). The proposed architecture makes it much easier to put the filter bank into practice and switch it to the desired frequency, which is useful for radar receiver applications.

A nanocomposite competent to overcome solubility and permeation issues of capsaicin and thiocolchicoside simultaneously in gout management: Fabrication of nanocubosomes.

This study aimed to formulate nano-cubosomes (NCs) co-loaded with capsaicin (CAP) and thiocolchicoside (TCS) to enhance their bioavailability and minimize associated potential side effects through transdermal delivery alongside their synergistic activity. Twenty seven (27) nano-cubosomal dispersions were prepared according to Box-Behnken factorial design and the effect of CAP, TCS, glyceryl mono oleate (GMO) and poloxamer 407 (P407) concentrations on particle size, polydispersity index (PDI), zeta potential, and entrapment efficiency were assessed. The results revealed that the optimized formulation exhibited a mean droplet size of 503 ± 10.3 nm, PDI of 0.405 ± 0.02, zeta potential of -10.0 ± 1.70 mV and entrapment efficiency of 86.9 ± 3.56 %. The in vivo anti-inflammatory effect of optimized formulation was studied in rats by injecting carrageenan to induce edema. The results of in vivo study showed that transdermal application of nano-cubosomes co-loaded with CAP and TCS significantly (p value < 0.05) improved carrageenan induced inflammation compared with standard treatment. The analgesic activity of optimized formulation was evaluated in rats by using Eddy's hot plate method. The findings of analgesic activity illustrated that the analgesic effects exhibited by test formulation may be associated with increased licking period and inhibition of prostaglandins level. In conclusion, the transdermal application of NCs co-loaded with CAP and TCS may be a promising delivery system for enhancing their bioavailability as well as synergistic analgesic and anti-inflammatory activity in gout management.

Isolation of a novel isoprenylated phenolic compound and neuroprotective evaluation of Dodonaea viscosa extract against cerebral ischaemia-reperfusion injury in rats.

Dodonaea viscosa grows widely in Saudi Arabia, but studies evaluating its neuroprotective activity are lacking. Thus, this study aimed to isolate and identify the secondary metabolites and evaluate the neuroprotective effects of D. viscosa leaves. The isolation and identification of phytochemicals were performed using chromatographic and spectroscopic techniques. The neuroprotective potential of the extract was evaluated against focal cerebral ischaemia-reperfusion injury in rat model. Neurobehavioural deficits in the rats were evaluated, and their brains were harvested to measure infarct volume and oxidative biomarkers. Results revealed the presence of three compounds: a novel isoprenylated phenolic derivative that was elucidated as 4-hydroxy-3-(3'-methyl-2'-butenyl) phenyl 1-O-ß-D-apiosyl-(1''' â†’ 6'')- ß-D-glucopyranoside (named Viscomarfadol) and two known compounds (isorhamnetin-3-O-rutinoside and epicatechin (4-8) catechin). Pre-treatment of the rats with the extract improved neurological outcomes. It significantly reduced neurological deficits and infarct volume; significantly reduced lipid peroxidation, as evidenced by decreased malondialdehyde levels; and significantly elevated antioxidant (superoxide dismutase, catalase, and glutathione) activities. These results indicate that D. viscosa is a promising source of bioactive compounds that can improve neurological status, decrease infarct volume, and enhance antioxidant activities in rats with cerebral ischaemic injury. Thus, D. viscosa could be developed into an adjuvant therapy for ischaemic stroke and other oxidative stress-related neurodegenerative disorders. Further investigations are warranted to explore other bioactive compounds in D. viscosa and evaluate their potential neuroprotective activities.

Formulation and characterization of polymeric nanoparticle of Rivastigmine for effective management of Alzheimer's disease.

Memory loss or dementia is a progressive disorder, and one of its common forms is Alzheimer's disease (AD), effecting mostly middle aged and older adults. In the present study, we developed Rivastigmine (RIV) nanoparticles using poly(lactic-co-glycolic acid) (RIV-loaded PLGA NPs) and polyvinyl alcohol (PVA). The prepared RIV-PLGA nanoparticles was evaluated for the management of Alzheimer's disease (AD). The nanoparticles were prepared by the slightly modified nano-precipitation technique. The developed formulations were evaluated for particle size, zeta potential (ZP), polydispersibility index (PDI) and surface morphology and drug content. The experimental result revealed that prepared RIV-loaded PLGA NPs (F1) was optimized having particle size (61.2 ± 4.6 nm), PDI (0.292), ZP (-11.2 ± 1.2). SEM study confirms the prepared nanoparticles depicted non-aggregated as well smooth surface particles without any fracture. This formulation (F1) was further assessed for in vivo studies on animal model. A pharmacological screening on an animal model of Alzheimer's disease revealed that RIV-loaded PLGA NPs formulations treat CNS disorders like Alzheimer's effectively. In addition to that, an in-vivo brain cholinesterase estimation study found that, animals treated with optimized formulation significantly (p < 0.01) reduced brain cholinesterase activity when compared to scopolamine-treated animals. According to the above results, it can be concluded that RIV-loaded PLGA NPs are ideal carriers for delivering the drug at a specific target site in the brain, thus may treat Alzheimer's disease efficiently and improve patient compliance.

Investigation of phytochemicals isolated from selected Saudi medicinal plants as natural inhibitors of SARS CoV-2 main protease: In vitro, molecular docking and simulation analysis.

The escalation of many coronavirus variants accompanied by the lack of an effective cure has motivated the hunt for effective antiviral medicines. In this regard, 18 Saudi Arabian medicinal plants were evaluated for SARS CoV-2 main protease (Mpro) inhibition activity. Among them, Terminalia brownii and Acacia asak alcoholic extracts exhibited significant Mpro inhibition, with inhibition rates of 95.3 % and 95.2 %, respectively, at a concentration of 100 µg/mL. Bioassay-guided phytochemical study for the most active n-butanol fraction of T. brownii led to identification of eleven compounds, including two phenolic acids (1, and 2), seven hydrolysable tannins (3-10), and one flavonoid (11) as well as four flavonoids from A. asak (12-15). The structures of the isolated compounds were established using various spectroscopic techniques and comparison with known compounds. To investigate the chemical interactions between the identified compounds and the target Mpro protein, molecular docking was performed using AutoDock 4.2. The findings identified compounds 4, 5, 10, and 14 as the most potential inhibitors of Mpro with binding energies of -9.3, -8.5, -8.1, and -7.8 kcal mol-1, respectively. In order to assess the stability of the protein-ligand complexes, molecular dynamics simulations were conducted for a duration of 100 ns, and various parameters such as RMSD, RMSF, Rg, and SASA were evaluated. All selected compounds 4, 5, 10, and 14 showed considerable Mpro inhibiting activity in vitro, with compound 4 being the most powerful with an IC50 value of 1.2 µg/mL. MM-GBSA free energy calculations also revealed compound 4 as the most powerful Mpro inhibitor. None of the compounds (4, 5, 10, and 14) display any significant cytotoxic activity against A549 and HUVEC cell lines.

Overcoming the hurdles: surmounting acquired resistance to anti-EGFR therapy in metastatic colorectal cancer.

Colorectal cancer is the third most prevalent cancer type in the United States, with an alarming incidence and mortality rate, especially among individuals younger than 50 years. The epidermal growth factor receptor (EGFR), essential for cell proliferation and survival, has surfaced as a promising therapeutic target for metastatic colorectal cancer and has demonstrated success in various clinical trials. Monoclonal antibodies such as cetuximab and panitumumab have proven to be effective against EGFR by blocking vital downstream signaling pathways and inhibiting gene transcription and cell proliferation. Despite this promise, most patients eventually develop resistance to anti-EGFR treatment, thereby limiting its long-term efficacy. Genomic alterations, such as mutations in KRAS, NRAS, and BRAF, often bypass the EGFR receptor, promoting resistance to therapy. Although our understanding of primary resistance to anti-EGFR therapy has improved, acquired resistance remains a significant hurdle. This review explores the potential mechanisms underpinning this acquired resistance and strategies to overcome it.

Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks.

Emerging consumer devices rely on the next generation IoT for connected support to undergo the much-needed digital transformation. The main challenge for next-generation IoT is to fulfil the requirements of robust connectivity, uniform coverage and scalability to reap the benefits of automation, integration and personalization. Next generation mobile networks, including beyond 5G and 6G technology, play an important role in delivering intelligent coordination and functionality among the consumer nodes. This paper presents a 6G-enabled scalable cell-free IoT network that guarantees uniform quality-of-service (QoS) to the proliferating wireless nodes or consumer devices. By enabling the optimal association of nodes with the APs, it offers efficient resource management. A scheduling algorithm is proposed for the cell-free model such that the interference caused by the neighbouring nodes and neighbouring APs is minimised. The mathematical formulations are obtained to carry out the performance analysis with different precoding schemes. Further, the allocation of pilots for obtaining the association with minimum interference is managed using different pilot lengths. It is observed that the proposed algorithm offers an improvement of 18.9% in achieved spectral efficiency using partial regularized zero-forcing (PRZF) precoding scheme at pilot length τp=10. In the end, the performance comparison with two other models incorporating random scheduling and no scheduling at all is carried out. As compared to random scheduling, the proposed scheduling shows improvement of 10.9% in obtained spectral efficiency by 95% of the user nodes.

Robust Resource Control Based on AP Selection in 6G-Enabled IoT Networks.

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22% at the cell-edge and 1.5% at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively.

6G-Powered Efficient Resource Control through IRS-UE Association.

The widespread popularity of live streaming, cloud gaming, mobile video streaming, and many real-time applications relies on high-speed data to ensure low latency and seamless user experience. This large high-speed data demand has led to the development of next-generation or sixth-generation (6G) communication technology. It aims to offer high-speed communication support to multiple applications and interactive services simultaneously. But the vulnerability of node communication to the changing propagation environment often leads to call drops, data loss, and high latency. This paper presents a 6G-enabled wireless network that makes use of multiple intelligent reflecting surfaces (IRSs). The distributed IRSs enhance the robustness of transmission but the increased overhead owing to multiple IRSs is the main challenge. To overcome this, efficient resource control is introduced, which associates sets of IRSs to user equipment (UE). An algorithm, namely IUABP (IRS-UE association based on pilots), is proposed; it offers selective resource control. Furthermore, the performance of the distributed IRS system is evaluated based on the achievable sum rate for different IRS numbers, reflecting elements, and transmit powers. We observed that the proposed association scheme offers an improvement of 30% in the achieved sum rate using N = 50 and R = 5 at a transmit power of 12 dBm. We also discuss the comparison with two other association schemes, namely, distance-based association and random association.

Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition.

Human action recognition is a constantly evolving field that is driven by numerous applications. In recent years, significant progress has been made in this area due to the development of advanced representation learning techniques. Despite this progress, human action recognition still poses significant challenges, particularly due to the unpredictable variations in the visual appearance of an image sequence. To address these challenges, we propose the fine-tuned temporal dense sampling with 1D convolutional neural network (FTDS-1DConvNet). Our method involves the use of temporal segmentation and temporal dense sampling, which help to capture the most important features of a human action video. First, the human action video is partitioned into segments through temporal segmentation. Each segment is then processed through a fine-tuned Inception-ResNet-V2 model, where max pooling is performed along the temporal axis to encode the most significant features as a fixed-length representation. This representation is then fed into a 1DConvNet for further representation learning and classification. The experiments on UCF101 and HMDB51 demonstrate that the proposed FTDS-1DConvNet outperforms the state-of-the-art methods, with a classification accuracy of 88.43% on the UCF101 dataset and 56.23% on the HMDB51 dataset.

EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification.

Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the models' high complexity. In this paper, we introduce EnViTSA, an innovative approach that tackles key challenges in AEC. EnViTSA combines an ensemble of Vision Transformers with SpecAugment, a novel data augmentation technique, to significantly enhance AEC performance. Raw acoustic signals are transformed into Log Mel-spectrograms using Short-Time Fourier Transform, resulting in a fixed-size spectrogram representation. To address data scarcity and overfitting issues, we employ SpecAugment to generate additional training samples through time masking and frequency masking. The core of EnViTSA resides in its ensemble of pre-trained Vision Transformers, harnessing the unique strengths of the Vision Transformer architecture. This ensemble approach not only reduces inductive biases but also effectively mitigates overfitting. In this study, we evaluate the EnViTSA method on three benchmark datasets: ESC-10, ESC-50, and UrbanSound8K. The experimental results underscore the efficacy of our approach, achieving impressive accuracy scores of 93.50%, 85.85%, and 83.20% on ESC-10, ESC-50, and UrbanSound8K, respectively. EnViTSA represents a substantial advancement in AEC, demonstrating the potential of Vision Transformers and SpecAugment in the acoustic domain.

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.

Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual's walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates.

Recent Advances in Traffic Sign Recognition: Approaches and Datasets.

Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.

Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications.

Since the CubeSats have become inherently used for the Internet of space things (IoST) applications, the limited spectral band at the ultra-high frequency (UHF) and very high frequency should be efficiently utilized to be sufficient for different applications of CubeSats. Therefore, cognitive radio (CR) has been used as an enabling technology for efficient, dynamic, and flexible spectrum utilization. So, this paper proposes a low-profile antenna for cognitive radio in IoST CubeSat applications at the UHF band. The proposed antenna comprises a circularly polarized wideband (WB) semi-hexagonal slot and two narrowband (NB) frequency reconfigurable loop slots integrated into a single-layer substrate. The semi-hexagonal-shaped slot antenna is excited by two orthogonal +/-45° tapered feed lines and loaded by a capacitor in order to achieve left/right-handed circular polarization in wide bandwidth from 0.57 GHz to 0.95 GHz. In addition, two NB frequency reconfigurable slot loop-based antennas are tuned over a wide frequency band from 0.6 GHz to 1.05 GH. The antenna tuning is achieved based on a varactor diode integrated into the slot loop antenna. The two NB antennas are designed as meander loops to miniaturize the physical length and point in different directions to achieve pattern diversity. The antenna design is fabricated on FR-4 substrate, and measured results have verified the simulated results.

HGR-ViT: Hand Gesture Recognition with Vision Transformer.

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.

A High-Performance Circularly Polarized and Harmonic Rejection Rectenna for Electromagnetic Energy Harvesting.

This paper presents a novel circularly polarized rectenna designed for efficient electromagnetic energy harvesting at the 2.45 GHz ISM band. A compact antenna structure is designed to achieve high performance in terms of radiation efficiency, axial ratio, directivity, effective area, and harmonic rejection over the entire bandwidth of the ISM frequency band. The optimized rectifier circuit enhances the RF harvested energy efficiency, with an AC-to-DC conversion efficiency ranging from 36% to 70% for low-level input power ranging from -10 dBm to 0 dBm. The stable output of DC power confirms the suitability of this design for various practical applications, including wireless sensor networks, energy harvesting power supplies, medical implants, and environmental monitoring systems. Experimental validation, which includes both the reflection coefficient and radiation patterns of the designed antenna, confirms the accuracy of the simulation. The study found that the proposed energy harvesting system has a high total efficiency ranging from 53% to 63% and is well-suited for low-power energy harvesting (0 dBm) from ambient electromagnetic radiation. The proposed circularly polarized rectenna is a competitive option for efficient electromagnetic energy harvesting, both as a standalone unit and in an array, due to its high performance, feasibility, and versatility in meeting various energy harvesting requirements. This makes it a promising and cost-effective solution for various wireless communication applications, offering great potential for efficient energy harvesting from ambient electromagnetic radiation.

Assembly of nanostructured lipid carriers loaded gefitinib and simvastatin as hybrid therapy for metastatic breast cancer: Codelivery and repurposing approach.

Breast cancer represents a life-threatening problem globally. The major challenge in the clinical setting is the management of cancer resistance and metastasis. Hybrid therapy can affect several cellular targets involved in carcinogenesis with a lessening of adverse effects. Therefore, the current study aims to assemble, and optimize a hybrid of gefitinib (GFT) and simvastatin (SIM)-loaded nanostructured lipid carrier (GFT/SIM-NLC) to combat metastatic and drug-resistant breast cancer. GFT/SIM-NLC cargos were prepared using design of experiments to investigate the impact of poloxamer-188 and fatty acids concentrations on the physicochemical and pharmaceutical behavior properties of NLC. Additionally, the biosafety of the prepared GFT/SIM-NLC was studied using a fresh blood sample. Afterward, the optimized formulation was subjected to an MTT assay to study the cytotoxic activity of GFT/SIM-NLC compared to free GFT/SIM using an MCF-7 cell line as a surrogate model for breast cancer. The present results revealed that the particle size of the prepared NLC ranged from (209 to 410 nm) with a negative zeta potential value ranging from (-17.2 to -23.9 mV). Moreover, the optimized GFT/SIM-NLC formulation showed favorable physicochemical properties and promising lymphatic delivery cargos. A biosafety study indicates that the prepared NLC has a gentle effect on erythrocyte hemolysis. Cytotoxicity studies revealed that GFT/SIM-NLC enhanced the killing of the MCF-7 cell line compared to free GFT/SIM. This study concluded that the hybrid therapy of GFT/SIM-NLC is a potential approach to combat metastatic and drug-resistant breast cancer.

Obtaining New Candidate Peptides for Biological Anticancer Drugs from Enzymatic Hydrolysis of Human and Bovine Hemoglobin.

Enzymatic hydrolysis of bovine and human hemoglobin generates a diversity of bioactive peptides, mainly recognized for their antimicrobial properties. However, antimicrobial peptides stand out for their ability to specifically target cancer cells while preserving rapidly proliferating healthy cells. This study focuses on the production of bioactive peptides from hemoglobin and evaluates their anticancer potential using two distinct approaches. The first approach is based on the use of a rapid screening method aimed at blocking host cell protein synthesis to evaluate candidate anticancer peptides, using Lepidium sativum seed germination as an indicator. The results show that: (1) The degree of hydrolysis (DH) significantly influences the production of bioactive peptides. DH levels of 3 to 10% produce a considerably stronger inhibition of radicle growth than DH 0 (the native form of hemoglobin), with an intensity three to four times greater. (2) Certain peptide fractions of bovine hemoglobin have a higher activity than those of human hemoglobin. (3) The structural characteristics of peptides (random coil or alpha helix) play a crucial role in the biological effects observed. (4) The α137-141 peptide, the target of the study, was the most active of the fractions obtained from bovine hemoglobin (IC50 = 29 ± 1 µg/mL) and human hemoglobin (IC50 = 48 ± 2 µg/mL), proving to be 10 to 15 times more potent than the other hemoglobin fractions, attributed to its strong antimicrobial potential. The second approach to assessing anticancer activity is based on the preliminary in vitro analysis of hydrolysates and their peptide fractions, with a focus on the eL42 protein. This protein is of major interest due to its overexpression in all cancer cells, making it an attractive potential target for the development of anticancer molecules. With this in mind, astudy was undertaken using a method for labeling formylase (formyl-methionyl-tRNA transformylase (FMTS)) with oxidized tRNA. This approach was chosen because of the similarities in the interaction between formylase and the eL42 protein with oxidized tRNA. The results obtained not only confirmed the previous conclusions but also reinforced the hypothesis that the inhibition of protein synthesis plays a key role in the anticancer mechanism of these peptides. Indeed, the data suggest that samples containing α137-141 peptide (NKT) and total hydrolysates may have modulatory effects on the interaction between FMTS and oxidized tRNA. This observation highlights the possibility that the latter could influence molecular binding mechanisms, potentially resulting in a competitive situation where the ability of substrate tRNA to bind efficiently to ribosomal protein is compromised in their presence. Ultimately, these results suggest the feasibility of obtaining candidate peptides for biological anticancer drugs from both human and bovine hemoglobin sources. These scientific advances show new hope in the fight against cancer, which affects a large number of people around the world.

Design, Physical Characterizations, and Biocompatibility of Cationic Solid Lipid Nanoparticles in HCT-116 and 16-HBE Cells: A Preliminary Study.

In this study, pEGFP-LUC was used as a model plasmid and three distinct cationic lipids (dioleyloxy-propyl-trimethylammonium chloride [DOTMA], dioleoyl trimethylammonium propane [DOTAP], and cetylpyridinium chloride [CPC]) were tested along with PEG 5000, as a nonionic surfactant, to prepare glyceryl monostearate (GMS)-based cationic solid lipid nanoparticles (cSLNs). Both the type and quantity of surfactant had an impact on the physicochemical characteristics of the cSLNs. Thermal analysis of the greater part of the endothermic peaks of the cSLNs revealed they were noticeably different from the individual pure compounds based on their zeta potential (ZP ranging from +17 to +56 mV) and particle size (PS ranging from 185 to 244 nm). The addition of cationic surfactants was required to produce nanoparticles (NPs) with a positive surface charge. This suggested that the surfactants and extensive entanglement of the lipid matrix GMS provided support for the behavioral diversity of the cSLNs and their capacity to interface with the plasmid DNA. Additionally, hemolytic assays were used to show that the cSLNs were biocompatible with the human colon cancer HCT-116 and human bronchial epithelial 16-HBE cell lines. The DOTMA 6-based cSLN was selected as the lead cSLN for further ex vivo and in vivo investigations. Taken together, these new findings might provide some guidance in selecting surfactants to prepare extremely efficient and non-toxic cSLN-based therapeutic delivery systems (e.g., gene therapy).

Assessment of the Anticancer Effect of Chlorojanerin Isolated from Centaurothamnus maximus on A549 Lung Cancer Cells.

The goal of this study was to assess the anticancer efficacy of chlorojanerin against various cancer cells. The effects of chlorojanerin on cell cytotoxicity, cell cycle arrest, and cell apoptosis were examined using MTT assay, propidium iodide staining, and FITC Annexin V assay. RT-PCR was employed to determine the expression levels of apoptosis-related genes. Furthermore, docking simulations were utilized to further elucidate the binding preferences of chlorojanerin with Bcl-2. According to MTT assay, chlorojanerin inhibited the proliferation of all tested cells in a dose-dependent manner with a promising effect against A549 lung cancer cells with an IC50 of 10 µM. Cell growth inhibition by chlorojanerin was linked with G2/M phase cell cycle arrest in A549 treated cells. Flow cytometry analysis indicated that the proliferation inhibition effect of chlorojanerin was associated with apoptosis induction in A549 cells. Remarkably, chlorojanerin altered the expression of many genes involved in apoptosis initiation. Moreover, we determined that chlorojanerin fit into the active site of Bcl-2 according to the molecular docking study. Collectively, our results demonstrate that chlorojanerin mediated an anticancer effect involving cell cycle arrest and apoptotic cell death and, therefore, could potentially serve as a therapeutic agent in lung cancer treatment.