This work presented the design and development of pH-sensitive nanoparticles fabricated by dispersion polymerization (PEG-surface-tagged polymeric nanoparticles) loaded with cisplatin and paclitaxel. In vitro drug release and In vitro drug efficacy of the dual-loaded nanoparticles were carried out vis-à-vis their suitability for the treatment of triple-negative breast cancer (TNBC). A pH-sensitive acetal crosslinking agent was synthesized followed by incorporation in polylactide, poly( ε -caprolactone and polylactide-poly( ε -caprolactone) (blend) nanoparticles to facilitate the hydrolysis of the nanoparticle matrix in the tumor acidic microenvironment and the release of the encapsulated drugs. The efficacy of paclitaxel and cisplatin, when combined, and the nature of their interaction were determined based on a combination index in an In vitro cell-based assay (antiproliferative studies) using a triple-negative breast cancer cell line (MDA-MB-231). Data show a synergistic interaction between paclitaxel and cisplatin: the two drugs work together to enhance efficacy. Combination Index values for the different formulations were less than 1, showing synergism. The drug combination demonstrated greater cytotoxicity at lower doses compared to higher doses of paclitaxel or cisplatin alone, indicating that this dual-loaded drug approach could maintain therapeutic efficacy while reducing adverse side effects commonly associated with chemotherapy at high doses. This novel strategy not only facilitates dose reduction of chemotherapeutic agents but also enables the simultaneous, site-specific delivery of both drugs, offering a promising advancement in targeted TNBC therapy. Confocal microscopy studies showed the internalization of the nanoparticles in the cytoplasm.
A brain tumor is an aberrant cell development that may be benign or malignant. Tor form, size, location, and overlapping elements of visual characteristics associated with multi-grade BT identification therefore vary. In order to tackle these problems, a model for Multi-Grade Brain Tumor Detection (MGBTD) called Latent Graph Encoder Coupled Triple Generative Adversarial Networks with Walk-Spread Algorithm (LGECTGA2Nets+WSA) is proposed. To improve image quality, MRI images from the BRATS 2018 & Figshare datasets are first pre-processed using the Hybrid Recursive Reversible Box Filter-Based Fast Adaptive Bilateral Filtering (HRRBF-FABF) approach. Then, robust spatial-frequency feature extraction is done using the Discrete Quaternion Quadratic Phase Fourier Transform (DQQPFT). Tumor segmentation is performed with a Convolution-Transformer (CT), followed by classification using LGECTGA2Nets. The Walk-Spread Algorithm has been used to obtain model optimization. Python-based system has an accuracy and sensitivity value of 99.9% and 99.8% respectively, which is better when compared to other systems used in detecting and classifying multiple grades of tumors.
Recent studies have highlighted the promising chemotherapeutic and antioxidant properties of phytochemicals, which offer the advantage of fewer side effects compared to conventional therapies. Glioblastoma multiforme (GBM) is an aggressive and fatal brain tumor with a poor prognosis and limited treatment options. Despite advances in surgery, radiotherapy, and chemotherapy, effective GBM treatments remain a major challenge, largely due to the restrictive nature of the blood–brain barrier and the severe side effects associated with conventional drugs. The integration of herbal-based compounds into nanocarrier-based multimodal drug delivery systems holds significant potential to enhance therapeutic efficacy and overcome current limitations. This review begins with an overview of GBM, including current treatment strategies and the challenges associated with drug delivery to the brain. We then explore various phytochemicals and medicinal plants used in GBM therapy and their incorporation into nanostructured delivery systems. Finally, we discuss ongoing clinical trials and the biological fate of nanostructures, emphasizing their potential in GBM treatment. The therapeutic efficacy of natural compounds can be markedly improved through nanocarrier-based delivery. There is a growing need for novel natural biomaterials and their combination with potent chemotherapeutics, given their desirable characteristics such as biodegradability, biocompatibility, accessibility, renewability, and low toxicity. Recent advancements underscore the potential of combining nanotechnology with phytotherapy as a promising approach for GBM treatment—an area where conventional therapies have often proven inadequate.
Human health has already been significantly influenced by rising atmospheric CO 2 and declining O 2 levels posing critical challenges for environmental sustainability and life-support technologies. One solution to this problem is to develop photocatalytic material systems capable of capturing sufficient amounts of CO 2 for its visible light conversion to O 2 from water. The hypothesis of this study is that high surface area, porous, nanomaterial systems will capture enough CO 2 from water for its conversion to O 2 using visible light. Due to their high surface area and high porosity, here, a metal—organic framework (MOF-210) is expected to adsorb ample CO 2 and release large quantities of O 2 after combination with bismuth vanadate (BiVO 4 ) nanoparticles and photocatalysis. To test this, BiVO 4 nanoparticles were precipitated onto MOF-210, and the resulting composites were dispersed in water. Different concentrations of MOF-210 and water were tested. They were then exposed to visible light, and the initial and final CO 2 and O 2 levels were measured using CO 2 sensors and pH test strips. For comparison, the MOF-210 higher oxygen-containing BiVO 4 nanosystem was evaluated against a lower surface area oxygen-containing, non-porous, BiVO 4 -titanium dioxide (TiO 2 ) nanosystem. Materials were characterized and confirmed for size (using Scanning Electron Microscopy and Dynamic Light Scattering) and chemistry (using Energy Dispersive Chemistry). Experimental results showed that the high surface area, highly porous, MOF-210 did indeed adsorb sufficient CO 2 and when combined with BiVO 4 nanoparticles formed sufficient amounts of O 2 . The control lower surface area, non-porous, TiO 2 nanoparticle system did not generate sufficient O 2 . These findings confirm that the high surface area, highly porous MOF–BiVO 4 nanocomposites can indeed adsorb and convert sufficient CO 2 into useful O 2 highlighting the potential of MOF-based photocatalysts as dual-function materials for mitigating greenhouse gases and regenerating oxygen, with broader implications for climate change remediation and closed-loop life support in space exploration necessary to improve human health.
Polyether ether ketone (PEEK) has been used extensively as an orthopedic and spinal implant device. Recently, it has been shown that substituting a ketone for an ether in the backbone of PEEK forming polyether ketone ketone (PEKK) can improve in vitro bone cell function and decrease bacteria functions without resorting to antibiotic use. However, the mechanism by which PEKK outperforms traditional orthopedic and spinal implants has not been elucidated to date. Since it is well known that initial protein adsorption controls bacteria attachment to implants, the objective of the present in vitro study was to determine the mechanism by which PEKK inhibits bacteria colonization. Results demonstrated that the presently fabricated PEKK possessed a large degree of nanoscale surface features which led to a surface energy that matched that of casein, mucin, and lubricin which are proteins known to reduce bacteria colonization. Further, when coating PEKK individually with casein, mucin, and lubricin, in vitro assays demonstrated significantly less MRSA, Staph. epidermidis , and Pseudomonas aeruginosa after 24 hours compared to PEEK. In addition, there were less live bacteria on the PEKK compared to PEEK. In this manner, this study provides the first mechanism of action for how PEKK decreases bacteria colonization as it showed that PEKK has a surface energy closer to that of proteins known to inhibit bacteria colonization. PEKK promotes the adsorption of such proteins to consequently decrease bacteria attachment and growth and thus should be further studied as a novel material that could potentially reduce orthopedic and spinal implant infection without resorting to antibiotic use.
Activating endogenous anti-tumor immunity in cancer therapy can dramatically improve therapeutic outcomes and therefore sparks enormous interest. Nevertheless, achieving simultaneous tumor control and robust T-cell immune activation remains a formidable challenge. Herein, we report a folate-functionalized graphene oxide–lobaplatin nanocomposite (FA-GO-LBP) that delivers photothermal–chemotherapy for epithelial ovarian cancer while simultaneously eliciting robust T-cell antitumor immune responses. FA modification enables specific cancer cell targeting, facilitates intracellular delivery via endocytosis, suppresses hypoxia-inducible factor 1α (HIF-1α) expression, and inhibits tumor cell growth. Upon near-infrared (NIR) irradiation, FA-GO-LBP exhibits synergistic photothermal-chemotherapy through localized hyperthermia and controlled release of lobaplatin. In vitro experiments demonstrate that this approach is highly selective and efficiently eliminates tumor cells through thermal stimulation and drug release, achieving an approximate tumor cell inhibition rate of 94.3%. Moreover, by inducing immunogenic cell death (ICD) and folate-dependent HIF-1α suppression, this synergistic therapy enhances inflammatory cytokines production in lymphocytes and expands CD4 + and CD8 + effector T cells populations approximately 1.59-fold. By integrating photothermal ablation, targeting chemotherapy, and T-cell activation, FA-GO-LBP presents a promising nanotherapeutic strategy for the comprehensive treatment of epithelial ovarian cancer.
Knocking out the oncogene UBC12 using CRISPR-Cas9 inhibited the neddylation pathway and produced significant anticancer effects. However, the delivery of CRISPR-Cas9 into tumor cells remains an urgent scientific challenge. Based on the clinical applications of polyethyleneimine (PEI) and its derivatives (GMP in vivo -jetPEI), we constructed a fluorinecontaining PEI to deliver CRISPR-Cas9 plasmids. To reduce off-target effects and improve delivery efficiency, we modified the nanomedicine surface with TLS11a aptamer-targeting molecules. Under the guidance of these targeting molecules, the CRISPR-Cas9 plasmid is internalized by liver cancer cells where it knocks out the UBC12 oncogene, inhibits the ned-dylation pathway in hepatocellular carcinoma cells, and induces the accumulation of cullin-ring ligases tumor-suppressor substrates, thereby suppressing liver cancer growth. This genetic nanomedicine design provides a scientific basis for the development of non-viral genetic nanomedicines.
This study explores the potential of sugarcane molasses as a culture medium for microbial electrosynthesis, with an emphasis on its capacity to facilitate CO 2 capture and subsequent conversion into energy-rich organic compounds. Microbiological analyses identified a diverse community of microorganisms, particularly species of the genus Bacillus , which are known for their resilience in redox-variable environments and enhanced performance in electrosynthetic systems. As a carbon source, sugarcane molasses effectively supported bacterial growth. When exposed to electrical currents, these microorganisms exhibited increased metabolic activity, promoting efficient CO 2 assimilation and generation of bioenergy-related compounds. The findings underscore sugarcane molasses as a promising substrate for carbon capture and renewable energy applications, highlighting microbial electrosynthesis as a viable strategy in sustainable biotechnology.
Brain stroke detection is one of the important diagnoses in healthcare domain. Accurate classification of brain stroke helps physicians to provide proper medication, and automatic classification reduces human efforts. Machine learning algorithms-based stroke classification frameworks are developed to extract and classify the brain image attributes. The recent development of deep learning techniques presented numerous architectures for different image processing applications. The functioning of the deep learning frameworks depends on their deep feature extraction characteristics and classification abilities. Convolutional Neural Network (CNN) is a familiar deep learning framework utilized in the proposed work for brain stroke classification. The deep extraction characteristics of CNN are improved in the proposed CNN framework, and the extracted attributes are classified using the XGBoost classifier. The Laboratory of Processing Image, Signals, and Computer Science (LAPISCO) dataset for brain stroke is utilized for experimental inspection. Improved functioning in stroke classification is verified through experiments and compared with conventional machine learning algorithms and H-StrokeNet. Experimental outcomes show that combining the improved CNN framework with XGBoost provides 97.67% accuracy, and the H-StrokeNet gives 96.55%.
In this work, an efficient Hybrid Lung Nodules Detection Network (HLNDNet) is developed for early lung cancer diagnosis using the Hybrid Deep Leaning Approach (HDLA). The effect of hybridizing deep learning architectures, such as Visual Geometric Group (VGG)-16, AlexNet, and GoogleNet, has not been thoroughly examined, even though these designs have been extensively researched for various image processing applications. Hence, this work designs an HDLA to detect and classify lung nodules using Computed Tomography (CT) images. The important modules include preprocessing, lung region segmentation, and lung nodule detection by a hybrid approach. Wiener filter and morphological operations are employed in preprocessing modules to filter noise and enhance lung structures. In the second module, the lungs are segmented, and the lung’s external components are removed using the blob detection technique. The proposed semantic image segmentation is employed independently using VGG-16, AlexNet, and GoogleNet in the last module. It labels each pixel in the lung region as either normal or abnormal (lung nodule). In the last module, the outputs of the architectures are hybridized using a Weighted Voting Approach (WVA) to reduce the false positives. The HLNDNet’s performance is evaluated using CT images of 130 patients from the database of Lung Image Database Consortium (LIDC) with three performance metrics: Intersection over Union (IoU), Class Accuracy (CA), and Pixel Accuracy (PA). Results show that the proposed HLNDNet attains an impressive PA of 98.39%, mean CA of 90.42%, and mean IoU of 78.68% for detecting lung nodules, as the hybrid approach helps improve the performance metrics over their counterparts.
We aimed to evaluate the drug coated balloon (DCB) based on lightweight deep learning model on coronary bifurcation lesions in patients with coronary artery disease (CAD). A lightweight deep learning model was applied to coronary angiography in patients. According to the different use of balloon after pre-treatment of branch vessel cutting balloon, the patients were assigned to observation (n = 45) and control (n = 45) groups. In terms of demographic data, no significant difference was observed between the groups (P >0·05). Similarly, no intergroup difference of statistical significance was found in Medina classification, reference diameter and minimum diameter of main branches (P >0·05). Following a 9-month period of follow-up, a significantly lower rate of adverse reaction (4.44%) was observed in the observation group, as opposed to the control group (17.78%) (P <0·05). While there was a postoperative decrease in the extent of vascular stenosis in both groups, the extent to which vascular stenosis was reduced in the observation group was markedly higher (P <0·05). In the results of branch re-examination diameter confirmed an intergroup difference of statistical significance (P <0·05). The duration of exposure to X-rat, operation time, and average dosage of contrast agent was substantially lower in the observation group (P <0·05). DCB active protection technology can reduce the extent of vascular stenosis in CAD.
Dengue fever poses a significant health threat that is expected to worsen due to climate change. Dengue epidemic prediction research faces challenges such as nonlinear interactions, model transparency issues, and biases in input data like temperature and reported cases. Existing dataset limitations hinder the full capture of transmission dynamics, reducing model accuracy. Overfitting, exacerbated by poor regularization or large ensemble sizes, further impacts prediction reliability. To address these challenges, a novel Advanced Fusion Ensemble Network with Dual Long Short-Term Memory that leverages advanced artificial intelligence techniques for enhanced predictive accuracy. Our approach integrates data preprocessing using the Adaptive Impute Scaler to ensure data consistency and reduce bias. Feature selection is achieved through a Gradual Ascent Network (GAN) to mitigate overfitting by extracting hierarchical features and reducing dimensionality. The Dual Long Short-Term Memory (D-LSTM) architecture effectively captures dengue transmission patterns. The model’s predictions are refined using a fully connected softmax function. The Local Epidemics Dengue Fever dataset was utilized, achieving a Root Mean Square Error (RMSE) of 52.44 and a Mean Absolute Error (MAE) of 25.45 for San Juan, and MAE and RMSE of 1.23 and 11.84, respectively, for Iquitos. Our findings demonstrate that the proposed AI-driven model offers accurate dengue forecasting and could be applicable to similar infectious diseases.
This study investigated the effects of bioactive glass, SiO2–Na2O–CaO–P2O5 (SNCP), and SNCP doped with titanium (SNCP:10Ti) on human dental pulp cell (hDPC) viability, migration, and alkaline phosphatase (ALP) expression. The materials were prepared and their morphology and structural changes were analyzed using atomic force microscopy and Fourier-transform infrared spectroscopy, respectively. hDPCs were cultured in aqueous solutions of SNCP, SNCP:10Ti, and Ca(OH)2. Cell viability was evaluated using the MTT formazan assay, and cell migration was assessed by a scratchassay. ALP activity was analyzed on day 7. Cell viability, pH, ALP fluorescence, and wound healing were analyzed using analysis of variance and Tukey’s test (α = 0.05). The thickness of the materials was 1 μm, and SNCP:10Ti had Ti nanoparticles located on the edge. The incorporation of TiO2 into SNCP did not affect the samples’ chemical integrity. Cytotoxicity levels were high for all materials at a 1:1 dilution (p<0.05). Cytotoxicity of all materials at 1:4, 1:8, and 1:16 dilutions was similar (p >0.05); and that of all materials at 1:8 and 1:16 dilutions was similar to the control cytotoxicity (p>0.05). However, SNCP:10Ti showed reduced cytotoxicity at a 1:8 dilution (p<0.05). The migration test did not reveal significant differences between samples (p > 0.05). The fluorescence intensity was similar in all experimental groups (p>0.05) but was lower in the control group (p > 0.05). In conclusion, SNCP and SNCP:Ti show potential for use in vital pulp therapies. Doping SNCP with Ti reduced its cytotoxicity in hDPCs.
The COVID-19 pandemic has thrusted the world into a public health crisis, necessitating a relentless pursuit of effective nanotechnological treatments alongside vaccination efforts. Coronavirus 2 (SARS-CoV-2, the virus that causes COVID-19) can persist in the blood and tissue for over a year, causing long COVID-19 and associated risks. As COVID continues to harm people worldwide, it is clear that there are numerous vastly different ways in which patients respond to the same SARS-CoV-2 virus, requiring a personalized nanotechnological drug approach. In the repurposing of drugs for COVID-19, in silico methods, driven by computational simulations, have proven instrumental. In harnessing the power of machine learning (ML), a subset of artificial intelligence (AI) tools, vast datasets of existing drugs and diseases can be efficiently analyzed to choose the right datasets for personalized COVID-19 treatment. Significantly, this approach is not only cost-effective but also expeditious, offering a quicker and more economical avenue than traditional drug discovery processes. In the study of SARS-CoV-2, ML has proven to be an effective approach, especially for identifying targets for potential therapeutic development and personalized treatment. Because ML models can handle large, complex datasets with ease, they are powerful tools for studying proteomic and genetic data of viruses. By discovering relationships in the data, ML models can help prioritize proteomic or genomic areas that are crucial for viral replication, entry, or evasion of host barricades. This process can lead to the identification of possible personalized therapeutic targets. This literature review article delves into the innovative approach of using AI, ML and nanotechnological 3D bioprinting (3DBP) for in silico drug repurposing to battle COVID-19. The article provides a detailed investigation of SARS-CoV-2 targets, the role of AI and ML in various aspects of COVID-19 management, and the integration of nanotechnological 3DBP in creating in vitro tissue models and therapeutic agents to precisely fabricate structures at the nanoscale. In doing so, this study highlights an important personalized and more effective approach to treat patients today for COVID-19 and any virus in the future.
This review examines the integration of the Internet of Things (IoT) and Social Internet of Things (SIoT) with nanotechnology-driven healthcare, highlighting their transformative capabilities for patient-centered care. The review offers wa comprehensive summary of IoT and SIoT technologies, tracing their development, and their practical implementation in the field of personalized medicine. Key studies are thoroughly examined to combine findings on approaches, results, and constraints,providing a well-rounded view of their present position in healthcare. Case studies specifically demonstrate the effectiveness of SIoT applications, showcasing improvements in patient care, healthcare services, and results. Obstacles such as technical hurdles, moral conundrums, budget limitations, and regulatory obligations are high-lighted, focusing on potential avenues for enhancement. The review highlights the unexploited potential of IoT and SIoT in facilitating advancements in nanotechnology innovations and specifies future research paths to overcome existing shortcomings.This work seeks to provide a comprehensive overview of knowledge,ultimately serving as a resource for researchers and policymakers to utilize these technologies in promoting improved patient outcomes and streamlined healthcare delivery.
Pityriasis versicolor is a superficial fungal infection caused by lipophilic yeasts of the genus Malassezia spp., characterized by a skin pigmentation disorder. Topical antifungal agents are commonly used for treatment; however, conventional therapies often lead to disease recurrence. This study aimed to develop and characterize an oil-in-water moisturizing emulsion containing biogenic silver nanoparticles and essential oils from Origanum vulgare and Melaleuca alternifolia as a potential treatment for pityriasis versicolor. The antifungal activity of the active ingredients against Malassezia pachydermatis was evaluated using disk diffusion, minimum inhibitory concentration, minimum fungicidal concentration, and checkerboard assays. Eight formulations were developed: one control and seven with active compounds, either alone or in binary or ternary combinations. Pharmacotechnical characteristics were assessed through organoleptic and physicochemical tests, along with a stability study. The antifungal efficacy of the formulations was evaluated using time-kill and scanning electron microscopy assays against M. pachydermatis. The essential oils and silver nanoparticles exhibited antifungal activity. All formulations demonstrated appropriate organoleptic and physicochemical characteristics for topical use. The formulation containing 3% biogenic silver nanoparticles showed the best spreadability and acted as a fungicide against M. pachydermatis within 48 h of treatment. Scanning electron microscopy revealed deformed fungal cells with physical damage and morphological changes. The formulation containing biogenic silver nanoparticles was identified as a potential antifungal alternative for the treatment of pityriasis versicolor. This is the first time biogenic silver nanoparticles have been incorporated into a topical cream, creating a nanotechnological product for pityriasis versicolor treatment, with promising potential in the pharmaceutical field.
Predicting Protein–Protein Interactions (PPIs) is essential to comprehending biological functions and is pivotal for drug discovery and disease understanding.However, accurately predicting these interactions remains a difficult issue because of the intricate and multifaceted nature of protein networks. Traditional models often fail to fully capture the intricate relationships between proteins and their interactions, especially when diverse datasets are involved. To address these challenges, a novel approach, named the Deep Radial Graph Basis Prism Refraction Search Convolutional Networks(DRGB-PRSCN) model, is proposed for PPI prediction using three distinct datasets: Human PPI, STRING, and DIP.The proposed method employs Gradient Domain Guided Filtering for effective data preprocessing, ensuring noise reduction while preserving essential features. Feature extraction is carried out using an Elastic Decision Transformer, which effectively captures key protein features. Deep Graph Convolutional Networks (DGCNs) are leveraged to model complex dependencies and interactions among proteins. The DRGB-PRSCN model, with its advanced architecture, is employed to predict the interactions with high precision. The model achieves a performance evaluation score of 99.9%, demonstrating its efficacy in accurately predicting PPI. This approach outperforms traditional methods by providing superior accuracy and robustness, making it highly beneficial for biological network analysis and drug discovery. The DRGB-PRSCN model’s primary benefit is its capacity to efficiently handle diverse datasets and predict PPIs with exceptional precision.
Epithelial-mesenchymal transition (EMT) is a pivotal biological phenomenon that underpins critical events in embryonic development and is reactivated in pathological conditions, including cancer metastasis and tumorigenesis. Despite being a well-studied topic, recent technological advancements and discoveries have shed new light on the intricacies of EMT regulation. EMT involves a multifaceted system of transcriptional and translational regulators, coupled with post-transcriptional and post-translational modifications that amplify initial indications. This review comprehensively examines key aspects of EMT research, spanning from its role during embryonic development, its implications in cancer biology, and the regulatory molecular pathways governing this process. Firstly, we delve into EMT during embryonic development, exploring the signaling pathways in gastrulation and neural crest formation, which highlight the conservation of EMT mechanisms across diverse biological contexts. Shifting focus to its connection with cancer, we elucidate the impact of EMT on disruption of cell junctions, cancer cell survival and polarity, the emergence of cancer stem cells, circulating tumor cells, and the development of drug resistance. Furthermore, we discuss the intricate regulatory pathways involved in EMT, encompassing gene expression alterations, the complexity of signaling cascades, the role of microRNAs, and the intriguing intersection with autophagy. Lastly, we address the critical role of EMT in cancer metastasis, emphasizing its significance in driving the invasive and migratory behavior of cancer cells. In conclusion, this review integrates historical insights with recent breakthroughs, providing a comprehensive understanding of the multifaceted role of EMT in both development and cancer biology, and highlighting its potential as a therapeutic target in cancer management.
CD105 is a serum marker of hepatoma. In this study, we established a stable detection platform constructed using a hemin-graphene-aptamer complex (HGN-apt) by optimizing the ratio of graphene, hemin, and aptamer. We also optimized CD105 detection conditions. The detection limit of this platform is 5.5 ng/mL, enabling sensitive detection of CD105. These experiments facilitate an innovative approach for the construction of a detection platform with high sensitivity and an expanded detection range using an HGN-apt probe, enabling specific detection of the serum hepatoma marker CD105. Our findings provide an important foundation for early diagnosis of hepatomas, and would therefore help to alleviate patient suffering, including mental and economic burdens. Our platform also offers a quantitative indicator for the prognosis of hepatomas.
Cellulose nanopaper (CNP) is a kind of flat foldable film material composed of cellulose nanofibers. Cellulose nanopaper is based on nanocellulose (NC) as the basic unit, with excellent mechanical properties, thermal properties, optical properties and other characteristics, is a high-performance new material. This study discusses the mechanism of cis-platinum and CXCR4 siRNA carried with CNP in restraining the biological effect of breast carcinoma cells. CXCR4 siRNA carried with CNP was established and identified. The MCF-7 cells were divided into control set, si-CXCR4 set, cis-platinum set and united set randomly. The proliferative and apoptotic activity, CXCR4 presentation, presentation of EMT, and invasive ability were detected. mRNA and protein expression of CXCR4 was restrained by knockdown of CXCR4 notably. The proliferation was restrained and apoptosis was prompted. Expression of E-cadherin was increased and Zeb1 presentation was reduced. The invasive ability was restrained. The action was more notable in united set. Breast carcinoma cell proliferation was restrained and the apoptosis was prompted by cis-platinum and CXCR4 siRNA carried with CNP. The occurrence of EMT and invasion were therefore restrained. The sensibility of breast carcinoma on drugs was elevated.
