Prediction of ovarian cancer using artificial intelligence tools.

Purpose: Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person. Method: In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected. Results: The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer. Conclusion: Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.

The anti-cancer properties of miR-340 plasmid-chitosan complexes (miR-340 CC) on murine model of breast cancer.

MiRNA-340 (miR-340) has been found to have tumour-suppressing effects in breast cancer (BC). However, for clinical use, miRNAs need to be delivered safely and effectively to protect them from degradation. In our previous study, we used chitosan complexes as a safe carrier with anti-cancer properties to deliver miR-340 plasmid into 4T1 cells. This study explored further information concerning the anti-cancer impacts of both chitosan and miR-340 plasmid in a murine model of BC. Mice bearing 4T1 cells were intra-tumorally administered miR-340 plasmid-chitosan complexes (miR-340 CC). Afterwards, the potential of miR-340 CC in promoting anti-tumour immune responses was evaluated. MiR-340 CC significantly reduced tumour size, inhibited metastasis, and prolonged the survival of mice. MiR-340 CC up-regulates P-27 gene expression related to cancer cell apoptosis, and down-regulates gene expressions involved in angiogenesis and metastasis (breast regression protein-39 (BRP-39)) and CD163 as an anti-inflammatory macrophages (MQs) marker. Furthermore, CD47 expression as a MQs immune check-point was remarkably decreased after miR-340 CC treatment. The level of IL-12 in splenocytes of miR-340 CC treated mice increased, while the level of IL-10 decreased, indicating anti-cancer immune responses. Our findings display that miR-340 CC can be considered as a promising therapy in BC.

NIR-Responsive injectable hydrogel cross-linked by homobifunctional PEG for photo-hyperthermia of melanoma, antibacterial wound healing, and preventing post-operative adhesion.

Current therapeutic approaches for skin cancer face significant challenges, including wound infection, delayed skin regeneration, and tumor recurrence. To overcome these challenges, an injectable adhesive near-infrared (NIR)-responsive hydrogel with time-dependent enhancement in viscosity is developed for combined melanoma therapy and antibacterial wound healing acceleration. The multifunctional hydrogel is prepared through the chemical crosslinking between poly(methyl vinyl ether-alt-maleic acid) and gelatin, followed by the incorporation of CuO nanosheets and allantoin. The synergistic inherent antibacterial potential of CuO nanosheets, the regenerative and smoothing effect of allantoin, the extracellular matrix-mimicking effect of gelatin, and the desirable swelling behavior of the hydrogel results in fast wound recovery after photothermal ablation of the tumor. Additionally, the hydrogel can serve as an alternative to sutures owing to its tissue adhesiveness ability, which can further render it the merits for accelerated repair of abdominal lesions while acting as a biocompatible barrier to prevent peritoneal adhesion.

Ultrasensitive detection of vital biomolecules based on a multi-purpose BioMEMS for Point of care testing: digoxin measurement as a case study.

Rapid and label-free detection of very low concentrations of biomarkers in disease diagnosis or therapeutic drug monitoring is essential to prevent disease progression in Point of Care Testing. For this purpose, we propose a multi-purpose optical Bio-Micro-Electro-Mechanical-System (BioMEMS) sensing platform which can precisely measure very small changes of biomolecules concentrations in plasma-like buffer samples. This is realized by the development of an interferometric detection method on highly sensitive MEMS transducers (cantilevers). This approach facilitates the precise analysis of the obtained results to determine the analyte type and its concentrations. Furthermore, the proposed multi-purpose platform can be used for a wide range of biological assessments in various concentration levels by the use of an appropriate bioreceptor and the control of its coating density on the cantilever surface. In this study, the present system is prepared for the identification of digoxin medication in its therapeutic window for therapeutic drug monitoring as a case study. The experimental results represent the repeatability and stability of the proposed device as well as its capability to detect the analytes in less than eight minutes for all samples. In addition, according to the experiments carried out for very low concentrations of digoxin in plasma-like buffer, the detection limit of LOD = 300 fM and the maximum sensitivity of S = 5.5 × 1012 AU/M are achieved for the implemented biosensor. The present ultrasensitive multi-purpose BioMEMS sensor can be a fully-integrated, cost-effective device to precisely analyze various biomarker concentrations for various biomedical applications.

Natural agents as wound-healing promoters.

The management of acute and chronic wounds resulting from diverse injuries poses a significant challenge to clinical practices and healthcare providers. Wound healing is a complex biological process driven by a natural physiological response. This process involves four distinct phases, namely hemostasis, inflammation, proliferation, and remodeling. Despite numerous investigations on wound healing and wound dressing materials, complications still persist, necessitating more efficacious therapies. Wound-healing materials can be categorized into natural and synthetic groups. The current study aims to provide a comprehensive review of highly active natural animal and herbal agents as wound-healing promoters. To this end, we present an overview of in vitro, in vivo, and clinical studies that led to the discovery of potential therapeutic agents for wound healing. We further elucidated the effects of natural materials on various pharmacological pathways of wound healing. The results of previous investigations suggest that natural agents hold great promise as viable and accessible products for the treatment of diverse wound types.

Toxicity prediction of nanoparticles using machine learning approaches.

Nanoparticle toxicity analysis is critical for evaluating the safety of nanomaterials due to their potential harm to the biological system. However, traditional experimental methods for evaluating nanoparticle toxicity are expensive and time-consuming. As an alternative approach, machine learning offers a solution for predicting cellular responses to nanoparticles. This study focuses on developing ML models for nanoparticle toxicity prediction. The training dataset used for building these models includes the physicochemical properties of nanoparticles, exposure conditions, and cellular responses of different cell lines. The impact of each parameter on cell death was assessed using the Gini index. Five classifiers, namely Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network, were employed to predict toxicity. The models' performance was compared based on accuracy, sensitivity, specificity, area under the curve, F measure, K-fold validation, and classification error. The Gini index indicated that cell line, exposure dose, and tissue are the most influential factors in cell death. Among the models tested, Random Forest exhibited the highest performance in the given dataset. Other models demonstrated lower performance compared to Random Forest. Researchers can utilize the Random Forest model to predict nanoparticle toxicity, resulting in cost and time savings for toxicity analysis.

Anti-cancer Effects of a Chitosan Based Nanoformulation Expressing miR-340 on 4T1 Breast Cancer Cells.

MicroRNAs (miRNAs) have a crucial role in the regulation of gene expression in tumor development, invasion, and metastasis. Herein, miRNA-340 (miR-340) has been shown to play tumor suppressor activity in breast cancer (BC). However, the clinical applications of miRNAs request the development of safe and effective delivery systems capable of protecting nucleic acids from degradation. In this study, biodegradable chitosan nanoparticles incorporating miR-340 plasmid DNA (pDNA) (miR-340 CNPs) were synthesized and characterized. Then, the anti-tumor effects of miR-340 CNPs were investigated using 4T1 BCE cells. The spherical nanoparticles (NPs) with an appropriate mean diameter of around 266 ± 9.3 nm and zeta potential of +17 ± 1.8 mV were successfully prepared. The NPs showed good stability, high entrapment efficiency and a reasonable release behavior, meanwhile their high resistance against enzymatic degradation was verified. Furthermore, NPs demonstrated appropriate transfection efficiency and could induce apoptosis, so had toxicity in 4T1 BCE cells. Also, CD47 expression on the surface of cancer cells was significantly reduced after treatment with miR-340 CNPs. The results showed that miR-340 CNPs augmented the expression of P-27 in BC cells. Furthermore, miR-340 CNPs caused down-regulation of BRP-39 (breast regression protein-39) increasingly suggested as a prognostic biomarker for neoplastic diseases like BC. In conclusion, our data show that miR-340 CNPs can be considered as a promising new platform for BC gene therapy.

Fabrication of controlled-release silver nanoparticle polylactic acid microneedles with long-lasting antibacterial activity using a micro-molding solvent-casting technique.

Most topical drug delivery techniques do not provide therapeutic concentrations for treatment of surgical site and other local infections and, therefore, require some kind of enhancement, such as physical methods like microneedles, the subject of the present investigation. Here, controlled-release long-lasting antibacterial polylactic acid (PLA) microneedles containing 1, 3, and 5% silver nanoparticles (AgNP) were prepared using micro-molding solvent-casting technique. Microneedles were characterized using optical microscopy, SEM, FTIR, XRD, and DSC. Also, mechanical strength, barrier disruption ability, insertion depth, in-vitro release kinetics, antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa, and silver permeation through rat skin were studied. Microneedles showed good mechanical strength with no signs of failure at an optimum PLA concentration of 25% (w/v). FTIR revealed no chemical interaction between ingredients, and XRD confirmed presence of AgNP in microneedles. Microneedles penetrated the skin model at depth of up to 1143 µm resulting 5-7 times increase in transepidermal water loss (TEWL). Release studies showed 2.2, 6.8, and 8.1 µg silver release from the whole body (obeying Higuchi's release model) and 0.33, 0.45, and 0.78 µg from the needles alone (obeying Fickian-cylindrical type release) for 1, 3, and 5% AgNP microneedles, respectively. Also, prolonged antibacterial activity (for 34 days) was observed. Skin studies over 72 h indicated that besides needles, silver is also released from the baseplate which had a marginal share in total silver permeation through the skin. In conclusion, a straightforward solvent-casting technique can be used to successfully prepare strong AgNP-containing PLA microneedles capable of long-lasting antibacterial activity.

Novel biodegradable molecularly imprinted polymer nanoparticles for drug delivery of methotrexate anti-cancer; synthesis, characterization and cellular studies.

BACKGROUND: Recently biodegradable nanoparticles are the center of attention for the development of drug delivery systems. Molecularly imprinted polymer (MIP) is an interesting candidate for designing drug nano-carriers. MIP-based nanoparticles could be used for cancer treatment and exhibited the potential to fill gaps regarding to ligand-based nanomaterials. Also, the presence of a cross-linker can play an essential role in nanoparticle stability and physicochemical properties of nanoparticles after synthesis. OBJECTIVES: In this research, a biodegradable drug delivery system based on MIP nanoparticles was prepared using a biodegradable cross-linker (dimethacryloyl hydroxylamine, DMHA) for methotrexate (MTX). A hydrolysable functional group CO-O-NH-CO was added to the crosslinking agent to increase the final biodegradability of the polymer. METHODS: Firstly, a biodegradable cross-linker was synthesized. Then, the non-imprinted polymers were prepared through mini-emulsion polymerization in the absence of a template; and efficient particle size distribution was determined. Finally, methotrexate was placed in imprinted polymers to achieve the desired MIP. Different types of MIPs were synthesized using different molar ratios of template, cross-linker, and functional monomer, and the optimal molar ratio was obtained at 1:4:20, respectively. RESULTS: HNMR successfully confirmed the chemical structure of the cross-linker. According to SEM images, nanoparticles had a spherical shape with a smooth surface. The imprinted nanoparticles showed a narrow size distribution with an average of 120 nm at a high ratio of cross-linker. The drug loading and entrapment efficiency were 6.4% and 92%, respectively. The biodegradability studies indicated that the nanoparticles prepared by DMHA had a more degradability rate than ethylene glycol dimethacrylate as a conventional cross-linker. Also, the polymer degradation rate was higher in alkaline environments. Release studies in physiological and alkaline buffer showed an initial burst release of a quarter of loaded MTX during the day and a 70% release during a week. The Korsmeyer-Peppas model described the release pattern. The cytotoxicity of MTX loaded in nanoparticles was studied on the MCF-7 cell line, and the IC50 was 3.54 µg/ml. CONCLUSION: It was demonstrated that nanoparticles prepared by DMHA have the potential to be used as biodegradable drug carriers for anticancer delivery. Synthesis schema of molecular imprinting of methotrexate in biodegradable polymer based on dimethacryloyl hydroxylamine cross-linker, for use as nanocarrier anticancer delivery to breast tumor.