Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.

Transferrin functionalized chitosan-PEG nanoparticles for targeted delivery of paclitaxel to cancer cells.

The present investigation was aimed to utilize the stealth property of polyethylene glycol (PEG) modified chitosan nanoparticles (NPs) and active targeting function of transferrin (Tf) by transferrin receptor-mediated endocytosis to promote drug delivery to cancer cells. Paclitaxel (PTX) loaded nanoparticles (PTX-NP) were prepared by solvent evaporation method; PEGylation was carried out by coupling amine group present on the surface of NPs with hydroxyl group present on the PEG (NP-PEG). Tf conjugation was carried out by coupling carboxylic group present on the surface of ligand and hydroxyl group present on the PEG (NP-PEG-Tf). The uptake of NP-PEG-Tf into cancer cells was found to be higher as compared to non-targeted NPs. Compared with free PTX, PTX-NPs and PTX-NPs-PEG, the PTX-NPs-PEG-Tf demonstrated higher cytotoxicity to human Non-Small Cell Lung cancer cell lines (HOP-62), higher intracellular uptake especially in nuclei and lower hemolytic toxicity. Tf conjugated NPs showed increased retention time in the lungs as well as in blood. These findings indicate that Tf conjugated PEGylated nanoparticles are promising nanoconstructs for the delivery of anti-cancer drugs to cancer cells.

Vesicular system: Versatile carrier for transdermal delivery of bioactives.

The transdermal route of drug delivery has gained immense interest for pharmaceutical researchers. The major hurdle for diffusion of drugs and bioactives through transdermal route is the stratum corneum, the outermost layer of the skin. Currently, various approaches such as physical approach, chemical approach, and delivery carriers have been used to augment the transdermal delivery of bioactives. This review provides a brief overview of mechanism of drug transport across skin, different lipid vesicular systems, with special emphasis on lipid vesicular systems including transfersomes, liposomes, niosomes, ethosomes, virosomes, and pharmacosomes and their application for the delivery of different bioactives.