Atomic interaction between mediator protein of human prostate cancer (PHPC) and Fe/C720 Buckyballs-Statin is important for medical science. For the first time, we use molecular dynamics (MD) approach based on Newton’s formalism to describe the destruction of PHPC via Fe/C720 Buckyballs-Statin with atomic accuracy. In this work, the atomic interaction of PHPC and Fe/C720 Buckyballs-Statin introduced via equilibrium molecular dynamics approach. In this method, each PHPC and Fe/C720 Buckyballs-Statin is defined by C, H, Cl, N, O, P, S, and Fe elements and contrived by universal force field (UFF) and DREIDING force-field to introduce their time evolution. The results of our studies regarding the dynamical behavior of these atom-base compounds have been reported by calculating the Potential energy, center of mass (COM) position, diffusion ratio and volume of defined systems. The estimated values for these quantities show the attraction force between Buckyball-based structure and protein sample, which COM distance of these samples changes from 10.27 Å to 2.96 Å after 10 ns. Physically, these interactions causing the destruction of the PHPC. Numerically, the volume of this biostructure enlarged from 665,276 Å3 to 737,143 Å3 by MD time passing. This finding reported for the first time which can be considered by the pharmaceutical industry. Simulations indicated the volume of the PHPC increases by Fe/C720 Buckyballs-Statin diffusion into this compound. By enlarging this quantity (diffusion coefficient), the atomic stability of PHPC decreases and protein destruction procedure fulfilled.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Cancer is the 3rd leading cause of death globally, and the countries with low-to-middle income account for most cancer cases. The current diagnostic tools, including imaging, molecular detection, and immune histochemistry (IHC), have intrinsic limitations, such as poor accuracy. However, researchers have been working to improve anti-cancer treatment using different drug delivery systems (DDS) to target tumor cells more precisely. Current advances, however, are enough to meet the growing call for more efficient drug delivery systems, but the adverse effects of these systems are a major problem. Nanorobots are typically controlled devices made up of nanometric component assemblies that can interact with and even diffuse the cellular membrane due to their small size, offering a direct channel to the cellular level. The nanorobots improve treatment efficiency by performing advanced biomedical therapies using minimally invasive operations. Chemotherapy’s harsh side effects and untargeted drug distribution necessitate new cancer treatment trials. The nanorobots are currently designed to recognize 12 different types of cancer cells. Nanorobots are an emerging field of nanotechnology with nanoscale dimensions and are predictable to work at an atomic, molecular, and cellular level. Nanorobots to date are under the line of investigation, but some primary molecular models of these medically programmable machines have been tested. This review on nanorobots presents the various aspects allied, i.e., introduction, history, ideal characteristics, approaches in nanorobots, basis for the development, tool kit recognition and retrieval from the body, and application considering diagnosis and treatment.

This article explores ethnomedicine or traditional medication written in palm-leaf manuscripts in Lombok, West Nusa Tenggara. Most of the manuscripts in Lombok are written on palm leaf or lontar. One genre of palm-leaf manuscripts is USADA or traditional medication. These USADA manuscripts, serving as guides for traditional healers (dukun and balian), contain valuable information on diseases, treatments, herbal remedies, and incantations. The study reveals that ethnomedicine remains a relevant and respected practice in Lombok, often considered on par with modern medicine. Many residents rely on traditional healers for healthcare, particularly for minor illnesses. The research also highlights the living nature of the manuscript tradition, with continued recitation, copying, and teaching of these texts to younger generations. However, challenges persist in preserving these manuscripts and promoting wider appreciation for this unique cultural heritage.