Breast cancer is one of the leading causes of cancer-related mortality in women worldwide, highlighting the importance of effective therapies. This study evaluates the interaction between camptothecin, a potent anticancer agent, and two key receptors implicated in breast cancer progression: HER2 (human epidermal growth factor receptor 2) and EGFR (epidermal growth factor receptor), using molecular docking. The results reveal a stronger binding affinity between camptothecin and HER2 than EGFR, in contrast to neratinib, which demonstrated affinity exclusively for HER2. Camptothecin exhibits significant hydrophobic and pi-alkyl interactions with HER2, whereas its interactions with EGFR are primarily mediated by hydrogen bonds. Molecular dynamics (MD) simulations of the camptothecin-HER2 complex indicate stable binding, with minimal fluctuations observed over 100 nanoseconds, confirming the stability of the ligand–receptor interaction. Pharmacokinetic evaluations, based on Lipinski’s rule of five, demonstrate that camptothecin adheres to essential drug-likeness parameters, suggesting favorable bioavailability. Furthermore, the analysis comparing the pharmacological properties of camptothecin with other well-known anticancer compounds, such as neratinib, shows that camptothecin exhibited superior compliance with drug-likeness rules. Despite its low solubility, the binding stability and pharmacokinetic profile suggest its potential as an effective therapeutic agent for breast cancer, particularly when combined with drug delivery systems that enhance solubility. This work underscores the importance of receptor-specific ligand interactions in drug design and highlights the need for further studies into camptothecin’s clinical applications, especially in HER2-positive breast cancer treatment.

A composite material composed of anodized aluminum oxide (AAO), carbon (C), and magnesium oxide (MgO) was developed for CO₂ capture applications. Inspired by the bryophyte organism, the AAO/C/MgO composite mirrors two primary features of these species—(1) morphological characteristics and (2) elemental composition—specifically carbon, oxygen, and magnesium. The synthesis process involved two sequential steps: electroanodization of aluminum foil followed by a hydrothermal method using a mixture of glucose and magnesium chloride (MgCl₂). The concentration of MgCl₂ was systematically varied as the sole experimental variable across five levels—1 mM, 2 mM, 3 mM, 4 mM, and 5 mM—to investigate the impact of MgO formation on the samples’ chemical and physical properties, and consequently, their CO₂ capture efficiency. Thus, scanning electron microscopy analysis revealed the AAO substrate’s porous structure, with pore diameters measuring 250 ± 30 nm. The growth of MgO on the AAO substrate resulted in spherical structures, whose diameter expanded from 15 nm ± 3 nm to 1000 nm ± 250 nm with increasing MgCl₂ concentration from the minor to major concentrations explored, respectively. X-ray photoelectron spectroscopy (XPS) analysis indicated that carbon serves as a linking agent between AAO and MgO within the composite. Notably, the composite synthesized with a 4 mM MgCl₂ concentration exhibited the highest CO₂ capture efficiency, as determined by UV-Vis absorbance studies using a sodium carbonate solution as the CO₂ source. This efficiency was quantified with a ‘k’ constant of 0.10531, significantly higher than those of other studied samples. The superior performance of the 4 mM MgCl₂ sample in CO₂ capture is likely due to the optimal density of MgO structures formed on the sample’s surface, enhancing its adsorptive capabilities as suggested by the XPS results.

Borophene, a two-dimensional (2D) allotrope of boron, has emerged as a highly promising material owing to its exceptional mechanical strength, electronic conductivity, and diverse structural phases. Unlike graphene and other 2D materials, borophene exhibits inherent anisotropy, flexibility, and metallicity, offering unique opportunities for advanced nanotechnological applications. This review presents a comprehensive summary of recent progress in borophene synthesis methods, highlighting both bottom–up strategies such as chemical vapor deposition (CVD) and molecular beam epitaxy (MBE), and top–down approaches, including liquid-phase exfoliation and sonochemical techniques. A key challenge discussed is the stabilization of borophene’s polymorphs, as bulk boron’s non-layered structure complicates exfoliation. The influence of substrates and doping strategies on structural stability and phase control is also explored. Moreover, the intrinsic physicochemical properties of borophene, including its high flexibility, oxidation resistance, and anisotropic charge transport, were examined in relation to their implications for electronic, catalytic, and sensing devices. Particular attention was given to borophene’s performance in hydrogen storage and hydrogen evolution reactions (HERs), where functionalization with alkali and transition metals significantly enhances H₂ adsorption energy and storage capacity. Studies demonstrate that certain borophene–metal composites, such as Ti- or Li-decorated borophene, can achieve hydrogen storage capacities exceeding 10 wt.%, surpassing the U.S. Department of Energy targets for hydrogen storage materials. Despite these promising characteristics, large-scale synthesis, long-term stability, and integration into practical systems remain open challenges. This review identifies current research gaps and proposes future directions to facilitate the development of borophene-based energy solutions. The findings support borophene’s strong potential as a next-generation material for clean energy applications, particularly in hydrogen production and storage systems.

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85–87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

In this research, multi-walled carbon nanotubes (MWCNTs) were decorated with two kinds of nanostructures, (1) silver nanoparticles (AgNPs) and (2) zinc oxide–silver nano-heterostructures (ZnO/Ag-NHs), via an accessible chemical coprecipitation method assisted with ultrasonic radiation. The high-resolution transmission electron microscopy analysis demonstrated the successful decoration of MWCNTs with the nanostructures with a diameter size of 11 nm ± 2 nm and 46 nm ± 5 nm for the AgNPs and the ZnO/Ag-NHs, respectively. The reactive species were promoted in an aqueous medium assisted with UV irradiation on the functionalized MWCNT. UV-Vis spectroscopy demonstrated that production of the reactive species density increased 4.07 times, promoted by the single MWCNT after the functionalization. X-ray photoelectron spectroscopy showed that Sp² hybridization in carbon atoms of MWCNTs participates in the binding of AgNPs and ZnO/Ag-NH decoration and thus participates in the formation of reactive species in an aqueous medium, as is the case for cancer cells.

This study presents the design, simulation, and prototype creation of a quadruped robot inspired by the Acinonyx jubatus (cheetah), specifically designed to replicate its distinctive walking, trotting, and galloping locomotion patterns. Following a detailed examination of the cheetah’s skeletal muscle anatomy and biomechanics, a simplified model of the robot with 12 degrees of freedom was conducted. The mathematical transformation hierarchy model was established, and direct kinematics were simulated. A bio-inspired control approach was introduced, employing a Central Pattern Generator model based on Wilson–Cowan neural oscillators for each limb, interconnected by synaptic weights. This approach assisted in the simulation of oscillatory signals for relative phases corresponding to four distinct gaits in a system-level simulation platform. The design phase was conducted using CAD software (SolidWorks 2018), resulting in a 1:3-scale robot mirroring the cheetah’s actual proportions. Movement simulations were performed in a virtual mechanics software environment, leading to the construction of a prototype measuring 35.5 cm in length, 21 cm in width, 27 cm in height (when standing), and weighing approximately 2.1 kg. The experimental validation of the prototype’s limb angular positions and trajectories was achieved through the image processing of video-recorded movements, demonstrating a high correlation (0.9025 to 0.9560) in joint angular positions, except for the knee joint, where a correlation of 0.7071 was noted. This comprehensive approach from theoretical analysis to practical implementation showcases the potential of bio-inspired robotics in emulating complex biological locomotion.