Nanomedicine-Based Delivery Strategies for Breast Cancer Treatment and Management

Breast cancer is one of the most common types of cancer among women globally. It is caused by mutations in the estrogen/progesterone receptors and conventional treatment methods are commonly utilized. About 70–80 percent of individuals with the early-stage non-metastatic disease may be cured. Conventional treatment is far less than the optimal ratio, as demonstrated through the high mortality rate of women with this cancer. However, conventional treatment methods like surgery, radiotherapy, and chemotherapy are not as effective as expected and lead to concerns about low bioavailability, low cellular uptake, emerging resistance, and adverse toxicities. A nanomedicine-based approach is a promising alternative for breast cancer treatment. The present era is witnessing rapid advancements in nanomedicine as a platform for investigating novel therapeutic applications and modern intelligent healthcare management strategies. This paper focuses on nanomedicine-based therapeutic interventions that are becoming more widely accepted for improving treatment effectiveness and reducing undesired side effects in breast cancer patients. By evaluating the state-of-the-art tools and taking the challenges involved into consideration, various aspects of the proposed nano-enabled therapeutic approaches have been discussed in this review.     

CO2 methanation over Ni and Rh based catalysts: Process optimization at moderate temperature

H₂ was produced from aluminum/water reaction and reacted with CO₂ over Ni and Rh based catalysts to optimize the process conditions for CO₂ methanation at moderate temperature. Monometallic catalysts were prepared by incorporating Ni and Rh using nickel nitrate hexahydrate (Ni(NO₃)₂·6H₂O) and rhodium(III) chloride trihydrate (RhCl₃·3H₂O)as a precursor chemical. The preliminary study of the catalysts revealed higher activity and CH₄ selectivity for Rh based catalyst compared to that of Ni based catalyst. Further, Rh based catalyst was investigated using response surface methodology (RSM) involving central composite design. The quadratic model was employed to correlate the effects of variable parameters including methanation temperature, %humidity, and catalyst weight with the %CO₂ conversion, %CH₄ selectivity, and CH₄ production capacity. Analysis of variance revealed that methanation temperature and humidity play an important role in CO₂ methanation. Higher response values of CO₂ conversion (54.4%), CH₄ selectivity (73.5%) and CH₄ production capacity (8.4 μmol g^?1 min^?1) were noted at optimum conditions of 206.7°C of methanation temperature, 12.5% humidity and 100 mg of the catalyst. The results demonstrated the ability of Rh catalyst supported on palm shell activated carbon (PSAC) for CO₂ methanation at low temperature and atmospheric pressure.     

Stability of electronic nose (e-nose) as determined by considering date-pits heated at different temperatures

Variability and sensitivity of a portable electronic nose (32 sensors) was assessed by considering different variables for measurement (i.e., reference, standard, first and second purges, sample draw time, waiting time for the volatiles released in the headspace, and mass of sample or headspace volume of the jar containing sample). In this study, dried date-pits were used a model sample. The highest stability was achieved when both reference and standard (i.e., dried date-pits, no heating treatment) were used before test sample measurement. Higher sample draw time more than 10 s significantly decreased the stability, whereas optimum second purge was observed at 50 s. Optimum time to generate volatile was observed as 24 h. A sample of 100 g increased the signal intensity compared to the 50 g sample for the 60°C and 100°C treated samples, while an opposite trend was observed for the 150°C treated sample. Finally, the responses of volatile components in date-pits heated at different temperatures (60°C, 100°C, and 150°C) were measured using the optimum operating conditions. Principal component analysis explored the relationships between the volatile features and classified date-pits heated at different temperatures. The results showed that an electronic nose was able to classify date-pits based on their volatile components generated by different degrees of heating (93.3% accuracy).     

Optimize adaptive media playout using dynamic fuzzy logic control for video streaming

Multimedia Tools and Applications - Adaptive Media Playout (AMP) controls adapt playout rate to prevent buffer outage and to reduce delay in playout. Most AMP techniques use buffer fullness or its...     

A novel compound fast fractional integral sliding mode control and adaptive PI control of a MEMS gyroscope

Microsystem Technologies - This study considers a novel compound fast fractional integral sliding mode control and adaptive PI control (APIFFOISMC) of a MEMS gyroscope. MEMS gyroscope has been...     

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Machine Learning - Covariance estimation for high-dimensional datasets is a fundamental problem in machine learning, and has numerous applications. In these high-dimensional settings the number of...     

Efficient 3D point clouds classification for face detection using linear programming and data mining

Most of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.