Machine learning- a new paradigm in nanoparticle-mediated drug delivery to cancerous tissues through the human cardiovascular system enhanced by magnetic field.

Nanoparticle-mediated drug delivery offers a promising approach to targeted cancer therapy, leveraging the ability of nanoparticles to deliver therapeutic agents directly to cancerous tissues with minimal impact on surrounding healthy cells. The presence of these nanoparticles is governed by a concentration equation, which accounts for the diffusion, convection, and reaction of the nanoparticles with the blood components. It is well-known that whenever a disease or infection occurs in a human, in 80% of cases a rise in the concentration of hydrogen peroxide in the blood occurs. This is the reason why blood is assumed to contain hydrogen peroxide (in the present study), which is a biomarker of oxidative stress and inflammation. This study explores the integration of machine learning (ML) techniques into the optimization of drug delivery processes within the human cardiovascular system, focusing on the enhancement of these processes through the application of magnetic fields. By employing ML algorithms, we analyze and predict the behavior of nanoparticles as they navigate the complex fluid dynamics of the cardiovascular system, particularly under the influence of an external magnetic field. The predictive power of ML models enables the precise control of nanoparticle trajectories, optimizing their accumulation in cancerous tissues and improving the efficacy of the drug delivery system. The findings of this study demonstrate that ML-enhanced magnetic targeting can significantly enhance the precision and effectiveness of nanoparticle-mediated drug delivery, offering a new paradigm in cancer treatment strategies. This approach has the potential to revolutionize the field by providing personalized and highly efficient therapeutic solutions for cancer patients.

An Investigation on the Potential of Utilizing Aluminum Alloys in the Production and Storage of Hydrogen Gas.

The interest in hydrogen is rapidly expanding because of rising greenhouse gas emissions and the depletion of fossil resources. The current work focuses on employing affordable Al alloys for hydrogen production and storage to identify the most efficient alloy that performs best in each situation. In the first part of this work, hydrogen was generated from water electrolysis. The Al alloys that are being examined as electrodes in a water electrolyzer are 1050-T0, 5052-T0, 6061-T0, 6061-T6, 7075-T0, 7075-T6, and 7075-T7. The flow rate of hydrogen produced, energy consumption, and electrolyzer efficiency were measured at a constant voltage of 9 volts to identify the Al alloy that produces a greater hydrogen flow rate at higher process efficiency. The influence of the electrode surface area and water electrolysis temperature were also studied. The second part of this study examines these Al alloys' resistance to hydrogen embrittlement for applications involving compressed hydrogen gas storage, whether they are utilized as the primary vessel in Type 1 pressure vessels or as liners in Type 2 or Type 3 pressure vessels. Al alloys underwent electrochemical charging by hydrogen and Charpy impact testing, after which a scanning electron microscope (SEM) was used to investigate the fracture surfaces of both uncharged and H-charged specimens. The structural constituents of the studied alloys were examined using X-ray diffraction analysis and were correlated to the alloys' performance. Sensitivity analysis revealed that the water electrolysis temperature, electrode surface area, and electrode material type ranked from the highest to lowest in terms of their influence on improving the efficiency of the hydrogen production process. The 6061-T0 Al alloy demonstrated the best performance in both hydrogen production and storage applications at a reasonable material cost.

Evaluation of an ergonomically designed schoolbag: Heart rate variability and body discomfort rating.

BACKGROUND: Unsuitable schoolbags may stress the spine and promote poor body posture, particularly for school students. Global recommendations have suggested that schoolbag weight must not exceed 10% of a healthy student's body mass, which would need continuous monitoring and enforcement. OBJECTIVES: The present study presents a comparison between an ergonomically designed schoolbag, which helps reduce the potential effects of carrying a load, and a commercial one. METHODS: A total of 30 healthy male students were recruited for this experiment. Independent variables determined were schoolbag type (ergonomically designed and commercial schoolbags) and three load levels based on body mass percentage (i.e., 10%, 15%, and 20% of body mass). Heart rate variability (HRV) and body discomfort rating were then measured. RESULTS: Our results showed that the developed schoolbag promoted enhanced subjective measures and HRV response at 15% and 20% of body mass. Participants who wore the developed schoolbags experienced significantly lesser neck, shoulder, upper and lower trunk discomfort than those who wore the traditional ones. Changing the load percentage from 10% to 15% caused an increase in heart rate among participants carrying a commercial schoolbag but a decrease in heart rate among those carrying the developed schoolbag. CONCLUSIONS: The findings presented herein suggest introducing strategies for reducing the potential impact of load carrying through the combined effect of new educational inventions and policy changes.

Insight into chitosan/zeolite-A nanocomposite as an advanced carrier for levofloxacin and its anti-inflammatory properties; loading, release, and anti-inflammatory studies.

Chitosan/zeolite-A nanocomposite (CH/ZA) was synthesized as a potential carrier for levofloxacin (LVOX) of enhanced technical properties. The CH/ZA composite displayed enhanced loading capacity (425 mg/g) as compared to chitosan (188.8 mg/g) and zeolite-A (234.6 mg/g). The loading behavior follows Pseudo-Second-order and Langmuir as kinetic and isotherm models. The equilibrium studies, Gaussian energy (8.15 KJ/mol), and thermodynamic parameters demonstrate homogenous and monolayer loading by complex chemical and physical reactions that are of spontaneous and exothermic nature. The CH/ZA composite is of slow and continuous release profile (200h) with 94.3% as the maximum release percentage. The release reactions are of non-Fickian behavior involving both diffusion and erosion mechanisms. The loading of LVOX into CH/ZA induced its anti-inflammatory effect against the cytokine production (IL-6 and IL-8) within the human bronchial epithelia cells (NL20). The cytotoxicity studies on the normal cells demonstrated a high safety value for the composite.

Insight into chitosan/mesoporous silica nanocomposites as eco-friendly adsorbent for enhanced retention of U (VI) and Sr (II) from aqueous solutions and real water.

The chitosan chains were integrated with MCM-48 mesoporous silica in an eco-friendly composite (CH/MCM-48) of enhanced adsorption capacity. The prepared CH/MCM-48 composite was applied in systematic retention of U (VI) as well as Sr (II) ions from water as the commonly detected radioactive pollutants. It displayed promising retention capacities of 261.3 mg/g and 328.6 mg/g for U (VI) and Sr (II) considering the equilibrium time interval that was identified after 420 min. The composite showed the kinetic behavior of the Pseudo-First order model and the isotherm properties of the Langmuir assumption. The thermodynamic assessment of the reactions validated the retention of both U (VI) and Sr (II) ions by spontaneous, favorable, and exothermic reactions. Based on the theoretical values of entropy (-5.94 kJ mol-1 (U (VI)) and -2.93 kJ mol-1 (Sr (II))), Gibbs free energy (less than 20 kJ mol-1), and Gaussian energy (5.77 kJ mol-1 (U (VI)) and 4.56 kJ mol-1 (Sr (II))) the uptake processes are related to physical adsorption reactions. The CH/MCM-48 composite is of significant recyclability and showed considerable affinities for the studied radioactive ions even in the presence of other metal ions (Cd (II), Pb (II), Zn (II), and Co (II)).

Driver Missense Mutation Identification Using Feature Selection and Model Fusion.

Driver mutations propel oncogenesis and occur much less frequently than passenger mutations. The need for automatic and accurate identification of driver mutations has increased dramatically with the exponential growth of mutation data. Current computational solutions to identify driver mutations rely on sequence homology. Here we construct a machine learning-based framework that does not rely on sequence homology or domain knowledge to predict driver missense mutations. A windowing approach to represent the local environment of the sequence around the mutation point as a mutation sample is applied, followed by extraction of three sequence-level features from each sample. After selecting the most significant features, the support vector machine and multimodal fusion strategies are employed to give final predictions. The proposed framework achieves relatively high performance and outperforms current state-of-the-art algorithms. The ease of deploying the proposed framework and the relatively accurate performance make this solution applicable to large-scale mutation data analyses.

Wavelet analysis in current cancer genome research: a survey.

With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences.