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Sajid Ali,1 Muhammad Umair Amin,1,2 Imran Tariq,1,3 Muhammad Farhan Sohail,4,5 Muhammad Yasir Ali,1,6 Eduard Preis,1 Ghazala Ambreen,1 Shashank Reddy Pinnapireddy,1 Jarmila Jedelská,1 Jens Schäfer,1 Udo Bakowsky1 1Department of Pharmaceutics and Biopharmaceutics, University of Marburg, Marburg, Germany; 2Faculty of Pharmacy, The University of Lahore, Lahore, Pakistan; 3Punjab University College of Pharmacy, University of the Punjab, Allama Iqbal Campus, Lahore, Pakistan; 4Riphah Institute of Pharmaceutical Sciences (RIPS), Riphah International University, Lahore, Pakistan; 5Department of Pharmacy, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark; 6Faculty of Pharmaceutical Sciences, GC University Faisalabad, Faisalabad, PakistanCorrespondence: Udo BakowskyDepartment of Pharmaceutics and Biopharmaceutics, University of Marburg, Robert Koch Str. 4, Marburg, 35037, GermanyTel + 49 6421 28 25884Fax + 49 6421 28 27016Email ubakowsky@aol.comPurpose: Lipoparticles are the core-shell type lipid-polymer hybrid systems comprising polymeric nanoparticle core enveloped by single or multiple pegylated lipid layers (shell), thereby melding the biomimetic properties of long-circulating vesicles as well as the mechanical advantages of the nanoparticles. The present study was aimed at the development of such an integrated system, combining the photodynamic and chemotherapeutic approaches for the treatment of multidrug-resistant cancers.Methods: For this rationale, two different sized Pirarubicin (THP) loaded poly lactic-co-glycolic acid (PLGA) nanoparticles were prepared by emulsion solvent evaporation technique, whereas liposomes containing Temoporfin (mTHPC) were prepared by lipid film hydration method. Physicochemical and morphological characterizations were done using dynamic light scattering, laser doppler anemometry, atomic force microscopy, and transmission electron microscopy. The quantitative assessment of cell damage was determined using MTT and reactive oxygen species (ROS) assay. The biocompatibility of the nanoformulations was evaluated with serum stability testing, haemocompatibility as well as acute in vivo toxicity using female albino (BALB/c) mice.Results and Conclusion: The mean hydrodynamic diameter of the formulations was found between 108.80 ± 2.10 to 405.70 ± 10.00 nm with the zeta (ζ) potential ranging from − 12.70 ± 1.20 to 5.90 ± 1.10 mV. Based on the physicochemical evaluations, the selected THP nanoparticles were coated with mTHPC liposomes to produce lipid-coated nanoparticles (LCNPs). A significant (p< 0.001) cytotoxicity synergism was evident in LCNPs when irradiated at 652 nm, using an LED device. No incidence of genotoxicity was observed as seen with the comet assay. The LCNPs decreased the generalized in vivo toxicity as compared to the free drugs and was evident from the serum biochemical profile, visceral body index, liver function tests as well as renal function tests. The histopathological examinations of the vital organs revealed no significant evidence of toxicity suggesting the safety and efficacy of our lipid-polymer hybrid system.Keywords: atomic force microscopy, FACS, in vivo, in vitro cytotoxicity, lipid-polymer hybrid nanoparticles, photodynamic therapy, PLGA nanoparticles, TEM

This research paper introduces an innovative approach to predicting failures in Local Area Network (LAN) switches, combining Double Exponential Smoothing (DES) with a suite of Machine Learning (ML) algorithms including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM). The primary objective of this study is to enhance the accuracy and timeliness of LAN switch failure predictions, thereby facilitating more proactive and effective network management. Our methodology involves the integration of DES for trend analysis and forecasting in time-series data, with the advanced predictive capabilities of the aforementioned ML algorithms. This hybrid approach not only leverages the strengths of DES in identifying underlying patterns in failure data but also capitalizes on the diverse predictive models to handle various aspects of failure prediction more robustly. The paper details the process of data collection, preprocessing, and the specific application of DES and each ML algorithm to the dataset. A notable contribution of this research is the development of a framework that effectively combines the output of DES with ML models, leading to a significant improvement in predictive accuracy as compared to traditional methods. Through rigorous testing and validation; the proposed approach demonstrated a marked increase in the precision and reliability of failure predictions. The results indicate that the integration of DES with ML algorithms can substantially aid in preemptive maintenance and decision-making processes in LAN management. The implications of these findings are profound, suggesting that such a combined approach can greatly enhance network stability and efficiency. While the focus of this study is on LAN switches, the methodology has the potential for broader applications in various fields of network management and predictive maintenance.

In the face of increasing global disruptions, the cybersecurity field is confronting rising threats posed by offensive groups and individual hackers. Traditional security measures often fall short in detecting and mitigating these sophisticated attacks, necessitating advanced intrusion detection methods. The goal of our study is to develop robust network intrusion detection methods using machine learning techniques. In addition, we evaluate the effectiveness of various machine learning models in detecting network intrusions. Model performances are optimized through hyperparameter tuning and feature selection. A range of classification and clustering models have been employed. Data from SIEM systems capturing real-time statistics from cloud-hosted Windows virtual machines has been gathered and augmented with web attack logs from CICIDS2017, each comprising approximately fifteen thousand rows. Hyperparameter tuning, data normalization, standardization and feature selection techniques for model optimization have been used in our study. The research showcases the potential of machine learning in enhancing network intrusion detection capabilities. The findings underscore the effectiveness of the Random Forest Classifier (0.97) and highlight the importance of utilizing diverse datasets and advanced optimization techniques. This study offers valuable insights and sets a foundation for future advancements in cybersecurity strategies and intrusion detection systems.

Spinal cord injuries affect nearly five to ten individuals per million every year. Spinal cord injury causes damage to the nerves, muscles, and the tissue surrounding the spinal cord. Depending on the severity, spinal injuries are linked to degeneration of axons and myelin, resulting in neuronal impairment and skeletal muscle weakness and atrophy. The protection of neurons and promotion of myelin regeneration during spinal cord injury is important for recovery of function following spinal cord injury. Current treatments have little to no effect on spinal cord injury and neurogenic muscle loss. Clemastine, an Food and Drug Administration-approved antihistamine drug, reduces inflammation, protects cells, promotes remyelination, and preserves myelin integrity. Recent clinical evidence suggests that clemastine can decrease the loss of axons after spinal cord injury, stimulating the differentiation of oligodendrocyte progenitor cells into mature oligodendrocytes that are capable of myelination. While clemastine can aid not only in the remyelination and preservation of myelin sheath integrity, it also protects neurons. However, its role in neurogenic muscle loss remains unclear. This review discusses the pathophysiology of spinal cord injury, and the role of clemastine in the protection of neurons, myelin, and axons as well as attenuation of skeletal muscle loss following spinal cord injury.

Several studies have investigated the association between strength and speed, showing that stronger athletes perform better during sprint performances (Baker & Nance, 1999). Moreover, the aim of this study was to evaluate the correlation between sprint performance and anthropometric parameters. Subjects were 32 youth soccer players. The age of participants was 15.1±0.3 years. Speed time (50 m sprint) was evaluated during sprint test, and anthropometric parameters were measured (weight, height, percent body fat). Correlation analysis (Pearson test) was performed to evaluate the correlation between speed and anthropometrics. Results showed correlation between body weight and speed (r=-0.041 Sig=0.834); BMI values and speed (r=0.231; Sig=0.236), body height and speed (r=-0.384; Sig=0.044); percent body fat and speed (r=0.440; Sig=0.019).In conclusion, the results of this study show no significance association between body weight and BMI with sprint performance and significance correlation between body height (negative correlation) and percent body fat (positive correlation) with speed.