Stem cell‐derived exosomes have been identified as novel cell‐free therapeutics for regenerative medicine. Three‐dimensional (3D) culture of stem cells were reported to improve their “stemness” and therapeutic efficacy. This work focused on establishing serum‐free 3D culture of dental pulp pluripotent‐like stem cells (DPPSCs)—a newly characterized pluripotent‐like stem cell for exosome production. DPPSCs were expanded in regular 2D culture in human serum‐supplemented (HS)‐medium and transferred to a micropatterned culture plate for 3D culture in HS‐medium (default) and medium supplemented with KnockOut™ serum replacement (KO‐medium). Bright‐field microscopy observation throughout the culture period (24 days) revealed that DPPSCs in KO‐medium formed spheroids of similar morphology and size to that in HS‐medium. qRT‐PCR analysis showed similar Oct4A gene expression in DPPSC spheroids in both HS‐medium and KO‐medium, but Nanog expression significantly increased in the latter. Vesicles isolated from DPPSC spheroids in KO‐medium in the first 12 days of culture showed sizes that fall within the exosomal size range by nanoparticle tracking analysis (NTA) and express the canonical exosomal markers. It is concluded that 3D culture of DPPSCs in KO‐medium provided an optimal serum‐free condition for successful isolation of DPPSC‐derived exosomes for subsequent applications in regenerative medicine.

The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data and Electroencephalography (EEG) measurements to predict outcomes over a two-year period. The study employs a multilevel fusion of data features to enhance the accuracy of the predictions. Therefore this paper suggests a hybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy brain injury (HCM-BI). A Support Vector Machine is applied with clinical details to define the Hypoxia-Ischemia outcomes of each infant. The newborn babies are assessed every two years again to know the neural development results. A selection of four attributes is derived from the Electroencephalography records, and SVM does not get conclusions regarding the classification of diseases. The final feature extraction of the EEG signal is optimized by the Bayesian Neural Network (BNN) to get the clear health condition of Hypoglycemia and Epilepsy patients. Through monitoring and assessing physical effects resulting from Electroencephalography, The Bayesian Neural Network (BNN) is used to extract the test samples with the most log data and to report hypoglycemia and epilepsy patients non-invasively. The experimental findings demonstrate that the suggested strategy improves accuracy by 95.05% and reduces the error rate to 0.41 when comparing diseases.

Mechanical alloying, especially nickel-titanium alloys, has attracted great interest recently as researchers strive to enhance the properties of nanocomposites, expand their usefulness, and how to produce them at minimal costs and produce homogeneous fine powders.In this paper, high-energy ball milling was used to produce Ti-Ni ultrafine powders. The effects of grinding parameters such as grinding tools and initial state of powders, grinding conditions such as operating times, in addition to the effect of equal size balls and the speed of the grinding machine, were investigated, while avoiding contamination of the powders in contact with air by applying silica. Milling process balls such as X-ray diffraction, SEM, EDS, plus particle size.

The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.

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