Comparing the effects of Bone+B® xenograft and InterOss® xenograft bone material on rabbit calvaria bone defect regeneration.

BACKGROUND: The bone regeneration therapy is often used in patients with inadequate bone support for implants, particularly following tooth extractions. Xenografts derived from animal tissues are effective bone reconstructive options that resist resorption and pose a low risk of transmitting disease. Therefore, these implants may be a good option for enhancing and stabilizing maxillary sinuses. The purpose of this study was to compare two xenografts, Bone+B® and InterOss®, for the reconstruction of rabbit calvaria defects. METHODS AND MATERIALS: The study involved seven male New Zealand white rabbits. In the surgical procedure, 21 spots were created on both sides of the midline calvarium by creating three 8-millimeter defects. A control group was used, as well as two treatment groups utilizing Bone+B® Grafts and InterOss® Grafts. After 3 months, the rabbits were euthanized, followed by pathological evaluation. Analysis of these samples focused on bone formation, xenograft remaining material, and inflammation levels, using Adobe Photoshop CS 8.0 and SPSS version 24. RESULTS: With the application of Bone+B® graft, bone formation ranged from 32% to 45%, with a mean of 37.80% (±5.63), and the remaining material ranged from 28% to 37%, with a mean of 32.60% (±3.65). Using InterOss® grafts, bone formation was 61% to 75%, the mean was 65.83% (±4.75), and the remaining material was 9% to 18%, with a mean of 13.17% (±3.06). The bone formation in the control group ranged from 10% to 25%, with a mean of 17.17% (±6.11). InterOss® had lower inflammation levels than other groups, but the difference was not statistically significant (p > .05). CONCLUSION: InterOss® bone powder is the best option for maxillofacial surgery and bone reconstruction. This is due to more bone formation, less remaining material, and a lower inflammation level. Compared to the control group, Bone+B® improves healing and bone quality, thus making it an alternative to InterOss®.

A systems biology approach for discovering the cellular and molecular aspects of psychogenic non-epileptic seizure.

Objectives: Psychogenic non-epileptic seizure (PNES) is the most common non-epileptic disorder in patients referring to epilepsy centers. Contrary to common beliefs about the disease's harmlessness, the death rate of PNES patients is similar to drug-resistant epilepsy. Meanwhile, the molecular pathomechanism of PNES is unknown with very limited related research. Thus, the aim of this in silico study was to find different proteins and hormones associated with PNES via a systems biology approach. Methods: Different bioinformatics databases and literature review were used to find proteins associated with PNES. The protein-hormone interaction network of PNES was constructed to discover its most influential compartments. The pathways associated with PNES pathomechanism were found by enrichment analysis of the identified proteins. Besides, the relationship between PNES-related molecules and psychiatric diseases was discovered, and the brain regions that could express altered levels of blood proteins were discovered. Results: Eight genes and three hormones were found associated with PNES through the review process. Proopiomelanocortin (POMC), neuropeptide Y (NPY), cortisol, norepinephrine, and brain-derived neurotrophic factor (BDNF) were identified to have a high impact on the disease pathogenesis network. Moreover, activation of Janus kinase-signaling transducer and activator of transcription (JAK-STAT) and JAK, as well as signaling of growth hormone receptor, phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT), and neurotrophin were found associated with PNES molecular mechanism. Several psychiatric diseases such as depression, schizophrenia, and alcohol-related disorders were shown to be associated with PNES predominantly through signaling molecules. Significance: This study was the first to gather the biochemicals associated with PNES. Multiple components and pathways and several psychiatric diseases associated with PNES, and some brain regions that could be altered during PNES were suggested, which should be confirmed in further studies. Altogether, these findings could be used in future molecular research on PNES patients.

Machine learning applications to differentiate comorbid functional seizures and epilepsy from pure functional seizures.

PURPOSE: We have utilized different methods in machine learning (ML) to develop the best algorithm to differentiate comorbid functional seizures (FS) and epilepsy from those who have pure FS. METHODS: This was a retrospective study of an electronic database of patients with seizures. All patients with a diagnosis of FS (with or without comorbid epilepsy) were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021. We arbitrarily selected 14 features that are important in making the diagnosis of patients with seizures and also are easily obtainable during history taking. Pytorch and Scikit-learn packages were used to construct various models including random forest classifier, decision tree classifier, support vector classifier, k-nearest neighbor, and TabNet classifier. RESULTS: Three hundred and two patients had FS (82.5%), while 64 patients had FS and comorbid epilepsy (17.5%). The "TabNet classifier" could provide the best sensitivity (90%) and specificity (74%) measures (accuracy of 76%) to help differentiate patients with FS from those with FS and comorbid epilepsy. CONCLUSION: These satisfactory differentiating measures suggest that the current algorithm could be used in clinical practice to help with the difficult task of distinguishing patients with FS from those with FS and comorbid epilepsy. Based on the results of the current study, we have developed an Application (SeiDx). This App is freely accessible at the following address: https://drive.google.com/file/d/1rAgBXKNPW9bmUCDioaGHHzLBQgzZ-HZ2/view. This App should be validated in a prospective assessment.