Neurologische Störungen

Antimicrobial agents are the most frequently used pharmacological agents by a practising clinician. Many of the commonly used antibiotics are well tolerated. They do have side effects either idiosyncratic or dose dependent. Special caution is needed on monitoring their neurotoxic effects as if not recognized promptly, these may be confused with other neurological states therefore ending up in complications. The practising clinician in most countries, initiates’ treatment based upon clinical findings and does not have the luxury of getting confirmatory laboratory results in a timely manner. Factors influence neurotoxicity include old age, decreased renal function, nutritional status, use of other drugs that lower seizure threshold and damage to the blood brain barrier. The role of genetic factors though has been suggested, their effects have not been fully determined. In most instances, the neurological side effects can be reversed by discontinuation of the offending drug. This article highlights the neurological side effects of commonly used antimicrobial agents, thereby enabling clinicians to be aware of these side effects to diagnose and initiate proper treatment apart from stopping the drug in a timely manner.

A learning disability (LDs) is a comprehensive word used for various learning problems. Children with learning disabilities are not sluggish or intelligently retarded. Learning disability is a neurological condition that is characterized by a vague understanding of words and poor reading skills. It affects many schoolaged children, with fellows being more likely to be involved, placing them at risk for deprived academic concerts and low self-esteem for the rest of their lives. Our research entails developing a machine learning model to analyse EEG signals from people with learning difficulties and provide results in minutes with the highest level of accuracy. In this research, we have used Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) methods were used for component analyse of the dataset. For classification purposes we have used Support Vector Machines (SVM), Random Forest (RF), Logistic Regression (LR), K-nearest neighbours (K-NN), Decision Trees and XGBoost, etc., different types of algorithms. The goal is to determine which data pre-processing approaches and machine learning algorithms are the most effective in detecting learning disabilities.

The following review is an introduction to the terminology and concepts related to blockchain and metaverse platforms including AR, VR, XR-realities, NFTs, operating systems, and security protocols. Methods of overcoming risks, limitations and challenges are explored. Lastly, this review explores current and future applications of blockchain and metaverse platforms in healthcare, including a comprehensive and concise evaluation of various benefits of these advancing systems in the neuropsychiatric setting.

Background: The ability to predict coming seizures will improve the quality of life of patients with epilepsy. Analysis of brain electrical activity using multivariate sequential signals can be used to predict seizures.

Methods: Seizure prediction can be regarded as a classification problem between interictal and preictal EEG signals. In this work, hospital multivariate sequential EEG signals were transformed into multidimensional input, multidimensional convolutional neural network models were constructed to predict seizures several channels segments were extracted from the interictal and preictal time duration and fed them to the proposed deep learning models.

Results: The average accuracy of multidimensional deep network model for multi-channel EEG data is about 94%, the average sensitivity is 88.47%, and the average specificity is 89.75%.

Conclusion: This study combines the advantages of multivariate sequential signals and multidimensional convolution network for EEG data analysis to predict epileptic seizures, thereby enabling early warning before epileptic seizures in clinical applications.

Background: The integration of genetic code from RNA viruses into host DNA, once thought to be a rare or even impossible phenomenon, is now recognized as probable. The Long Interspersed Nuclear Element (LINE)-1 mediated mechanism of insertion implies that many viral RNAs (apart from retroviral) can be reverse transcribed and then stably incorporated into DNA. Recombination between exogenous non-retroviral RNA and endogenous retroviral sequences that leads to reverse transcription and finally integration of the resulting cDNA into the host genome has been described.

Recent data demonstrate that SARS-CoV-2 RNA sequences can be transcribed into DNA and may be actively integrated into the genome of affected human cells, mediated by retrotransposons. In some SARS-CoV-2 infected patient specimens, there is evidence for a large fraction SARS-CoV-2 sequence integration and subsequent generation of SARS-CoV-2 human chimeric transcripts.

Results: In this review, the potential role of mobile genetic elements in the etiopathogenesis of neurological, cardiovascular, immunological, and oncological disease and the possibilities of human DNA interference by SARS-CoV-2 infection and vaccination are explored. Vulnerable germ line cells, cancer cells, and neurons can presumably all be targets for anomalous mRNA integration, especially in aging cells that show increased LINE-1 activity compared to younger cells.