Genomic characterization of SARS-CoV-2 isolates from patients in Turkey reveals the presence of novel mutations in spike and nsp12 proteins.

Novel mutations have been emerging in the genome of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2); consequently, the evolving of more virulent and treatment resistance strains have the potential to increase transmissibility and mortality rates. The characterization of full-length SARS-CoV-2 genomes is critical for understanding the origin and transmission pathways of the virus, as well as identifying mutations that affect the transmissibility and pathogenicity of the virus. We present an analysis of the mutation pattern and clade distribution of full-length SARS-CoV-2 genome sequences obtained from specimens tested at Gazi University Medical Virology Laboratory. Viral RNA was extracted from nasopharyngeal specimens. Next-generation sequencing libraries were prepared and sequenced on Illumina iSeq 100 platform. Raw sequencing data were processed to obtain full-length genome sequences and variant calling was performed to analyze amino acid changes. Clade distribution was determined to understand the phylogenetic background in relation to global data. A total of 293 distinct mutations were identified, of which 152 missense, 124 synonymous, 12 noncoding, and 5 deletions. The most frequent mutations were P323L (nsp12), D614G (ORF2/S), and 2421C>T (5'-untranslated region) found simultaneously in all sequences. Novel mutations were found in nsp12 (V111A, H133R, Y453C, M626K) and ORF2/S (R995G, V1068L). Nine different Pangolin lineages were detected. The most frequently assigned lineage was B.1.1 (17 sequences), followed by B.1 (7 sequences) and B.1.1.36 (3 sequences). Sequence information is essential for revealing genomic diversity. Mutations might have significant functional implications and analysis of these mutations provides valuable information for therapeutic and vaccine development studies. Our findings point to the introduction of the virus into Turkey through various sources and the subsequent spread of several key variants.

Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder.

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.