Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding-An Interdisciplinary Project.

Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient's identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.

Isolation of Cells and Exosomes from Glioblastoma Tissue to Investigate the Effects of Ascorbic Acid on the c-Myc, HIF-1α, and Lnc-SNHG16 Genes.

Glioblastoma multiforme (GBM) is incurable with routine treatments. Ascorbic acid (Asc) has antioxidant and anti-cancer properties. However, its specific anti-cancer mechanisms are only partially understood. In this study, the effect of Asc on the c-Myc, HIF-1α, and lnc-SNHG16 genes in GBM cells and their exosomes was investigated. Cells isolated from the tissue were characterized by the immunocytochemistry method (GFAP+). The cell-doubling time was determined, and FBS-free medium supplemented with Asc (5 mM) was added to the cells. The extracted exosomes in the cell culture medium were scanned by electron microscopy, Zetasizer, and BCA assay. The expression of lnc-SNHG16 in the exosomes and c-Myc and HIF-1α in the treated and control cells was evaluated by real-time PCR. The interactions between Asc and the c-Myc and HIF-1α proteins were studied using the molecular docking method. The cells showed 90-100% GFAP+ in passage 4, with a cell-doubling time of 4.8 days. Exosomal vesicles measuring 98.25-105.9 were observed. Zetasizer results showed a sharp pick at 90 nm. Protein quantitation showed 3.812 µg/ml protein in the exosomes. Lnc-SNHG16 expression was reduced (P = 0.041), and c-Myc was upregulated (P = 0.002). The expression of HIF-1α was not significant in the treated cells. Also, Asc was able to interact and affect c-Myc and HIF-1α. Asc exerts its effect by reducing lnc-SNHG16 expression in exosomes, upregulating c-Myc in GBM cells, and interacting with HIF-1α and c-Myc. Further research is necessary to achieve a full understanding of these findings.

Efficacy of Anti-VEGF Drugs Based Combination Therapies in Recurrent Glioblastoma: Systematic Review and Meta-Analysis.

BACKGROUND: Recurrent glioblastoma multiforme (rGBM) has a grim prognosis with current therapies offering no survival benefit. Several combination therapies involving anti-VEGF agents have been studied with mixed results. METHODS: A systematic search was performed using five electronic databases: PubMed, Scopus, ISI, Embase, and the Cochrane Library without language limitations. The primary outcome of interest was progression free survival (PFS). Secondary outcomes were overall survival (OS), objective response ratio (ORR), and grade ≥ 3 adverse events. Estimates for PFS, OS were calculated as random effects hazard ratio (HR) with 95% confidence intervals (CIs) using the generic inverse variance method. Estimates for ORR, grade ≥ 3 adverse events were calculated using a random-effects risk ratio (RR) with 95% confidence intervals (CIs) using the Mantel-Haenszel method. RESULTS: Thirteen studies met the inclusion criteria and a total of 1994 patients have been included in the analysis. There was no statistically significant improvement in PFS (HR 0.84; 95% CI (0.68, 1.03); I2=81%), OS (HR 0.99; 95% CI (0.88, 1.12); I2=0%), ORR (RR 1.36; 95% CI (0.96, 1.92); I2=61%) in the combination therapy group when compared to the control group. Significantly higher grade ≥ 3 adverse events (RR 1.30; 95% CI (1.14, 1.48); I2=47%) were seen in the combination therapy when compared to the control group. CONCLUSION: Our analysis showed that the use of combination therapy with anti-VEGF agents did not offer any benefit in PFS, OS, or ORR. In contrast, it had significantly higher grade 3-5 adverse events. Further studies are needed to identify effective therapies in rGBM that can improve survival.

The Relation of the Viral Structure of SARS-CoV-2, High-Risk Condition, and Plasma Levels of IL-4, IL-10, and IL-15 in COVID-19 Patients Compared to SARS and MERS Infections.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) has a high mortality rate due to widespread infection and strong immune system reaction. Interleukins (ILs) are among the main immune factors contributing to the deterioration of the immune response and the formation of cytokine storms in coronavirus 2019 (COVID-19) infections. INTRODUCTION: This review article aimed at investigating the relationship between virus structure, risk factors, and patient plasma interleukin levels in infections caused by the coronavirus family. METHODS: The keywords "interleukin," "coronavirus structure," "plasma," and "risk factors" were searched to find a relationship among different interleukins, coronavirus structures, and risk factors in ISI, PUBMED, SCOPUS, and Google Scholar databases. RESULTS: Patients with high-risk conditions with independent panels of immune system markers are more susceptible to death caused by SARS-CoV-2. IL-4, IL-10, and IL-15 are probably secreted at different levels in patients with coronavirus infections despite the similarity of inflammatory markers. SARS-CoV-2 and SARS-CoV increase the secretion of IL-4, while it remains unchanged in MERS-CoV infection. MERS-CoV infection demonstrates increased IL-10 levels. Although IL-10 levels usually increase in SARS-CoV infection, different levels are recorded in SARS-CoV-2, i.e., it increases in some patients while it decreases in others. This difference may be due to factors such as the patient's condition and the pathogenicity of SARS-CoV-2. MERS-CoV increases IL-15 secretion while its levels remain unchanged in SARS-CoV-2. The levels of IL-15 in patients with SARS-CoV have not been studied. CONCLUSION: In conclusion, the different structures of SARS-CoV-2, such as length of spike or nonstructural proteins (NSPs) and susceptibility of patients due to differences in their risk factors, may lead to differences in immune marker secretion and pathogenicity. Therefore, identifying and controlling interleukin levels can play a significant role in managing the symptoms and developing individual-specific treatments.

Exploring VEGF-Linked Pathways: Investigating Multiple miRNAs for Their Therapeutic Potential in Angiogenesis Targets and as Biomarkers in Recurrent Glioblastoma Multiforme.

Alternative pathways frequently operate as the origins of resistance to drugs that block the vascular endothelial growth factor (VEGF) pathway. To find possible therapeutic targets and indicators, this study explored the VEGF pathway and how miRNAs control it in recurrent glioblastoma multiforme (rGBM). Differentially expressed miRNAs (DEmiRNAs) were identified by using GBM GSE profiles (GSE32466). To find pathways containing DEmiRNAs, VEGF pathway genes, and their related genes, DIANA-miRPath v3.0 and the ToppGene database were utilized. miRNAs linked to VEGF signaling pathway genes, interactional genes, and DEmiRNAs were discovered by extracting common pathways. The ability of these miRNAs to distinguish rGBM patients from those with primary GBM was assessed using ROC analysis. The study revealed that in rGBM, 30 miRNAs were significantly up-regulated and 49 miRNAs were considerably down-regulated. Among them, the VEGF pathway was connected to 22 up-regulated miRNAs and 29 down-regulated miRNAs. The MAPK pathway shared the most genes with the VEGF pathway, accounting for 1,014 of the interacting genes, which were discovered to have interactions with VEGF signaling pathway genes. Furthermore, 14 miRNAs were identified as having a great deal of potential as molecular biomarkers and therapeutic targets for rGBM. The results indicate that the VEGF pathway in rGBM is regulated by a number of interrelated pathways. The discovered miRNAs hold promise as rGBM biomarkers and therapeutic targets, offering possibilities for novel therapy strategies and aiding rGBM diagnosis and prognosis.