Immunoregulatory biological response modifiers: effect of cytokines on septic shock

Whole bacteria or bacterial components or their extracts were employed to restore or augment the immune system. Beneficial effects were attained with these agents in treating various diseases. These agents were named biological response modifiers (BRMs) because they regulated certain cellular components of the immune system. The cellular regulation induced by these BRMs was found to be due to cytokines. The cytokines were shown to act directly on the various cellular components and to provide therapeutic benefit in various autoimmune and immune deficiency diseases. Overproduction of specific cytokines however leads to a deleterious effect on the host. Overproduction of tumour necrosis factor (endotoxin, lipopolysaccharide) leads to septic shock. Bacteraemia is the leading cause of overproduction of tumour necrosis factor (TNF). Septic shock in many cases leads to death. Several monoclonal antibodies to lipopolysaccharide (LPS) and anticytokines have demonstrated protection against septic shock

Lack of effect of aspartame or of -phenylalanine on photically induced myoclonus in the baboon, Papio papio

The effects of large doses of -phenylalanine and of aspartame on seizure susceptibility and severity have been assessed in baboons Papio papio from Senegal which show photosensitive epileptic responses similar to primary generalised epilepsy in man. -Phenylalanine, 50, 150 or 450 mg/kg, or aspartame, 300 or 1000 mg/kg, were administered orally. Peak plasma -phenylalanine concentrations of approximately 2000 [mu]moles/1 occurred 1-4 h after the highest dose of -phenylalanine or aspartame. The plasma -phenylalanine to large neutral amino acid ratio increased approximately 30-fold at this time. Compared with water administration there were no changes in epileptic responses 1-5 h after either treatment. In this primate model of epilepsy acute increases in plasma phenylalanine concentration are neither pro- nor anticonvulsant.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27853/1/0000264.pd

Broad-Spectrum Drugs Against Viral Agents

Development of antivirals has focused primarily on vaccines and on treatments for specific viral agents. Although effective, these approaches may be limited in situations where the etiologic agent is unknown or when the target virus has undergone mutation, recombination or reassortment. Augmentation of the innate immune response may be an effective alternative for disease amelioration. Nonspecific, broad-spectrum immune responses can be induced by double-stranded (ds)RNAs such as poly (ICLC), or oligonucleotides (ODNs) containing unmethylated deocycytidyl-deoxyguanosinyl (CpG) motifs. These may offer protection against various bacterial and viral pathogens regardless of their genetic makeup, zoonotic origin or drug resistance

Machine-Learning-Based Non-Destructive Evaluation of Refractory Anchor Welds via Analysis of Percussion-Induced Acoustic Signals

Welding is a critical process in modern infrastructure, particularly for securing refractory anchors in high-temperature vessels used across refineries, power plants, and chemical facilities. Ensuring the quality of these welds is essential, but traditional inspection methods are often destructive, time-consuming, or limited in accuracy. This thesis investigates a novel non-destructive evaluation (NDE) technique that leverages machine learning (ML) and percussion-induced audio analysis to assess weld quality. A custom weld plate was fabricated containing both properly welded and intentionally flawed refractory anchors. Controlled mechanical impacts—using tools such as a hammer and chisel—were applied to the exposed ends of these anchors. The resulting audio signals were recorded and transformed into Mel-Frequency Cepstral Coefficients (MFCCs). These MFCCs served as input features for multiple machine learning models including supervised and unsupervised models. The supervised models used were support vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a semi-independent test using newly recorded data from the same weld plate, and a fully independent test involving unseen anchors. Despite increased variability across tests, the supervised models achieved 100% classification accuracy, while the unsupervised clustering method reached 99.42%. These results demonstrate that audio-based machine learning offers a fast, cost-effective, and objective alternative for weld inspection, with strong potential for improving industrial quality control practices