Volume 7 (2023): Issue 4 (October 2023)

Obsessive-Compulsive Disorder (OCD) is a psychiatric illness that produces significant psychological distress in patients. Individuals with OCD have recurring unwanted thoughts or sensations which make them obsessed with something and feel to do something repetitively as a compulsion. In general detection of OCD is performed by symptoms analysis. However, the symptoms are significantly visible at a later stage. Even individuals with OCD have less faith in the analysis of the symptoms as long as it is not affecting their life negatively. As a result, they start their treatment at a later stage and the treatment process becomes longer. However, it is observed that if the detection is performed through laboratory analysis through some biomarkers then the patients have more faith in the detection process and can start their treatment well in advance. Therefore laboratory detection of OCD can play a vital role in OCD treatment effectiveness. Most of the laboratory detection process proposed in the literature uses Machine Learning on related biomarkers. However, the prediction accuracy rate is not enough. This research aims to analyze the approaches to pediatric OCD based on machine learning using neuroimaging biomarkers and oxidative stress biomarkers. The challenges in OCD detection and prediction using neuroimaging biomarkers, oxidative stress biomarkers, and Machine Learning models have been described. Further, it analyzes the performance of different machine learning models that were used for OCD detection and highlights the research gap to improve prediction accuracy.

Bioactive peptides are protein components which are inactive within the protein structure, and upon release by enzymatic hydrolysis, they exhibit special physiological functions. In the last years, the characteristics of bioactive peptides obtained from various plant, animal and microbial sources have received much attention. Bioactive peptides are produced using hydrolysis by enzymes extracted from plants or microorganisms, or digestive enzymes and fermentation by proteolytic starter cultures. The composition and sequence of the amino acids determines their different functions, including relaxing effects, solute binding properties, strengthening of the immune system, antioxidant, anti-microbial, anti-inflammatory, cholesterol-lowering and anti-hypertensive effects. Bioactive peptides are identified by different methods including membrane separation techniques and chromatography from protein hydrolysis products and using spectrometric techniques. The possibility of using bioactive peptides as health or therapeutic components depends on ensuring their bio stability, bioavailability and safety.

An increasingly relevant functional measurement is a liquid biopsy to assist in the diagnosis of cancers. The existing approach for liquid biopsy is to utilize microfluidic chips for the isolation of circulating tumor cells (CTCs) or exosomes or extracellular vesicles (EV) from patient samples, and then for the analysis of the cargo contained inside the CTCs, exosomes or EVs. However, such an analysis does not provide a real-time liquid biopsy, since there is a long delay between the time of sample collection and the results from the analysis. Microfluidic chip-formats also provide the capability to mimic tissue functions from the analysis of small numbers of cells cultured in the chip. Analysis of the secreted molecules from such cells could provide a measurement of the secretome, which could be analogous to a liquid biopsy. A 3D structural organization of cells in microfluidic chips is usually in the form of organoids or spheroids. The analysis of organoids or spheroids is well-adapted for immunohistochemistry or ELISA-type identification of surface markers, but not for real-time analysis of secreted molecules since the fluid and molecules in the interior volume of the organoid or spheroid is not accessible in real-time. We have recently proposed an alternative novel design for a microfluidic chip format comprising 3D micro-niches that provide a real-time analysis of secretions produced directly from small numbers of cells. The microfluidic chip with 3D micro-niches then analyses the secretions from these monolayers in real-time (“secretome”). The microfluidic chip includes electronic biosensors that provide real-time measurement of secreted molecules. This short review concludes with a proposition for the means to utilize this novel microfluidic chip to function as a real-time and quantifiable diagnostic screening device to differentiate cancerous cells from healthy cells.

Climate change has imposed a significant struggle for survival most of the Earth’s species, highlighting the urgent need for a healthy and secure environment. Recent scientific investigations have primarily concentrated on the development and use of microorganisms as powerful biotechnological tools to address the escalating pollution that poses a severe threat to life. But this microorganisims long-term effects on biodiversity and ecosystems remain a subject of inquiry. In this comprehensive review, we aim to thoroughly evaluate the effects of microorganisms on the general ecosystem and critically assess the use of existing biotechnological tools developed to combat climate-related challenges. By shedding light on the potential implications, this review strives to contribute to a deeper understanding of the intricate interplay between microorganisms, ecosystems, and climate change mitigation.