Biotechnological product potential of Auxenochlorella protothecoides including biologically active compounds (BACs) under nitrogen stress conditions.

Nitrogen stress can influence microalgae's growth characteristics, and microalgae grown in nitrogen-deficient conditions may produce higher or lower levels of biotechnological products as a result of metabolic changes. In photoautotrophic and heterotrophic cultures, nitrogen limitation has been proven effective in promoting lipid accumulation. In spite of this, no study has demonstrated a significant correlation between lipid content and other biotechnological products such as bioactive compounds (BACs). This research examines a strategy for lipid accumulation as well as the potential production of BACs with antibacterial properties in parallel with that strategy. This concept involved the treatment of the microalga Auxenochlorella protothecoides with low and high concentrations of ammonium (NH4+). This particular experiment reached a maximum lipid content of 59.5% using a 0.8 mM NH4+ concentration, resulting in the yellowing of the chlorophyll levels. Agar diffusion assays were conducted to determine the antibacterial activity of different extracts derived from the biomass when stressed with different levels of nitrogen. Algal extracts prepared by a variety of solvents showed different levels of antibacterial activity against representative strains of both gram-negative (Escherichia coli) and gram-positive (Staphylococcus aureus) bacteria. Among the extracts tested, 500 mg/L ethyl acetate extract had the greatest antibacterial activity against Escherichia coli. In order to identify the components responsible for the extract's antibacterial activity, fatty acid methyl ester (FAME) analysis was performed. It has been suggested that the lipid fraction may be a valuable indicator of these activities since some lipid components are known to possess antimicrobial properties. In this regard, it was found that the amount of polyunsaturated fatty acid (PUFA) significantly decreased by 53.4% under the conditions with the highest antibacterial activity observed.

Bone marrow mesenchymal stromal cells support regeneration of intestinal damage in a colitis mouse model, independent of their CXCR4 expression.

Inflammatory bowel disease (IBD) is characterized by a chronically dysregulated immune response in the gastrointestinal tract. Bone marrow multipotent mesenchymal stromal cells have an important immunomodulatory function and support regeneration of inflamed tissue by secretion of soluble factors as well as through direct local differentiation. CXCR4 is the receptor for CXCL12 (SDF-1, stromal-derived factor-1) and has been shown to be the main chemokine receptor, required for homing of MSCs. Increased expression of CXCL12 by inflamed intestinal tissue causes constitutive inflammation by attracting lymphocytes but can also be used to direct MSCs to sites of injury/inflammation. Trypsin is typically used to dissociate MSCs into single-cell suspensions but has also been shown to digest surface CXCR4. Here, we assessed the regenerative effects of CXCR4high and CXCR4low MSCs in an immune-deficient mouse model of DSS-induced colitis. We found that transplantation of MSCs resulted in clinical improvement and histological recovery of intestinal epithelium. In contrary to our expectations, the levels of CXCR4 on transplanted MSCs did not affect their regenerative supporting potential, indicating that paracrine effects of MSCs may be largely responsible for their regenerative/protective effects.

Relationships between trace elements and cognitive and depressive behaviors in sprague dawley and wistar albino rats.

Introduction: This study investigates the effects of social isolation on mental health and cognitive functions in Sprague Dawley (SD) and Wistar Albino (WIS) rat strains, prompted by the heightened awareness of such impacts amid the COVID-19 pandemic. This study aims to explore the impact of social isolation on memory, learning, and behavioral changes in middle-aged SD and WIS rat strains and to investigate cortical trace element levels, seeking potential correlations between these levels and the observed behavioral responses to social isolation. Methods: Four groups of 14-month-old male rats were established: control and isolated SDs and WIS rats (CONT-SD, ISO-SD, CONT-WIS, ISO-WIS). Morris Water Maze and Porsolt Forced Swimming tests were conducted for behavioral assessment. Following behavioral tests, rats were sacrificed under general anesthesia, and cortices were isolated for analysis of macro and trace element levels (ICP/MS). Results: In behavioral tests, CONT-SD rats exhibited superior performance in the Morris Water Maze test compared to CONT-WIS rats, but displayed increased depressive behaviors following social isolation, as evident in the Porsolt Forced Swimming test (p < 0.05). ISO-SD rats showed elevated levels of Co and Cu, along with reduced levels of Cs and As, compared to ISO-WIS rats. Moreover, isolation resulted in decreased Cu and Mo levels but increased Rb levels in WIS rats. Comparison of trace element levels in naïve groups from different strains revealed lower Zn levels in the WIS group compared to SD rats. Discussion: The findings suggest that the SD strain learns faster, but is more susceptible to depression after isolation compared to the WIS strain. Increased Co and Cu levels in ISO-SD align with previous findings, indicating potential trace element involvement in stress responses. Understanding these mechanisms could pave the way for preventive treatment strategies or therapeutic targets against the consequences of stressors, contributing to research and measures promoting a balanced diet to mitigate neurobehavioral abnormalities associated with social isolation in the future.

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

Natural Photosensitizers in Antimicrobial Photodynamic Therapy.

Health problems and reduced treatment effectiveness due to antimicrobial resistance have become important global problems and are important factors that negatively affect life expectancy. Antimicrobial photodynamic therapy (APDT) is constantly evolving and can minimize this antimicrobial resistance problem. Reactive oxygen species produced when nontoxic photosensitizers are exposed to light are the main functional components of APDT responsible for microbial destruction; therefore, APDT has a broad spectrum of target pathogens, such as bacteria, fungi, and viruses. Various photosensitizers, including natural extracts, compounds, and their synthetic derivatives, are being investigated. The main limitations, such as weak antimicrobial activity against Gram-negative bacteria, solubility, specificity, and cost, encourage the exploration of new photosensitizer candidates. Many additional methods, such as cell surface engineering, cotreatment with membrane-damaging agents, nanotechnology, computational simulation, and sonodynamic therapy, are also being investigated to develop novel APDT methods with improved properties. In this review, we summarize APDT research, focusing on natural photosensitizers used in in vitro and in vivo experimental models. In addition, we describe the limitations observed for natural photosensitizers and the methods developed to counter those limitations with emerging technologies.