Minocycline mitigated enduring neurological consequences in the mice model of sepsis.

AIM: Sepsis-associated encephalopathy is a frequently observed consequence of sepsis, often resulting in chronic brain inflammation and injury, ultimately leading to a range of behavioral abnormalities. This study explores the potential preventive effects of minocycline on the long-lasting outcome of sepsis in a mice model of sepsis. METHODS: Adult male C57 mice were subjected to experimental sepsis through a single intraperitoneal injection of 5 mg/kg lipopolysaccharide (LPS). Minocycline administration via oral gavage (12.5, 25, and 50 mg/kg) commenced three days before sepsis induction and continued on the day of induction. Mice underwent behavioral assessments one month post-sepsis, with subsequent brain tissue analysis to investigate oxidative stress markers and cholinergic function. KEY FINDINGS: One month following sepsis induction, mice exhibited significant anxiety- and depressive-like behaviors as determined by assessments in the elevated plus maze (EPM), open field, and tail suspension test (TST). Additionally, they displayed impaired recognition memory in the novel object recognition (NOR) test. Brain tissue analysis revealed a notable increase in oxidative stress markers and acetylcholinesterase (AChE) activity in septic mice. Notably, minocycline treatment effectively mitigated the long-term behavioral abnormalities resulting from sepsis, attenuated oxidative stress markers, and reduced AChE activity. SIGNIFICANCE: These findings underscore the potential of minocycline as a therapeutic intervention during sepsis induction to prevent the enduring behavioral and neurological consequences of experimental sepsis.

Immune system roles in pathogenesis, prognosis, control, and treatment of Toxoplasma gondii infection.

Toxoplasma gondii is the protozoan causative agent of toxoplasmosis in humans and warm-blooded animals. Recent studies have illustrated that the immune system plays a pivotal role in the pathogenesis of toxoplasmosis by triggering immune cytokines like IL-12, TNF-α, and IFN-γ and immune cells like DCs, Th1, and Th17. On the other hand, some immune components can serve as prognosis markers of toxoplasmosis. In healthy people, the disease is often asymptomatic, but immunocompromised people and newborns may suffer severe symptoms and complications. Therefore, the immune prognostic markers may provide tools to measure the disease progress and help patients to avoid further complications. Immunotherapies using monoclonal antibody, cytokines, immune cells, exosomes, novel vaccines, and anti-inflammatory molecules open new horizon for toxoplasmosis treatment. In this review article, we discussed the immunopathogenesis, prognosis, and immunotherapy of Toxoplasma gondii infection.

Thymoquinone Effect on Leishmania tropica/infantum and Leishmania-Infected Macrophages.

INTRODUCTION: Leishmania is a parasitic protozoan that tries to enter and amplify within macrophages. Macrophage cells are also immune defense cells that phagocyte many microbes like bacteria, fungi, as well as parasites like Leishmania spp. However, they are unable to kill this parasite that resides in the phagosomes of contaminated macrophages and multiplies in these macrophages, leading to the destruction of contaminated macrophages and the emerging of Leishmania wounds. A large number of current therapies for Leishmania cure have adverse effects, or parasites have developed resistance to some of these therapies, so a better therapy for the cure of Leishmania is required. Thymoquinone is one of the Nigella Sativa ingredients with numerous biological effects, such as antioxidant as well as antimicrobial effects on a variety of microbes, namely fungi, bacteria, as well as parasites like Leishmania spp. The impacts of Thymoquinone on Leishmania tropica and Leishmania infantum, as well as Leishmania-infected macrophages, were examined in this study. METHODS: The impact of various Thymoquinone dosages on L. tropica and L. infantum promastigotes and amastigotes was examined in vitro. Flow cytometry, as well as MTT, was also applied to examine the cytotoxic activity of Thymoquinone on promastigotes of L. tropica and L. infantum, as well as the incidence of apoptosis. The amastigote assay is also utilized to calculate the % of contaminated macrophages as well as the number of the present parasites in each macrophage. RESULTS: The percentage of macrophages contaminated with L. tropica and L. infantum amastigotes after medicating with 20 µM of Thymoquinone was 23% and 19%, respectively. Also, after medicating with 10 µM of Thymoquinone, these percentages were 32% and 31%, respectively. Flow cytometry indicated that Thymoquinone caused 33.9% and 31.4% apoptosis in L. tropica and L. infantum, respectively. As determined by the promastigote assay, the inhibitory concentration (IC50) of Thymoquinone for L. tropica and L. infantum was 9.49 µM and 12.66 µM, respectively. The results of the promastigote and amastigote assay show that with an increase in Thymoquinone doses, its ability to kill Leishmania parasites increases, too. CONCLUSION: According to the results of the study, Thymoquinone has a potentially lethal impact on L. tropica and L. infantum promastigotes as well as amastigotes (within leishmania contaminated macrophages).

Recent advances in treatment Crimean-Congo hemorrhagic fever virus: A concise overview.

The Crimean Congo Hemorrhagic Fever Virus (CCHFV) is widespread in Africa, Asia, and Europe, among other places. The disease was initially discovered in the Crimean cities of the Soviet Union and the Congo, and it was given the name Crimean Congo because it induces hemorrhagic fever. According to studies, when the virus enters the body, it settles in immune cells such as macrophages and dendritic cells, causing them to malfunction and secrete inflammatory cytokines such as TNF-alpha, IL1, and IL6, resulting in cytokine storms that induces shock via endothelial activation and vascular leakage, while on the other hand, clots and disseminated intravascular coagulation (DIC) formation causes massive defects in various organs such as the liver and kidneys, as well as fatal bleeding. Disease prevention and treatment are crucial since no other effective vaccination against the disease has yet been developed. Immunotherapy is utilized as a consequence. One of the most effective treatments, when combined with compensatory therapies such as blood and platelet replacement, water, electrolytes, Fresh Frozen Plasma (FFP) replacement, and other compensatory therapies, is one of the most effective treatments. Studies; show that immunotherapy using IVIG and neutralizing and non-neutralizing monoclonal antibodies; cytokine therapy, and anti-inflammatory therapy using corticosteroids are effective ways to treat the disease.

Improved Estimation of Bio-Oil Yield Based on Pyrolysis Conditions and Biomass Compositions Using GA- and PSO-ANFIS Models.

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination (R 2) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination (R 2) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests.

On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models.

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food.

In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.