Pentavalent antimonials have been the first-line treatment for leishmaniasis for decades. However, the development of resistance to sodium stibogluconate (SSG) has limited its use, especially for treating visceral leishmaniasis (VL). The present work aims to optimize a cationic liposomal formulation of SSG for the treatment of both SSG-sensitive (AG83) and SSG-resistant (GE1F8R and CK1R) Leishmania donovani infections. Parasite killing was determined by the 3-(4,5-dimethylthiazol-2)-2,5-diphenyltetrazolium bromide (MTT) assay and microscopic counting of Giemsa-stained macrophages. Macrophage uptake studies were carried out by confocal microscopic imaging. Parasite-liposome interactions were visualized through transmission electron microscopy. Toxicity tests were performed using assay kits. Organ parasite burdens were determined by microscopic counting and limiting dilution assays. Cytokines were measured by enzyme-linked immunosorbent assays (ELISAs) and flow cytometry. Although all cationic liposomes studied demonstrated leishmanicidal activity, phosphatidylcholine (PC)-dimethyldioctadecylammonium bromide (DDAB) vesicles were most effective, followed by PC-stearylamine (SA) liposomes. Since entrapment of SSG in PC-DDAB liposomes demonstrated enhanced ultrastructural alterations in promastigotes, PC-DDAB-SSG vesicles were further investigated in vitro and in vivo . PC-DDAB-SSG could effectively alleviate SSG-sensitive and SSG-resistant L. donovani infections in the liver, spleen, and bone marrow of BALB/c mice at a dose of SSG (3 mg/kg body weight) not reported previously. The parasiticidal activity of these vesicles was attributed to better interactions with the parasite membranes, resulting in direct killing, and generation of a strong host-protective environment, necessitating a very low dose of SSG for effective cures.

Background: The observation that recovery from infection with Leishmania confers immunity to reinfection suggests that control of leishmaniasis by vaccination may be possible. New generation vaccines, particularly those based on recombinant proteins and DNA, are found to be less immunogenic. Objective: There is an urgent need for the development of new and improved vaccine adjuvants. Methods: Based on their principal mechanisms of action, adjuvants can be broadly separated into two classes: immunostimulatory adjuvants and vaccine delivery systems. Vaccine delivery systems can carry both antigen and adjuvant for effective delivery to the antigen-presenting cells (APCs). In this article, we review the adjuvants, the delivery systems and their combinations used in the search of an effective vaccine against leishmaniasis. Conclusion: Based on current knowledge, cationic liposomes appear to have better prospects as effective delivery systems for developing a vaccine for leishmaniasis.

Leishmaniasis presents a complex spectrum of diseases and immunological manifestations depending upon both the species of the microorganism and the host it infe...

The liver plays a vital role in biotransforming and extricating xenobiotics and is thus prone to their toxicities. Short-term administration of carbon tetrachloride (CCl₄) causes hepatic inflammation by enhancing cellular reactive oxygen species (ROS) level, promoting mitochondrial dysfunction, and inducing cellular apoptosis. Curcumin is well accepted for its antioxidative and anti-inflammatory properties and can be considered as an effective therapeutic agent against hepatotoxicity. However, its therapeutic efficacy is compromised due to its insolubility in water. Vesicular delivery of curcumin can address this limitation and thereby enhance its effectiveness. In this study, it was observed that both liposomal and nanoparticulated formulations of curcumin could increase its efficacy significantly against hepatotoxicity by preventing cellular oxidative stress. However, the best protection could be obtained through the polymeric nanoparticle-mediated delivery of curcumin. Mitochondria have a pivotal role in ROS homeostasis and cell survivability. Along with the maintenance of cellular ROS levels, nanoparticulated curcumin also significantly (P<0.0001) increased cellular antioxidant enzymes, averted excessive mitochondrial destruction, and prevented total liver damage in CCl₄-treated rats. The therapy not only prevented cells from oxidative damage but also arrested the intrinsic apoptotic pathway. In addition, it also decreased the fatty changes in hepatocytes, centrizonal necrosis, and portal inflammation evident from the histopathological analysis. To conclude, curcumin-loaded polymeric nanoparticles are more effective in comparison to liposomal curcumin in preventing CCl₄-induced oxidative stress–mediated hepatocellular damage and thereby can be considered as an effective therapeutic strategy.

This systematic review (2018–2023) evaluates the diagnostic accuracy of MCV and MPV in pregnancy-related disorders. Evaluate diagnostic accuracy of MCV/MPV for pregnancy complications. PRISMA 2020 review of 36 studies (2018–2023) using automated analyzers. - MPV > 10.2 fL predicts preeclampsia (78% sensitivity, AUC 0.81). - MCV < 80 fL indicates iron-deficiency anemia (85% specificity). - MPV > 9.8 fL associates with gestational diabetes (OR 2.3, 95% CI 1.8–3.0). - $0.20/test cost advantage. These low-cost markers could enhance early detection in resource-limited settings. These hematological markers are associated with up to 60% reduction in diagnostic costs when compared to conventional testing methods, making them viable for LMICs.

Objectives: To evaluate the diagnostic and prognostic performance of the neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) in SLE and to integrate these biomarkers into an interpretable artificial intelligence (AI) model for clinical decision support. Design: We conducted a two-phase mixed-methods study: (1) a meta-analysis of 50 studies (n=12 850 patients with SLE), compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and (2) the development and validation of an XGBoost machine learning model, guided by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-AI, with SHapley Additive exPlanations (SHAP) explainability. Setting: Our analysis used multicentre data from global SLE registries, including cohorts from Asia, Europe, North America and Africa. Participants: The study included adults (≥18 years) who met the 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for SLE, with NLR and PLR measured via standardised complete blood count. Comparator groups consisted of healthy controls and patients with non-SLE autoimmune diseases. Interventions: NLR and PLR were assessed as biomarkers for SLE activity and complications. Our AI model integrated these ratios with standard clinical biomarkers and multi-omics data. Primary and secondary outcome measures: The primary outcome was diagnostic accuracy (measured by area under the curve (AUC), sensitivity and specificity) for active SLE (defined as Systemic Lupus Erythematosus Disease Activity Index ≥6). Secondary outcomes included prognostic value (HRs for lupus nephritis, cardiovascular events and mortality) and treatment response monitoring. Results: Our analysis demonstrated that NLR has superior diagnostic accuracy for active SLE compared with PLR, with a pooled AUC of 0.85 vs 0.78 (p=0.02). NLR showed pooled sensitivity and specificity of 78% and 82%, respectively, while PLR showed 70% and 75%. Elevated NLR (>3.5) and PLR (>185) predicted higher risks of lupus nephritis (HR=2.1 and 1.8, respectively), cardiovascular events (HR=2.3 and 1.9) and mortality (HR=3.1 and 2.1; all p3.1 for Asian populations, >2.8 for Caucasian populations and >3.4 for African populations. The AI model achieved an AUC of 0.87 in training and 0.82 in validation, with NLR emerging as the top predictive feature (SHAP score=0.25). Conclusion: NLR outperforms PLR in SLE diagnosis and risk stratification, with validated cut-offs that vary significantly by ethnicity. The integration of these biomarkers into AI models enhances predictive accuracy, supporting the use of NLR and PLR as cost-effective tools for SLE management.