Fungal infections, especially those caused have emerged as a significant medical concern over the past three decades, particularly among immunocompromised patients. However, recent studies have ...highlighted the increasing prevalence of fungal infections resembling yeast other than
, such as trichosporonosis, especially among immunosuppressed individuals worldwide.
has been identified as a significant contributor to superficial and invasive infections. Invasive trichosporonosis, primarily affecting immunocompromised patients, poses a significant threat with high mortality rates.
The current study aimed to explore the clinical epidemiology of
spp at King Abdulaziz University Hospital (KAUH) in Saudi Arabia.
This retrospective study aimed to assess the clinical epidemiology of
spp. infections in microbiology cultures obtained from KAUH in Saudi Arabia. The study analyzed data from patients over a five-year period, focusing on demographic, clinical, and microbiological characteristics.
This study encompassed 21 participants, categorized into four distinct age groups. Moreover, this study indicated
as the predominant species isolated, accounting for 90.5% of infections, followed by
(9.5%). ICU hospitalization, diabetes mellitus, taking immunosuppressive drugs, and antifungal drugs, and the use of invasive medical equipment were identified as prominent risk factors for trichosporonosis. Urinary tract infections were the most common clinical presentation, particularly among male and elderly patients. Mortality rates were high, especially among older individuals.
This study contributes valuable epidemiological insights into trichosporonosis, highlighting the need for enhanced surveillance and preventive strategies in healthcare settings. Further research is warranted to optimize treatment approaches and infection control measures, ultimately reducing the burden of
infections on patient outcomes.
The current study explored the protective potential of kaempferol 3-sophoroside-7-glucoside (KSG) against acute lung injury (ALI). Pre-treatment with KSG effectively secured mice from ALI and showed ...similar efficaciousness to dexamethasone. KSG markedly increased the survival rate and alleviated lung pathological lesions induced by lipopolysaccharide (LPS). Furthermore, KSG attenuated differential and total cell counts in BALF (bronchoalveolar lavage fluid) and MPO (myeloperoxidase) activity. KSG counteracted the NF-κB (nuclear factor-κB) activation and significantly ameliorated the downstream inflammatory cytokine, TNF-α (tumor necrosis factor-α). Simultaneously, KSG suppressed the over-expression of NLRP3 (NOD-like receptor protein 3), caspase-1, and pro-inflammatory cytokine interleukin IL-1β (interleukine-1β) and prohibited the elevation of the pyroptotic parameter GSDMD-N (N-terminal domain of gasdermin D) induced by LPS challenge. In addition, KSG significantly enhanced Nrf2 (nuclear-factor erythroid-2-related factor) and HO-1 (heme-oxygenase-1) expression. Meanwhile, KSG mitigated lipid peroxidative markers (malondialdehyde, protein carbonyl and 4-hydroxynonenal) and boosted endogenous antioxidants (superoxide dismutase/reduced glutathione/catalase) in lung tissue. In silico analyses revealed that KSG disrupts Keap1-Nrf2 protein–protein interactions by binding to the KEAP1 domain, consequently activating Nrf2. Specifically, molecular docking demonstrated superior binding affinity of KSG to KEAP1 compared to the reference inhibitor, with docking scores of −9.576 and −6.633 Kcal/mol, respectively. Additionally, the MM-GBSA binding free energy of KSG (−67.25 Kcal/mol) surpassed that of the reference inhibitor (−56.36 Kcal/mol). Furthermore, MD simulation analysis revealed that the KSG-KEAP1 complex exhibits substantial and stable binding interactions with various amino acids over a duration of 100 ns. These findings showed the protective anti-inflammatory and anti-oxidative modulatory efficiencies of KSG that effectively counteracted LPS-induced ALI and encouraged future research and clinical applications of KSG as a protective strategy for ALI.
This study was designed to develop neem oil nanoemulsion of tea tree oil (TTO-NO-NE) using design of experiment based Box–Behnken design, which provided thermodynamic stable NE with globule size of ...174 nm, and PDI 0.28, respectively. Whereas the zeta potential of optimized NE has occurred as − 20 mV with spherical and non-segregated in shape. Next, TTO-NO-NE-loaded nanogel and conventional gel were prepared, and initially, a comparative evaluation was performed for homogeneity, pH, spreadability, extrudability, and drug content. Furthermore, in vitro release pattern, ex vivo dermatokinetic profile, in vivo skin safety study, and stability of nanogel was determined. Comparative in vitro release study showed significantly sustained release of drug from nanogel (p < 0.005) when compared to the conventional gel. Simultaneously, a comparative ex vivo dermatokinetic study demonstrated significantly maximum drug deposition for nanogel (p < 0.05) than conventional gel. Moreover, in vivo skin safety study exhibited no signs of toxicity in terms of zero scoring for nanogel and was considered safe for future use. Finally, a stability study showed no significant (p > 0.05) variation in the pH, spreadability, extrudability, and drug content of optimized nanogel, which claims good stability for nanogel after 6 months of storage in prescribed environmental conditions. Thus, it can be concluded that the developed novel nanogel is a promising therapeutic asset that can mitigate dermal infections effectively.
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•Tomatidine has remarkable antioxidant and anti-inflammatory activities, protecting against fulminant hepatic failure in mice.•The interplay between Nrf2 and NF-κB signaling pathways ...mediates fulminant hepatic failure in mice.•Tomatidine modulates the crosstalk between Nrf2 and NF-κB signaling pathways, mitigating oxidative stress and inflammation.•Tomatidine enhances Keap1/Nrf2/HO-1 signaling while repressing NF-κB/TNF-α/IL-6/IL-1β and NF-κB/iNOS signaling pathways.
Fulminant hepatic failure (FHF) is the terminal phase of acute liver injury, which is characterized by massive hepatocyte necrosis and rapid hepatic dysfunction in patients without preexisting liver disease. There are currently no therapeutic options for such a life-threatening hepatic failure except liver transplantation; therefore, the terminal phase of the underlying acute liver injury should be avoided. Tomatidine (TOM), asteroidal alkaloid, may have different biological activities, including antioxidant and anti-inflammatory effects. Herein, the lipopolysaccharide (LPS)/D-galactosamine (D-GalN)-induced FHF mouse model was established to explore the protective potential of TOM and the underlying mechanisms of action. TOM pretreatment significantly inhibited hepatocyte necrosis and decreased serum aminotransferase activities in LPS/D-GalN-stimulated mice. TOM further increased the level of different antioxidant enzymes while reducing lipid peroxidation biomarkers in the liver. These beneficial effects of TOM were shown to be associated with targeting of NF-κB signaling pathways, where TOM repressed NF-κB activation and decreased LPS/D-GalN-induced TNF-α, IL-6, IL-1β, and iNOS production. Moreover, TOM prevented LPS/D-GalN-induced upregulation of Keap1 expression and downregulation of Nrf2 and HO-1 expression, leading to increased Nrf2-binding activity and HO-1 levels. Besides, TOM pretreatment repressed LPS/D-GalN-induced upregulation of proliferating cell nuclear antigen (PCNA) expression, which spared the hepatocytes from damage and subsequent repair following the LPS/D-GalN challenge. Collectively, our findings revealed that TOM has a protective effect on LPS/D-GalN-induced FHF in mice, showing powerful antioxidant and anti-inflammatory effects, primarily mediated via modulating Keap1/Nrf2/HO-1 and NF-κB/TNF-α/IL-6/IL-1β/iNOS signaling pathways.
The journal retracts the article, "The Enhanced Cytotoxic and Pro-Apoptotic Effects of Optimized Simvastatin-Loaded Emulsomes on MCF-7 Breast Cancer Cells" ....
Statins, including simvastatin (SMV), are commonly used for the control of hyperlipidaemia and have also proven therapeutic and preventative effects in cardiovascular diseases. Besides that, there is ...an emerging interest in their use as antineoplastic drugs as demonstrated by different studies showing their cytotoxic activity against different cancer cells. In this study, SMV-loaded emulsomes (SMV-EMLs) were formulated and evaluated for their cytotoxic activity in MCF-7 breast cancer cells. The emulsomes were prepared using a modified thin-film hydration technique. A Box-Behnken model was used to investigate the impact of formulation conditions on vesicle size and drug entrapment. The optimized formulation showed a spherical shape with a vesicle size of 112.42 ± 2.1 nm and an entrapment efficiency of 94.34 ± 1.11%. Assessment of cytotoxic activities indicated that the optimized SMV-EMLs formula exhibited significantly lower half maximal inhibitory concentration (IC50) against MCF-7 cells. Cell cycle analysis indicated the accumulation of cells in the G2-M phase as well as increased cell fraction in the pre-G1 phase, suggesting an enhancement of anti-apoptotic activity of SMV. The staining of cells with Annex V revealed an increase in early and late apoptosis, in line with the increased cellular content of caspase-3 and Bax. In addition, the mitochondrial membrane potential (MMP) was significantly decreased. In conclusion, SMV-EMLs demonstrated superior cell death-inducing activity against MCF-7 cells compared to pure SMV. This is mediated, at least in part, by enhanced pro-apoptotic activity and MMP modulation of SMV.
Luliconazole is a new topical imidazole antifungal drug for the treatment of skin infections. It has low solubility and poor skin penetration which limits its therapeutic applications. In order to ...improve its therapeutic efficacy, spanlastics nanoformulation was developed and optimized using a combined mixture-process variable design (CMPV). The optimized formulation was converted into a hydrogel formula to enhance skin penetration and increase the efficacy in experimental cutaneous
infections in Swiss mice wounds. The optimized formulation was generated at percentages of Span and Tween of 48% and 52%, respectively, and a sonication time of 6.6 min. The software predicted that the proposed formulation would achieve a particle size of 50 nm with a desirability of 0.997. The entrapment of luliconazole within the spanlastics carrier showed significant (
< 0.0001) antifungal efficacy in the immunocompromised Candida-infected Swiss mice without causing any irritation, when compared to the luliconazole treated groups. The microscopic observation showed almost complete removal of the fungal colonies on the skin of the infected animals (0.2 ± 0.05 log CFU), whereas the control animals had 0.2 ± 0.05 log CFU. Therefore, luliconazole spanlastics could be an effective formulation with improved topical delivery for antifungal activity against
.
Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess ...contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.
The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special ...occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient's visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient's clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients' data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.
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