Development of thermosensitive hydrogel of Amphotericin-B and Lactoferrin combination-loaded PLGA-PEG-PEI nanoparticles for potential eradication of ocular fungal infections: In-vitro, ex-vivo and in-vivo studies

The most prevalent conditions among ocular surgery and COVID−19 patients are fungal eye infections, which may cause inflammation and dry eye, and may cause ocular morbidity. Amphotericin-B eye drops are commonly used in the treatment of ocular fungal infections. Lactoferrin is an iron-binding glycoprotein with broad-spectrum antimicrobial activity and is used for the treatment of dry eye, conjunctivitis, and ocular inflammation. However, poor aqueous stability and excessive nasolacrimal duct draining impede these agens' efficiency. The aim of this study was to examine the effect of Amphotericin-B, as an antifungal against Candida albicans, Fusarium, and Aspergillus flavus, and Lactoferrin, as an anti-inflammatory and anti-dry eye, when co-loaded in triblock polymers PLGA-PEG-PEI nanoparticles embedded in P188-P407 ophthalmic thermosensitive gel. The nanoparticles were prepared by a double emulsion solvent evaporation method. The optimized formula showed particle size (177.0 ± 0.3 nm), poly-dispersity index (0.011 ± 0.01), zeta-potential (31.9 ± 0.3 mV), and entrapment% (90.9 ± 0.5) with improved ex-vivo pharmacokinetic parameters and ex-vivo trans-corneal penetrability, compared with drug solution. Confocal laser scanning revealed valuable penetration of fluoro-labeled nanoparticles. Irritation tests (Draize Test), Atomic force microscopy, cell culture and animal tests including histopathological analysis revealed superiority of the nanoparticles in reducing signs of inflammation and eradication of fungal infection in rabbits, without causing any damage to rabbit eyeballs. The nanoparticles exhibited favorable pharmacodynamic features with sustained release profile, and is neither cytotoxic nor irritating in-vitro or in-vivo. The developed formulation might provide a new and safe nanotechnology for treating eye problems, like inflammation and fungal infections. Graphical Unlabelled Image

Development of thermosensitive hydrogel of Amphotericin-B and Lactoferrin combination-loaded PLGA-PEG-PEI nanoparticles for potential eradication of ocular fungal infections: In-vitro, ex-vivo and in-vivo studies.

The most prevalent conditions among ocular surgery and COVID-19 patients are fungal eye infections, which may cause inflammation and dry eye, and may cause ocular morbidity. Amphotericin-B eye drops are commonly used in the treatment of ocular fungal infections. Lactoferrin is an iron-binding glycoprotein with broad-spectrum antimicrobial activity and is used for the treatment of dry eye, conjunctivitis, and ocular inflammation. However, poor aqueous stability and excessive nasolacrimal duct draining impede these agens' efficiency. The aim of this study was to examine the effect of Amphotericin-B, as an antifungal against Candida albicans, Fusarium, and Aspergillus flavus, and Lactoferrin, as an anti-inflammatory and anti-dry eye, when co-loaded in triblock polymers PLGA-PEG-PEI nanoparticles embedded in P188-P407 ophthalmic thermosensitive gel. The nanoparticles were prepared by a double emulsion solvent evaporation method. The optimized formula showed particle size (177.0 ± 0.3 nm), poly-dispersity index (0.011 ± 0.01), zeta-potential (31.9 ± 0.3 mV), and entrapment% (90.9 ± 0.5) with improved ex-vivo pharmacokinetic parameters and ex-vivo trans-corneal penetrability, compared with drug solution. Confocal laser scanning revealed valuable penetration of fluoro-labeled nanoparticles. Irritation tests (Draize Test), Atomic force microscopy, cell culture and animal tests including histopathological analysis revealed superiority of the nanoparticles in reducing signs of inflammation and eradication of fungal infection in rabbits, without causing any damage to rabbit eyeballs. The nanoparticles exhibited favorable pharmacodynamic features with sustained release profile, and is neither cytotoxic nor irritating in-vitro or in-vivo. The developed formulation might provide a new and safe nanotechnology for treating eye problems, like inflammation and fungal infections.

Cryptococcal chest wall mass and rib osteomyelitis associated with the use of fingolimod: A case report and literature review.

Being introduced in 2010, fingolimod was among the first oral therapies for relapsing multiple sclerosis (MS). Since that time, postmarketing surveillance has noted several case reports of various cryptococcal infections associated with fingolimod use. To date, approximately 15 such case reports have been published. We present the first and unique case of cryptococcal chest wall mass and rib osteomyelitis associated with fingolimod use. The patient presented with left-side chest pain and was found to have a lower left chest wall mass. Computerized tomography (CT) showed chest wall mass with the destruction of left 7th rib. Aspirate from the mass grew Cryptococcus neoformans. The isolate was serotype A. Fingolimod was stopped. The patient received liposomal amphotericin B for 2 weeks and started on fluconazole with a plan to continue for 6-12 months. The follow-up CT in 6 weeks showed a marked decrease in the size of the chest wall mass. In conclusion, our case highlights the atypical and aggressive form of cryptococcal infection possibly related to immunosuppression from fingolimod use.

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities.

This study is conducted to build a multi-criteria text mining model for COVID-19 testing reasons and symptoms. The model is integrated with a temporal predictive classification model for COVID-19 test results in rural underserved areas. A dataset of 6895 testing appointments and 14 features is used in this study. The text mining model classifies the notes related to the testing reasons and reported symptoms into one or more categories using look-up wordlists and a multi-criteria mapping process. The model converts an unstructured feature to a categorical feature that is used in building the temporal predictive classification model for COVID-19 test results and conducting some population analytics. The classification model is a temporal model (ordered and indexed by testing date) that uses machine learning classifiers to predict test results that are either positive or negative. Two types of classifiers and performance measures that include balanced and regular methods are used: (1) balanced random forest and (2) balanced bagged decision tree. The balanced or weighted methods are used to address and account for the biased and imbalanced dataset and to ensure correct detection of patients with COVID-19 (minority class). The model is tested in two stages using validation and testing sets to ensure robustness and reliability. The balanced classifiers outperformed regular classifiers using the balanced performance measures (balanced accuracy and G-score), which means the balanced classifiers are better at detecting patients with positive COVID-19 results. The balanced random forest achieved the best average balanced accuracy (86.1%) and G-score (86.1%) using the validation set. The balanced bagged decision tree achieved the best average balanced accuracy (83.0%) and G-score (82.8%) using the testing set. Also, it was found that the patient history, age, testing reasons, and time are the key features to classify the testing results.

Determinants of virtual classroom adoption in Jordan: the case of princess Sumaya university for technology

Purpose>The purpose of this study is to investigate the factors that affect students’ behavioral intentions to use virtual classrooms at Princess Sumaya University for Technology (PSUT) in Jordan.Design/methodology/approach>A quantitative research approach was adopted, an online survey method was used and the data were collected among students at PSUT in Jordan. A total of 511 responses were usable for analysis. A structural equation modeling partial least squares technique was used to examine the hypothesized model.Findings>The findings reveal that the proposed factors have direct and indirect relationships with behavioral intentions to use virtual classrooms. They show that students’ satisfaction has a direct influence on behavioral intention, while other variables such as instructor characteristics, virtual classroom quality, perceived self-efficacy, perceived organizational support, perceived ease of use and perceived usefulness have an indirect effect on behavioral intentions to use virtual classrooms.Research limitations/implications>The study was conducted at PSUT in Jordan, which could limit the generalizability of the findings. Furthermore, the present study measured students’ behavioral intentions to use virtual classrooms and future research should consider the actual use of virtual classrooms.Practical implications>The findings of this study offer significant and useful information to policymakers, instructors, developers and students regarding the use of virtual classrooms in universities. Based on students’ needs and readiness, the findings identify which factors to consider when developing an e-learning system to enhance learning and teaching performance.Originality/value>This study extends existing knowledge by developing a conceptual model to identify the key factors of virtual classroom adoption in higher education institutions in Arab countries. This study contributes to the literature in the context of e-learning by validating an extended technology acceptance model from an Arab countries perspective and considering the differences in culture, learning style and physical environment compared to developed countries.

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

This study is conducted to build a multi-criteria text mining model for COVID-19 testing reasons and symptoms. The model is integrated with a temporal predictive classification model for COVID-19 test results in rural underserved areas. A dataset of 6895 testing appointments and 14 features is used in this study. The text mining model classifies the notes related to the testing reasons and reported symptoms into one or more categories using look-up wordlists and a multi-criteria mapping process. The model converts an unstructured feature to a categorical feature that is used in building the temporal predictive classification model for COVID-19 test results and conducting some population analytics. The classification model is a temporal model (ordered and indexed by testing date) that uses machine learning classifiers to predict test results that are either positive or negative. Two types of classifiers and performance measures that include balanced and regular methods are used: (1) balanced random forest and (2) balanced bagged decision tree. The balanced or weighted methods are used to address and account for the biased and imbalanced dataset and to ensure correct detection of patients with COVID-19 (minority class). The model is tested in two stages using validation and testing sets to ensure robustness and reliability. The balanced classifiers outperformed regular classifiers using the balanced performance measures (balanced accuracy and G-score), which means the balanced classifiers are better at detecting patients with positive COVID-19 results. The balanced random forest achieved the best average balanced accuracy (86.1%) and G-score (86.1%) using the validation set. The balanced bagged decision tree achieved the best average balanced accuracy (83.0%) and G-score (82.8%) using the testing set. Also, it was found that the patient history, age, testing reasons, and time are the key features to classify the testing results.