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.

Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

Disruption of CCR5 signaling to treat COVID-19-associated cytokine storm: Case series of four critically ill patients treated with leronlimab.

Coronavirus disease 2019 (COVID-19) is associated with considerable morbidity and mortality. The number of confirmed cases of infection with SARS-CoV-2, the virus causing COVID-19 continues to escalate with over 70 million confirmed cases and over 1.6 million confirmed deaths. Severe-to-critical COVID-19 is associated with a dysregulated host immune response to the virus, which is thought to lead to pathogenic immune dysregulation and end-organ damage. Presently few effective treatment options are available to treat COVID-19. Leronlimab is a humanized IgG4, kappa monoclonal antibody that blocks C-C chemokine receptor type 5 (CCR5). It has been shown that in patients with severe COVID-19 treatment with leronlimab reduces elevated plasma IL-6 and chemokine ligand 5 (CCL5), and normalized CD4/CD8 ratios. We administered leronlimab to 4 critically ill COVID-19 patients in intensive care. All 4 of these patients improved clinically as measured by vasopressor support, and discontinuation of hemodialysis and mechanical ventilation. Following administration of leronlimab there was a statistically significant decrease in IL-6 observed in patient A (p=0.034) from day 0-7 and patient D (p=0.027) from day 0-14. This corresponds to restoration of the immune function as measured by CD4+/CD8+ T cell ratio. Although two of the patients went on to survive the other two subsequently died of surgical complications after an initial recovery from SARS-CoV-2 infection.