Permeation Protection by Waterproofing Mucosal Membranes.

The permeability of the oral or nasal mucosa is higher than that of the skin. Mucosa permeability depends mainly on the thickness and keratinization degree of the tissues. Their permeability barrier is conditioned by the presence of certain lipids. This work has the main aim of reinforcing the barrier effect of oral mucosa with a series of formulations to reduce permeation. Transmembrane water loss of different formulations was evaluated, and three of them were selected to be tested on the sublingual mucosa permeation of drugs. Caffeine, ibuprofen, dexamethasone, and ivermectin were applied on porcine skin, mucosa, and modified mucosa in order to compare the effectiveness of the formulations. A similar permeation profile was obtained in the different membranes: caffeine > ibuprofen~dexamethasone > ivermectin. The most efficient formulation was a liposomal formulation composed of lipids that are present in the skin stratum corneum. Impermeability provided by this formulation was notable mainly for the low-molecular-weight compounds, decreasing their permeability coefficient by between 40 and 80%. The reinforcement of the barrier function of mucosa provides a reduction or prevention of the permeation of different actives, which could be extrapolated to toxic compounds such as viruses, contaminants, toxins, etc.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Atypical progressive multifocal leukoencephalopathy in a patient with antisynthetase syndrome.

Antisynthetase syndrome is a disorder belonging to the dermatomyositis/polymyositis group, with high rates of morbidity and mortality. We herein present the case of a 71-year-old man who was diagnosed with antisynthetase syndrome and treated with rituximab. Almost three years later, the patient showed right-sided hemiparesis that ultimately progressed to complete hemiplegia and advancing cognitive deterioration with a poor clinical outcome. The neuropathological diagnosis was progressive multifocal leukoencephalopathy. Treatment with rituximab for antisynthetase syndrome itself plays a fundamental role in the development of infectious complications.