Biomimetic Adhesive Micropatterned Hydrogel Patches for Drug Release.

The optimized physical adhesion between bees' leg hairs and pollen grains-whereby the latter's diameter aligns with the spacing between the hairs-has previously inspired the development of a biomimetic drug dressing. Combining this optimized process with the improved natural mussels' adhesion in wet environments in a dual biomimetic process, it is herein proposed the fabrication of a natural-derived micropatterned hydrogel patch of methacrylated laminarin (LAM-MET), with enriched drug content and improved adhesiveness, suitable for applications like wound healing. Enhanced adhesion is accomplished by modifying LAM-MET with hydroxypyridinone groups, following the patch microfabrication by soft lithography and UV/vis-irradiation, resulting in a membrane with micropillars with a high aspect ratio. Following the biomimetics rational, a drug patch is engineered by combining the microfabricated dressing with drug particles milled to fit the spaces between pillars. Controlled drug release is achieved, together with inherent antibacterial activity against Escherichia coli and Pseudomonas aeruginosa, and enhanced biocompatibility using the bare micropatterned patches. This new class of biomimetic dressings overcomes the challenges of current patches, like poor mechanical properties and biocompatibility, limited adhesiveness and drug dosage, and lack of prolonged antimicrobial activity, opening new insights for the development of high drug-loaded dressings with improved patient compliance.

Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis.

Background and objectives: We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19. Methods: A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test. Results: Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04. Conclusion: ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality.

Low skeletal muscle radiodensity and neutrophil-to-lymphocyte ratio as predictors of poor outcome in patients with COVID-19.

Inflammatory states and body composition changes are associated with a poor prognosis in many diseases, but their role in coronavirus disease 2019 (COVID-19) is not fully understood. To assess the impact of low skeletal muscle radiodensity (SMD), high neutrophil-to-lymphocyte ratio (NLR) and a composite score based on both variables, on complications, use of ventilatory support, and survival in patients with COVID-19. Medical records of patients hospitalized between May 1, 2020, and July 31, 2020, with a laboratory diagnosis of COVID-19 who underwent computed tomography (CT) were retrospectively reviewed. CT-derived body composition measurements assessed at the first lumbar vertebra level, and laboratory tests performed at diagnosis, were used to calculate SMD and NLR. Prognostic values were estimated via univariate and multivariate logistic regression analyses and the Kaplan-Meier curve. The study was approved by the local Institutional Review Board (CAAE 36276620.2.0000.5404). A total of 200 patients were included. Among the patients assessed, median age was 59 years, 58% were men and 45% required ICU care. A total of 45 (22.5%) patients died. Multivariate logistic analysis demonstrated that a low SMD (OR 2.94; 95% CI 1.13-7.66, P = 0.027), high NLR (OR 3.96; 95% CI 1.24-12.69, P = 0.021) and both low SMD and high NLR (OR 25.58; 95% CI 2.37-276.71, P = 0.008) combined, were associated with an increased risk of death. Patients who had both low SMD and high NLR required more mechanical ventilation (P < 0.001) and were hospitalized for a longer period (P < 0.001). Low SMD, high NLR and the composite score can predict poor prognosis in patients with COVID-19, and can be used as a tool for early identification of patients at risk. Systemic inflammation and low muscle radiodensity are useful predictors of poor prognosis, and the assessment of these factors in clinical practice should be considered.

Multi-layer pre-vascularized magnetic cell sheets for bone regeneration.

The lack of effective strategies to produce vascularized 3D bone transplants in vitro, hampers the development of thick-constructed bone, limiting the translational of lab-based engineered system to clinical practices. Cell sheet (CS) engineering techniques provide an excellent microenvironment for vascularization since the technique can maintain the intact cell matrix, crucial for angiogenesis. In an attempt to develop hierarchical vascularized 3D cellular constructs, we herein propose the construction of stratified magnetic responsive heterotypic CSs by making use of iron oxide nanoparticles previously internalized within cells. Magnetic force-based CS engineering allows for the construction of thick cellular multilayers. Results show that osteogenesis is achieved due to a synergic effect of human umbilical vein endothelial cells (HUVECs) and adipose-derived stromal cells (ASCs), even in the absence of osteogenic differentiating factors. Increased ALP activity, matrix mineralization, osteopontin and osteocalcin detection were achieved over a period of 21 days for the heterotypic CS conformation (ASCs/HUVECs/ASCs), over the homotypic one (ASCs/ASCs), corroborating our findings. Moreover, the validated crosstalk between BMP-2 and VEGF releases triggers not only the recruitment of blood vessels, as demonstrated in an in vivo CAM assay, as well as the osteogenesis of the 3D cell construct. The in vivo angiogenic profile also demonstrated preserved human vascular structures and human cells showed the ability to migrate and integrate within the chick vasculature.

Low-dose transdermal hormone therapy does not interfere with the blood pressure of hypertensive menopausal women: a pilot study.

OBJECTIVES: To determine the effects of low-dose transdermal hormone therapy (HT) on systolic (SBP) and diastolic (DBP) blood pressure (BP) evaluated by 24-h ambulatory blood pressure monitoring (ABPM) in hypertensive postmenopausal women. METHODS: The study was conducted on 24 hypertensive postmenopausal women aged, on average, 54 years and under treatment with enalapril maleate (10-20 mg/day) combined or not with hydrochlorothiazide (25 mg/day). Thirteen women used a transdermal adhesive containing estradiol and norethisterone (25 and 125 mug active substance/day, respectively) and 11 did not receive HT. ABPM, lipid profile, and climacteric symptoms were evaluated before and 3 and 6 months after treatment. RESULTS: After 3 and 6 months of follow-up, there was a statistically significant reduction of the Blatt-Kupperman menopausal index in the treated group (19.6+/-8.3 vs. 9.6+/-5.9 vs. 9.7+/-7.0; P=0.01). No significant difference in any of the ABPM variables (areas under the systolic and diastolic curves, mean SBP and DBP, SBP and DBP loads and wakefulness-sleep variation) or in the lipid profile was observed between or within groups at the three time points studied. CONCLUSION: Low-dose transdermal HT administered for 6 months was effective in improving climacteric symptoms and did not change BP values or circadian pattern in postmenopausal women with mild-to-moderate arterial hypertension taking antihypertensive medications.