Biosensors for Detection and Monitoring of Joint Infections

The aim of this review is to assess the use of biosensors in the diagnosis and monitoring of joint infection (JI). JI is worldwide considered a significant cause of morbidity and mortality in developed countries. Due to the progressive ageing of the global population, the request for joint replacement increases, with a significant rise in the risk of periprosthetic joint infection (PJI). Nowadays, the diagnosis of JI is based on clinical and radiological findings. Nuclear imaging studies are an option but are not cost-effective. Serum inflammatory markers and the analysis of the aspirated synovial fluid are required to confirm the diagnosis. However, a quick and accurate diagnosis of JI may remain elusive as no rapid and highly accurate diagnostic method was validated. A comprehensive search on Medline, EMBASE, Scopus, CINAH, CENTRAL, Google Scholar, and Web of Science was conducted from the inception to June 2021. The PRISMA guidelines were used to improve the reporting of the review. The MINORS was used for quality assessment. From a total of 155 studies identified, only four articles were eligible for this study. The main advantages of biosensors reported were accuracy and capability to detect bacteria also in negative culture cases. Otherwise, due to the few studies and the low level of evidence of the papers included, it was impossible to find significant results. Therefore, further high-quality studies are required

Baseline Plasma Gas6 Protein Elevation Predicts Adverse Outcomes in Hospitalized COVID-19 Patients

: Reliable biomarkers allowing early patients' stratification for the risk of adverse outcomes in COVID-19 are lacking. Gas6, together with its tyrosine kinase receptors named TAM, is involved in the regulation of immune homeostasis, fibrosis, and thrombosis. Our aim was to evaluate whether Gas6, sAxl, and sMerTK could represent early predictors of disease evolution either towards a negative (death or need of ICU admission) or a positive (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. To this purpose, between January and May 2021 (corresponding to third pandemic wave in Italy), 139 consecutive SARS-CoV-2 positive patients were enrolled in a prospective observational study. Plasma levels of these molecules were measured by ELISA at the time of hospitalization and after 7 and 14 days. We observed that higher plasma Gas6 concentrations at hospital admission were associated with a worsening in clinical conditions while lower sMerTK concentrations at baseline and after 7 days of hospitalization were associated with a more favorable outcome. At multivariate analysis, after correction for demographic and COVID-19 severity variables (NEWS2 and PiO2/FiO2), only Gas6 measured at baseline predicted an adverse prognosis with an odds ratio of 1.03 (C.I. 1.01-10.5). At ROC curve analysis, baseline Gas6 levels higher than 58.0 ng/ml predicted a severe disease evolution with 53.3% sensitivity and 77.6% specificity (area under the curve 0.653, p = 0.01, likelihood ratio of 2.38, IQR: 1.46-3.87). Taken together, these results support the hypothesis that a dysregulation in the Gas6/TAM axis could play a relevant role in modulating the course of COVID-19 and suggest that plasma Gas6 may represent a promising prognostic laboratory parameter for this condition

Efficacy of advanced hybrid closed loop systems in cystic fibrosis related diabetes: a pilot study

Background and aimsCystic fibrosis related diabetes (CFRD) is correlated with worsening of nutritional status and greater deterioration of lung function. The role of new technologies for the treatment of CFRD is little explored. The aim of the study was to evaluate the efficacy of Advanced Hybrid Closed Loop (AHCL) systems on glycemic control in CF patients.MethodsA single-center retrospective study on CFRD patients using AHCL systems was performed. Glycated hemoglobin (HbA1c) values and Continuous Glucose Monitoring (CGM) metrics were collected at T0 (AHCL placement), T1 (1-month), T2 (6-months) and T3 (1-year) to evaluate glycemic control.Results10 patients were included in the study. Data showed a reduction of HbA1c value (7.31 ± 0.34 to 6.35 ± 1.00; p=0.03), glycemic variability (p=0.05) and insulin requirement (p=0.03). The study population reached American Diabetes Association (ADA) recommended glycemic targets at 1-year. An increase in the Time in Range (TIR) and a reduction in time in hyperglycemia were also observed, although not statistically significant.ConclusionsIn patients with CFRD, the use of AHCL leads to an improvement in glycemic control in terms of HbA1c and glycemic variability. The increase in TIR and the reduction of time in hyperglycemia, although not statistically significant, are extremely encouraging from a clinical point of view. Further studies with a larger population and a longer follow-up are needed. The results of this study demonstrate the importance of proposing the use of AHCL even in CF patients, who could benefit from glycemic improvement also in terms of nutritional status and respiratory function

Baseline Plasma Osteopontin Protein Elevation Predicts Adverse Outcomes in Hospitalized COVID-19 Patients

More than three years have passed since the first case, and COVID-19 is still a health concern, with several open issues such as the lack of reliable predictors of a patient's outcome. Osteopontin (OPN) is involved in inflammatory response to infection and in thrombosis driven by chronic inflammation, thus being a potential biomarker for COVID-19. The aim of the study was to evaluate OPN for predicting negative (death or need of ICU admission) or positive (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. We enrolled 133 hospitalized, moderate-to-severe COVID-19 patients in a prospective observational study between January and May 2021. Circulating OPN levels were measured by ELISA at admission and at day 7. The results showed a significant correlation between higher plasma concentrations of OPN at hospital admission and a worsening clinical condition. At multivariate analysis, after correction for demographic (age and gender) and variables of disease severity (NEWS2 and PiO2/FiO2), OPN measured at baseline predicted an adverse prognosis with an odds ratio of 1.01 (C.I. 1.0-1.01). At ROC curve analysis, baseline OPN levels higher than 437 ng/mL predicted a severe disease evolution with 53% sensitivity and 83% specificity (area under the curve 0.649, p = 0.011, likelihood ratio of 1.76, (95% confidence interval (CI): 1.35-2.28)). Our data show that OPN levels determined at the admission to hospital wards might represent a promising biomarker for early stratification of patients' COVID-19 severity. Taken together, these results highlight the involvement of OPN in COVID-19 evolution, especially in dysregulated immune response conditions, and the possible use of OPN measurements as a prognostic tool in COVID-19

Effect of Lactoferrin on Clinical Outcomes of Hospitalized Patients with COVID-19: The LAC Randomized Clinical Trial

: As lactoferrin is a nutritional supplement with proven antiviral and immunomodulatory abilities, it may be used to improve the clinical course of COVID-19. The clinical efficacy and safety of bovine lactoferrin were evaluated in the LAC randomized double-blind placebo-controlled trial. A total of 218 hospitalized adult patients with moderate-to-severe COVID-19 were randomized to receive 800 mg/die oral bovine lactoferrin (n = 113) or placebo (n = 105), both given in combination with standard COVID-19 therapy. No differences in lactoferrin vs. placebo were observed in the primary outcomes: the proportion of death or intensive care unit admission (risk ratio of 1.06 (95% CI 0.63-1.79)) or proportion of discharge or National Early Warning Score 2 (NEWS2) ≤ 2 within 14 days from enrollment (RR of 0.85 (95% CI 0.70-1.04)). Lactoferrin showed an excellent safety and tolerability profile. Even though bovine lactoferrin is safe and tolerable, our results do not support its use in hospitalized patients with moderate-to-severe COVID-19

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk

Automatic Evaluation of Progression Angle and Fetal Head Station through Intrapartum Echographic Monitoring

Labor progression is routinely assessed through transvaginal digital inspections, meaning that the clinical decisions taken during the most delicate phase of pregnancy are subjective and scarcely supported by technological devices. In response to such inadequacies, we combined intrapartum echographic acquisitions with advanced tracking algorithms in a new method for noninvasive, quantitative, and automatic monitoring of labor. Aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithm in assessing progression angle (PA) and fetal head station (FHS). A cohort of 10 parturients underwent conventional labor management, with additional translabial echographic examinations after each uterine contraction. PA and FHS were evaluated by our automatic algorithm on the acquired images. Additionally, an experienced clinical sonographer, blinded regarding the algorithm results, quantified on the same acquisitions of the two parameters through manual contouring, which were considered as the standard reference in the evaluation of automatic algorithm and routine method accuracies. The automatic algorithm (mean error ± 2SD) provided a global accuracy of 0.9±4.0 mm for FHS and 4° ± 9° for PA, which is far above the diagnostic ability shown by the routine method, and therefore it resulted in a reliable method for earlier identification of abnormal labor patterns in support of clinical decisions