The Importance of COVID-19/Influenza Vaccines Co-Administration: An Essential Public Health Tool.

Vaccine co-administration is an important tool with several advantages for public health, among which is the increase of vaccination coverage, as well as economic and logistical benefits. The purpose of this study was to assess and compare the immune response to the COVID-19 first booster dose in healthcare workers (HCWs) who chose co-administration and in HCWs who received only COVID-19 vaccination and to investigate personal opinions about the experience of co-administration. We carried out a retrospective analysis involving two groups of HCWs, both vaccinated with the complete primary cycle and the first booster dose of the COVID-19 vaccine, but one of them was also vaccinated, at the same time as the first booster dose, with the influenza vaccine. Active phone calls were also performed, and specific questions about the onset of side effects and general opinions were asked. A good immune response was found in both two groups without any statistically significant difference in the immune response. No severe reactions occurred in either group. A greater part of the sample was completely satisfied, and they would do it again. Our findings are totally in favor of the co-administration, considering the many positive aspects provided by administering, at the same time, more vaccines.

Reactogenicity and Humoral Immune Response after Heterologous Vaxzevria/Comirnaty Vaccination in a Group of Individuals Vaccinated in the AOU Policlinic "G. Martino" (Messina, Italy): A Retrospective Cohort Study.

On 11 June 2021, the Italian Ministry of Health authorized the heterologous vaccination schedule. The goals of our retrospective study were to (a) evaluate the undesirable effects after the administration of Vaxzevria and Comirnaty vaccines; (b) evaluate the antibody response after 28 days from the administration of the second dose; and (c) compare the antibody responses after the homologous and heterologous vaccination regimens. The undesirable effects were collected using a survey; IgG Spike was quantified using the electrochemiluminescence method; the comparison between the antibody responses was carried out using the sample of a homologous vaccine schedule previously analyzed. Pain at the injection site is the most common undesirable effect after the administration of both vaccines (62.1% after Vaxzevria vs. 82.75% after Comirnaty); swelling at the injection site is more frequent after the administration of Vaxzevria than after the administration of Comirnaty: (15.52% vs. 5.17%); headache is more frequent in women than in men for both the vaccination types (p < 0.05); 49.09% of the sample reported IgG Spike ≥ 12,500 U/mL; the antibody titer of the heterologous schedule is higher than that of the homologous vaccination. Our study demonstrated that the undesirable effects after the administration of the second dose are less frequent and less severe than after the administration of the first dose, and that the immunogenicity of the heterologous vaccinations is higher than that of the homologous ones.

COVID-19 serological evaluation in a cohort of Vaccinated and Seropositive healthcare workers.

INTRODUCTION: Severe Acquired Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) infection represents an unprecedented public health problem and, at present, vaccination is the only weapon available to combat the infection. The simplest and most immediate method to quantify the response of the subject's immune system to vaccination and / or infection is the serological assessment of the antibody titer. The objective of our study was 1) to evaluate the presence of antibody responses in a sample of healthcare workers subjected to a complete vaccination course as per ministerial provisions (double dose for negatives and single dose for ex-SARS-CoV subjects -2 positive) with Comirnaty vaccine (Pfizer / BioNTech) 2) evaluate the presence of statistically significant associations for sex, age and previous positive swab. MATERIALS AND METHODS: the antibody levels of both nucleocapsid antibodies and anti-Sars-CoV2 Spike antibodies of the study subjects were examined with the electrochemiluminescent immunoassay (ECLIA) method developed by Roche®. The cut-off value, as suggested by the manufacturer, for anti-nucleocapsid antibodies was 1 COI, while the Ig Spike value was 0.8 I / mL. The study sample was stratified by age (≤45 years, 46-55, ≥56 years old), previous positive molecular swab, gender and IgG S1 / S2 values ​​at the completed vaccination course (≤200, ≥200 AU / mL ). Statistical analyzes were carried out with the R software. RESULTS: almost all of the sample (89.45%) showed IgG Spike values> 200 AU / mL with statistically significant associations in relation to sex (greater in females, p≤0.05), to previous swab positivity in the presence of a vaccine dose (n = 44; p <0.001) and at age (with greater antibody response in subjects under 45; p <0.001). DISCUSSION AND CONCLUSIONS: The current study confirms what is reported in the literature. In the light of the results obtained, it could be interesting to promote studies that evaluate the antibody titers trend over time a) in women of childbearing age and postmenopausal age b) in particular categories of subjects with chronic degenerative diseases to assess the actual need for doses booster, it being understood that the immune system response is guaranteed by both cellular and humoral immunity and that the antibody titer does not faithfully reflect the protection obtained.

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features.

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a "mixed VD-AD dementia" (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.

Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia.

Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls-HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.

Metabolic syndrome increases carotid artery stiffness: the Northern Manhattan Study.

BACKGROUND: Arterial stiffness, an intermediate pre-clinical marker of atherosclerosis, has been associated with an increased risk of stroke and cardiovascular disease. The metabolic syndrome and its components are established cardiovascular disease risk factors and may also increase arterial stiffness; however, data regarding this are limited. AIM: The goal of this study was to determine the association between the metabolic syndrome and carotid artery stiffness in an elderly multi-ethnic cohort. METHODS: Carotid artery stiffness was assessed by carotid ultrasound as part of the Northern Manhattan Study, a prospective population-based cohort of stroke-free individuals. Carotid artery stiffness was calculated as [ln(systolic BP/diastolic BP)/strain], where strain was [(systolic diameter-diastolic diameter)/diastolic diameter]. Metabolic syndrome was defined by the National Cholesterol Education Program: Adult Treatment Panel III criteria. LogSTIFF was analysed as the dependent variable in linear regression models, adjusting for demographics, education, current smoking, presence of carotid plaque and intima-media thickness. RESULTS: Carotid artery stiffness was analysed in 1133 Northern Manhattan Study subjects (mean age 65 +/- 9 years; 61% women; 58% Hispanic, 22% Black and 20% Caucasian). The prevalence of the metabolic syndrome was 49%. The mean LogSTIFF was 2.01 +/- 0.61 among those with the metabolic syndrome and 1.90 +/- 0.59 among those without the metabolic syndrome (P=0.003). The metabolic syndrome was significantly associated with increased logSTIFF in the final adjusted model (parameter estimate beta=0.100, P=0.01). Among individual metabolic syndrome components, waist circumference and elevated blood pressure were most significantly associated with a mean increase in logSTIFF (P<0.01). CONCLUSION: The metabolic syndrome is significantly associated with increased carotid artery stiffness in a multiethnic population. Increased carotid artery stiffness may, in part, explain a high risk of stroke among individuals with the metabolic syndrome.