Breastfeeding mothers were excluded from the clinical trials conducted for vaccines against SARS-CoV-2. Since the start of the vaccination, some doubts have arisen regarding its compatibility with breastfeeding. The aim of this study was to analyse the presence of anti-SARS-CoV-2 antibodies in breast milk and serum (IgG and IgA) of vaccinated breastfeeding women. The main variables of the observational study were: adverse related events after vaccination and determination of the presence of IgG and IgA isotypes antibodies in serum and in breast milk of vaccinated women against the SARS-CoV-2 antigens. Results: 110 breastfeeding mothers were included; 70 women (63.6%) were vaccinated with two doses of BNT162b2, 20 women (18.2%) with two doses of mRNA-1273, and 20 women (18.2%) with a single dose of ChAdOx1-S. Regarding adverse reactions and vaccine safety, 38 women had no adverse reactions; 20 (18.2%) had general malaise or adenopathies; 10 (9.1%) had a headache; and 7 (6.4%) had fever. When analysing IgG antibodies, significantly higher levels of antibodies were found in serum and breast milk from mothers vaccinated with BNT162b2 or mRNA-1273 vs. ChAdOx1-S (p < 0.001 and p = 0.001, respectively). Analysing IgA antibodies, significant differences were found when comparing mean values in serum from mothers vaccinated with BNT162b2 or mRNA-1273 vs. ChAdOx1-S (0.12, 0.16, and 0.02, respectively; p < 0.001) and breast milk of mothers vaccinated when comparing BNT16b2 vs. ChAdOx1-S. All vaccinated breastfeeding mothers had serum anti-S1 IgG antibodies in response to vaccination against SARS-CoV-2, regardless of the commercial vaccine administered. Conclusions: the anti-SARS-CoV-2 vaccines were well tolerated by the mothers and the breastfed infant. In addition, breastfeeding mothers offer their infants IgA and IgG isotype antibodies directed against SARS-CoV-2 protein S in breast milk.

The presence of antinuclear antibodies (ANA) is associated with a wide range of ANA-associated autoimmune rheumatic diseases (AARD). The most commonly method used for the detection of ANA is indirect immunofluorescence (IIF) on HEp-2 cells. This method is very sensitive but unspecific. As a consequence, ANA testing on HEp-2 substrates outside a proper clinical specialist framework may lead to inappropriate referrals to tertiary care specialists and, worst case inappropriate and potentially toxic therapy for the patient. Among ANA, isolated anti-DFS70 antibodies represent a potentially important biomarker that can be clinically used to discriminate AARD from non-AARD patients in ANA IIF positive individuals. Therefore, their presence may avoid unnecessary follow-up testing and referrals. In our study, we investigated if the implementation of a new ANA workup algorithm allowing for the identification of anti-DFS70 antibodies is cost-effective through the reduction of both unnecessary follow-up testing and outpatient clinic visits generated by the clinical suspicion of a potential AARD. None of the 181 patients included with a positive monospecific anti-DFS70 antibody result developed SARD during the follow-up period of 10 years. The reduction in number of tests after ANA and anti-DFS70 positive results was significant for anti-ENA (230 vs. 114 tests; p \ 0.001) and anti-dsDNA antibodies (448 vs. 114 tests; p \ 0.001). In addition, the outpatient clinic visits decreased by 70 % (p \ 0.001). In total, the adoption of the new algorithm including anti-DFS70 antibody testing resulted in a cost saving of 60869.53 € for this pilot study. In conclusion, the use of anti-DFS70 antibodies was clearly cost-efficient in our setting.

Patients with SLE show a broad spectrum of more than 200 autoantibodies. They can be pathogenic, predictive, prognostic or even an epiphenomenon. Here, we discuss different autoantibodies that have not been included in EULAR/ACR 2019 classification criteria. Most of them have been addressed to monitor and detect disease activity and not specifically as classification criteria. Indeed, markers to assess disease activity fluctuate as compared with classification criteria and their validation is different. The development of new methods will probably bring new clinical associations and be evaluated as potential classification criteria.

Autoantibodies are a hallmark of autoimmunity and, specifically, antinuclear antibodies (ANAs) are the most relevant autoantibodies present in systemic autoimmune rheumatic diseases (SARDs). Over the years, different methods from LE cell to HEp-2 indirect immunofluorescence (IIF), solid-phase assays (SPAs), and finally multianalyte technologies have been developed to study ANA-associated SARDs. All of them provide complementary information that is important to provide the most clinically valuable information. The identification of new biomarkers together with multianalyte platforms will help close the so-called “seronegative gap” and to correctly classify and diagnose patients with SARDs. Finally, artificial intelligence and machine learning is an area still to be exploited but in a next future will help to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management.

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

Several protein tyrosine phosphatase non-receptor 22 (PTPN22) single-nucleotide polymorphisms (SNPs) have been significantly related with rheumatoid arthritis (RA) susceptibility. Nevertheless, its potential influence on PTPN22 expression in RA has not been completely elucidated. Furthermore, PTPN22 binds to C-Src tyrosine kinase (CSK) forming a key complex in autoimmunity. However, the information of CSK gene in RA is scarce. In this study, we analyzed the relative PTPN22 and CSK expression in peripheral blood from 89 RA patients and 43 controls to determine if the most relevant PTPN22 (rs2488457, rs2476601 and rs33996649) and CSK (rs34933034 and rs1378942) polymorphisms may influence on PTPN22 and CSK expression in RA. The association between PTPN22 and CSK expression in RA patients and their clinical characteristics was also evaluated. Our study shows for the first time a marked down-regulation of PTPN22 expression in RA patients carrying the risk alleles of PTPN22 rs2488457 and rs2476601 compared to controls (p = 0.004 and p = 0.007, respectively). Furthermore, CSK expression was significantly lower in RA patients than in controls (p < 0.0001). Interestingly, a reduced PTPN22 expression was disclosed in RA patients with ischemic heart disease (p = 0.009). The transcriptional suppression of this PTPN22/CSK complex may have a noteworthy clinical relevance in RA patients.

Background: Low-grade inflammation has been repeatedly associated with both excess weight and psychosis. However, no previous studies have addressed the direct effect of body mass index (BMI) on basal serum cytokines in individuals with first-episode psychosis (FEP). Objectives: The aim of this study is to analyze the effect of BMI on basal serum cytokine levels in FEP patients and control subjects, separating the total sample into two groups: normal-weight and overweight individuals. Methods: This is a prospective and open-label study. We selected 75 FEP patients and 75 healthy controls with similar characteristics to patients according to the following variables: sex, age, and cannabis and tobacco consumption. Both controls and patients were separated into two groups according to their BMI: subjects with a BMI under 25 were considered as normal weight and those with a BMI equal to or more than 25 were considered as overweight. Serum levels of 21 cytokines/chemokines were measured at baseline using the Human High Sensitivity T Cell Magnetic Bead Panel protocol from the Milliplex® Map Kit. We compared the basal serum levels of the 21 cytokines between control and patient groups according to their BMI. Results: In the normal-weight group, IL-8 was the only cytokine that was higher in patients than in the control group (p = 0.001), whereas in the overweight group, serum levels of two pro-inflammatory cytokines (IL-6, p = 0.000; IL-1β, p = 0.003), two chemokines (IL-8, p = 0.001; MIP-1β, p = 0.001), four Th-1 and Th-2 cytokines (IL-13, p = 0.009; IL-2, p = 0.001; IL-7, p = 0.001; IL-12p70, p = 0.010), and one Type-3 cytokine (IL-23, p = 0.010) were higher in patients than in controls. Conclusions: Most differences in the basal serum cytokine levels between patients and healthy volunteers were found in the overweight group. These findings suggest that excess weight can alter the homeostasis of the immune system and therefore may have an additive pro-inflammatory effect on the one produced by psychosis in the central nervous system.

During the COVID-19 pandemic, many studies have been carried out to evaluate different immune system components to search for prognostic biomarkers of the disease. A broad multiparametric antibody panel of cellular and humoral components of the innate and the adaptative immune response in patients with active SARS-CoV-2 infection has been evaluated in this study. A total of 155 patients were studied at admission into our center and were categorized according to the requirement of oxygen therapy as mild or severe (the latter being those with the requirement). The patients with severe disease were older and had high ferritin, D-dimer, C-reactive protein, troponin, interleukin-6 (IL-6) levels, and neutrophilia with lymphopenia at admission. Moreover, the patients with mild symptoms had significantly increased circulating non-classical monocytes, innate lymphoid cells, and regulatory NK cells. In contrast, severe patients had a low frequency of Th1 and regulatory T cells with increased activated and exhausted CD8 phenotype (CD8+CD38+HLADR+ and CD8+CD27−CD28−, respectively). The predictive model included age, ferritin, D-dimer, lymph counts, C4, CD8+CD27−CD28−, and non-classical monocytes in the logistic regression analysis. The model predicted severity with an area under the curve of 78%. Both innate and adaptive immune parameters could be considered potential predictive biomarkers of the prognosis of COVID-19 disease.