Physiology of Midkine and Its Potential Pathophysiological Role in COVID-19

SARS-CoV2 infection not only causes abnormal severe pneumonia but also induces other relevant pathophysiological effects on several tissues and organs. In this regard, the clinical complications observed in COVID-19 include acute coronary syndrome, pulmonary thromboembolism, myocarditis and, in the severe cases, the occurrence of disseminated intravascular coagulation. Literature on COVID-19 highlighted the central role of the Renin Angiotensin Aldosterone System in the determinism of SARS-CoV2 cellular internalization in the target tissues. Lung degeneration and respiratory distress appear to be dependent on the perturbance of physiological mechanisms, such as the uncontrolled release of pro-inflammatory cytokines, a dysregulation of the fibrinolytic coagulative cascade and the hyperactivation of immune effector cells. In this mini review, we address the physiology of Midkine, a growth factor able to bind heparin, and its pathophysiological potential role in COVID-19 determinism. Midkine increases in many inflammatory and autoimmune conditions and correlates with several dysfunctional immune-inflammatory responses that appear to show similarities with the pathophysiological elicited by SARS-CoV2. Midkine, together with its receptor, could facilitate the virus entry, fostering its accumulation and increasing its affinity with Ace2 receptor. We also focus on Netosis, a particular mechanism of pathogen clearance exerted by neutrophils, which under certain pathological condition becomes dysfunctional and can cause tissue damage. Moreover, we highlight the mechanism of autophagy that the new coronavirus could try to escape in order to replicate itself, as well as on pulmonary fibrosis induced by hypoxia and on the release of cytokines and mediators of inflammation, correlating the interplay between Midkine and SARS-CoV2

Fast COVID-19 vaccine effectiveness estimation on the basis of recovered individual propensity to be vaccinated

The main purpose of this article is to point out to the CDCs of the various governments, as well as to independent agencies and press offices, the need and advantages of correcting incidence data of the infection, as well as to propose a practical equation to calculate vaccine effectiveness, based on the count of recovered subjects who have not yet been vaccinated. This equation can be used to accompany data on infection incidence aimed at the general public, as well as an “easy-to-access” formula to be used for the official and institutional communication of the CDCs

Secondary immune-mediated thrombocytopenia in dogs naturally infected by Leishmania infantum.

Forty-four dogs naturally infected by Leishmania infantum were divided into two groups: 20 thrombocytopenic dogs with fewer than 150 x 10(9) platelets/l, and 24 non-thrombocytopenic dogs with more than 200 x 10(9) platelets/l. Ten clinically healthy dogs were used as controls. A haematological profile was obtained and the dogs' serum was used to assess the presence of platelet-binding IgM and IgG antibodies using a flow cytometry technique. Nineteen of the 20 thrombocytopenic dogs, and 13 of the 24 non-thrombocytopenic dogs had detectable levels of platelet-binding immunoglobulins, but none of the control dogs did so. The differences were significantly different for both IgM and IgG platelet-binding antibodies

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56(bright) and mature cytotoxic NK CD56(dim). Therefore, the ability to discriminate CD56(dim) from CD56(bright) could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56(bright) to CD56(dim). We trained our ML algorithm with sorted NK cell subclasses (>= 86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56(bright) to CD56(dim) and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy

Serafino Zappacosta: An Enlightened Mentor and Educator

With this article, the authors aim to honor the memory of Serafino Zappacosta, who had been their mentor during the early years of their career in science. The authors discuss how the combination of Serafino Zappacosta's extraordinary commitment to teaching and passion for science created a fostering educational environment that led to the creation of the “Ruggero Ceppellini Advanced School of Immunology.” The review also illustrates how the research on the MHC and the inspirational scientific context in the Zappacosta's laboratory influenced the authors' early scientific interests, and subsequent professional work as immunologists

Pro-Inflammatory and Immunological Profile of Dogs with Myxomatous Mitral Valve Disease

Myxomatous mitral valve disease (MMVD) is a very frequently acquired cardiac disease in dog breeds and is responsible for congestive heart failure (CHF). The involvement of the immune system and pro-inflammatory cytokines in dogs with CHF due to mitral valve disease has not yet been extensively investigated. Here, we investigate the role of pro-inflammatory cytokines and the dysfunction of the immune system in dogs with different stages of severity through the blood assessment of CD4+FoxP3+regulatory T cells (Treg) cells, leptin, tumor necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6 pro-inflammatory cytokines, and immunological and echocardiographic parameters. A total of 36 cardiopathic dogs, 14 females and 22 males, with MMVD were included. Mean age and body weight (BW) at the time of enrollment were 10.7 ± 2.77 years and 10.9 ± 6.69 kg, respectively. For the comparison of the pro-inflammatory and immunological parameters, two groups of healthy dogs were also established. Control group 1 consisted of young animals (n. 11; 6 females and 5 males), whose age and mean weight were 4.1 ± 0.82 years and 13.8 ± 4.30 kg, respectively. Control group 2 consisted of elderly dogs (n. 12; 6 females and 6 males), whose age and BW were 9.6 ± 0.98 years and 14.8 ± 6.15 kg, respectively. Of particular interest, an increase in Treg cells was observed in the cohort of MMVD dogs, as compared to the healthy dogs, as Treg cells are involved in the maintenance of peripheral tolerance, and they are involved in etiopathogenetic and pathophysiological mechanisms in the dog. On the other hand, TNF-α, IL-1β, and IL-6 significantly increased according to the severity of the disease in MMVD dogs. Furthermore, the positive correlation between IL-6 and the left ventricle diastolic volume suggests that inflammatory activation may be involved in cardiac remodeling associated with the progressive volumetric overload in MMVD

Superoxide dismutase-1 intracellular content in T lymphocytes associates with increased regulatory T cell level in multiple sclerosis subjects undergoing immune-modulating treatment

Reactive oxygen species (ROS) participate in the T-cell activation processes. ROS-dependent regulatory networks are usually mediated by peroxides, which are more stable and able to freely migrate inside cells. Superoxide dismutase (SOD)-1 represents the major physiological intracellular source of peroxides. We found that antigen-dependent activation represents a triggering element for SOD-1 production and secretion by human T lymphocytes. A deranged T-cell proinflammatory response characterizes the pathogenesis of multiple sclerosis (MS). We previously observed a decreased SOD-1 intracellular content in leukocytes of MS individuals at diagnosis, with increasing amounts of such enzyme after interferon (IFN)-b 1b treatment. Here, we analyzed in depth SOD-1 intracellular content in T cells in a cohort of MS individuals undergoing immune-modulating treatment. Higher amounts of the enzyme were associated with increased availability of regulatory T cells (Treg) prefer-entially expressing Foxp3-exon 2 (Foxp3-E2), as described for effective Treg. In vitro administration of recombinant human SOD-1 to activated T cells, significantly increased their IL-17 production, while SOD-1 molecules lacking dismutase activity were unable to interfere with cytokine production by activated T cells in vitro. Furthermore, hydrogen peroxide addition was observed to mimic, in vitro, the SOD-1 effect on IL-17 production. These data add SOD-1 to the molecules involved in the molecular pathways contributing to re-shaping the T-cell cytokine profile and Treg differentiation

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56bright and mature cytotoxic NK CD56dim. Therefore, the ability to discriminate CD56dim from CD56bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56bright to CD56dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56bright to CD56dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy