COVID-19 and atrial fibrillation: Intercepting lines.

Almost 20% of COVID-19 patients have a history of atrial fibrillation (AF), but also a new-onset AF represents a frequent complication in COVID-19. Clinical evidence demonstrates that COVID-19, by promoting the evolution of a prothrombotic state, increases the susceptibility to arrhythmic events during the infective stages and presumably during post-recovery. AF itself is the most frequent form of arrhythmia and is associated with substantial morbidity and mortality. One of the molecular factors involved in COVID-19-related AF episodes is the angiotensin-converting enzyme (ACE) 2 availability. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses ACE2 to enter and infect multiple cells. Atrial ACE2 internalization after binding to SARS-CoV-2 results in a raise of angiotensin (Ang) II, and in a suppression of cardioprotective Ang(1-7) formation, and thereby promoting cardiac hypertrophy, fibrosis and oxidative stress. Furthermore, several pharmacological agents used in COVID-19 patients may have a higher risk of inducing electrophysiological changes and cardiac dysfunction. Azithromycin, lopinavir/ritonavir, ibrutinib, and remdesivir, used in the treatment of COVID-19, may predispose to an increased risk of cardiac arrhythmia. In this review, putative mechanisms involved in COVID-19-related AF episodes and the cardiovascular safety profile of drugs used for the treatment of COVID-19 are summarized.

The Spread of SARS-CoV-2 Omicron Variant in CALABRIA: A Spatio-Temporal Report of Viral Genome Evolution.

We investigated the evolution of SARS-CoV-2 spread in Calabria, Southern Italy, in 2022. A total of 272 RNA isolates from nasopharyngeal swabs of individuals infected with SARS-CoV-2 were sequenced by whole genome sequencing (N = 172) and/or Sanger sequencing (N = 100). Analysis of diffusion of Omicron variants in Calabria revealed the prevalence of 10 different sub-lineages (recombinant BA.1/BA.2, BA.1, BA.1.1, BA.2, BA.2.9, BA.2.10, BA.2.12.1, BA.4, BA.5, BE.1). We observed that Omicron spread in Calabria presented a similar trend as in Italy, with some notable exceptions: BA.1 disappeared in April in Calabria but not in the rest of Italy; recombinant BA.1/BA.2 showed higher frequency in Calabria (13%) than in the rest of Italy (0.02%); BA.2.9, BA.4 and BA.5 emerged in Calabria later than in other Italian regions. In addition, Calabria Omicron presented 16 non-canonical mutations in the S protein and 151 non-canonical mutations in non-structural proteins. Most non-canonical mutations in the S protein occurred mainly in BA.5 whereas non-canonical mutations in non-structural or accessory proteins (ORF1ab, ORF3a, ORF8 and N) were identified in BA.2 and BA.5 sub-lineages. In conclusion, the data reported here underscore the importance of monitoring the entire SARS-CoV-2 genome.

Dynamics of Viral Infection and Evolution of SARS-CoV-2 Variants in the Calabria Area of Southern Italy

In this study, we report on the results of SARS-CoV-2 surveillance performed in an area of Southern Italy for 12 months (from March 2021 to February 2022). To this study, we have sequenced RNA from 609 isolates. We have identified circulating VOCs by Sanger sequencing of the S gene and defined their genotypes by whole-genome NGS sequencing of 157 representative isolates. Our results indicated that B.1 and Alpha were the only circulating lineages in Calabria in March 2021;while Alpha remained the most common variant between April 2021 and May 2021 (90 and 73%, respectively), we observed a concomitant decrease in B.1 cases and appearance of Gamma cases (6 and 21%, respectively);C.36.3 and Delta appeared in June 2021 (6 and 3%, respectively);Delta became dominant in July 2021 while Alpha continued to reduce (46 and 48%, respectively). In August 2021, Delta became the only circulating variant until the end of December 2021. As of January 2022, Omicron emerged and took over Delta (72 and 28%, respectively). No patient carrying Beta, Iota, Mu, or Eta variants was identified in this survey. Among the genomes identified in this study, some were distributed all over Europe (B1_S477N, Alpha_L5F, Delta_T95, Delta_G181V, and Delta_A222V), some were distributed in the majority of Italian regions (B1_S477N, B1_Q675H, Delta_T95I and Delta_A222V), and some were present mainly in Calabria (B1_S477N_T29I, B1_S477N_T29I_E484Q, Alpha_A67S, Alpha_A701S, and Alpha_T724I). Prediction analysis of the effects of mutations on the immune response (i.e., binding to class I MHC and/or recognition of T cells) indicated that T29I in B.1 variant;A701S in Alpha variant;and T19R in Delta variant were predicted to impair binding to class I MHC whereas the mutations A67S identified in Alpha;E484K identified in Gamma;and E156G and ΔF157/R158 identified in Delta were predicted to impair recognition by T cells. In conclusion, we report on the results of SARS-CoV-2 surveillance in Regione Calabria in the period between March 2021 and February 2022, identified variants that were enriched mainly in Calabria, and predicted the effects of identified mutations on host immune response.

Whole-genome analysis of SARS-CoV-2 in a 2020 infection cluster in a nursing home of Southern Italy.

BACKGROUND: Nursing homes have represented important hotspots of viral spread during the initial wave of COVID-19 pandemics. The proximity of patients inside nursing homes allows investigate the dynamics of viral transmission, which may help understand SARS-Cov2 biology and spread. METHODS: SARS-CoV-2 viral genomes obtained from 46 patients infected in an outbreak inside a nursing home in Calabria region (South Italy) were analyzed by Next Generation Sequencing. We also investigated the evolution of viral genomes in 8 patients for which multiple swabs were available. Phylogenetic analysis and haplotype reconstruction were carried out with IQ-TREE software and RegressHaplo tool, respectively. RESULTS: All viral strains isolated from patients infected in the nursing home were classified as B.1 lineage, clade G. Overall, 14 major single nucleotide variations (SNVs) (frequency > 80%) and 12 minor SNVs (frequency comprised between 20% and 80%) were identified with reference to the Wuhan-H-1 sequence (NC_045512.2). All patients presented the same 6 major SNVs: D614G in the S gene; P4715L, ntC3037T (F924F) and S5398P in Orf1ab gene; ntC26681T (F53F) in the M gene; and ntC241T in the non-coding UTR region. However, haplotype reconstruction identified a founder haplotype (Hap A) in 36 patients carrying only the 6 common SNVs indicated above, and 10 other haplotypes (Hap BK) derived from Hap A in the remaining 10 patients. Notably, no significant association between a specific viral haplotype and clinical parameters was found. CONCLUSION: The predominant viral strain responsible for the infection in a nursing home in Calabria was the B.1 lineage (clade G). Viral genomes were classified into 11 haplotypes (Hap A in 36 patients, Hap BK in the remaining patients).

From Spheroids to Organoids: The Next Generation of Model Systems of Human Cardiac Regeneration in a Dish.

Organoids are tiny, self-organized, three-dimensional tissue cultures that are derived from the differentiation of stem cells. The growing interest in the use of organoids arises from their ability to mimic the biology and physiology of specific tissue structures in vitro. Organoids indeed represent promising systems for the in vitro modeling of tissue morphogenesis and organogenesis, regenerative medicine and tissue engineering, drug therapy testing, toxicology screening, and disease modeling. Although 2D cell cultures have been used for more than 50 years, even for their simplicity and low-cost maintenance, recent years have witnessed a steep rise in the availability of organoid model systems. Exploiting the ability of cells to re-aggregate and reconstruct the original architecture of an organ makes it possible to overcome many limitations of 2D cell culture systems. In vitro replication of the cellular micro-environment of a specific tissue leads to reproducing the molecular, biochemical, and biomechanical mechanisms that directly influence cell behavior and fate within that specific tissue. Lineage-specific self-organizing organoids have now been generated for many organs. Currently, growing cardiac organoid (cardioids) from pluripotent stem cells and cardiac stem/progenitor cells remains an open challenge due to the complexity of the spreading, differentiation, and migration of cardiac muscle and vascular layers. Here, we summarize the evolution of biological model systems from the generation of 2D spheroids to 3D organoids by focusing on the generation of cardioids based on the currently available laboratory technologies and outline their high potential for cardiovascular research.

Combined lymphocyte/monocyte count, D-dimer and iron status predict COVID-19 course and outcome in a long-term care facility.

BACKGROUND: The Sars-CoV-2 can cause severe pneumonia with multiorgan disease; thus, the identification of clinical and laboratory predictors of the progression towards severe and fatal forms of this illness is needed. Here, we retrospectively evaluated and integrated laboratory parameters of 45 elderly subjects from a long-term care facility with Sars-CoV-2 outbreak and spread, to identify potential common patterns of systemic response able to better stratify patients' clinical course and outcome. METHODS: Baseline white blood cells, granulocytes', lymphocytes', and platelets' counts, hemoglobin, total iron, ferritin, D-dimer, and interleukin-6 concentration were used to generate a principal component analysis. Statistical analysis was performed by using R statistical package version 4.0. RESULTS: We identified 3 laboratory patterns of response, renamed as low-risk, intermediate-risk, and high-risk, strongly associated with patients' survival (p < 0.01). D-dimer, iron status, lymphocyte/monocyte count represented the main markers discriminating high- and low-risk groups. Patients belonging to the high-risk group presented a significantly longer time to ferritin decrease (p: 0.047). Iron-to-ferritin-ratio (IFR) significantly segregated recovered and dead patients in the intermediate-risk group (p: 0.012). CONCLUSIONS: Our data suggest that a combination of few laboratory parameters, i.e. iron status, D-dimer and lymphocyte/monocyte count at admission and during the hospital stay, can predict clinical progression in COVID-19.