Emerging infectious diseases (EIDs) are newly emerging and reemerging infectious diseases. The National Institute of Allergy and Infectious Diseases identifies the following as emerging infectious diseases: SARS, MERS, COVID-19, influenza, fungal diseases, plague, schistosomiasis, smallpox, tick-borne diseases, and West Nile fever. The factors that should be taken into consideration are the genetic adaptation of microbial agents and the characteristics of the human host or environment. The new approach to identifying new possible pathogens will have to go through the One Health approach and omics integration data, which are capable of identifying high-priority microorganisms in a short period of time. New bioinformatics technologies enable global integration and sharing of surveillance data for rapid public health decision-making to detect and prevent epidemics and pandemics, ensuring timely response and effective prevention measures. Machine learning tools are being more frequently utilized in the realm of infectious diseases to predict sepsis in patients, diagnose infectious diseases early, and forecast the effectiveness of treatment or the appropriate choice of antibiotic regimen based on clinical data. We will discuss emerging microorganisms, omics techniques applied to infectious diseases, new computational solutions to evaluate biomarkers, and innovative tools that are useful for integrating omics data and electronic medical records data for the clinical management of emerging infectious diseases.

Monkeypox is caused by a sylvatic, double-stranded DNA zoonotic virus. Since 1 January 2022, monkeypox cases have been reported to WHO from 106 Member States across six WHO regions, and as of 2 October 2022, a total of 68,900 confirmed cases, including 25 deaths, occurred. Here, by using a whole genome approach, we perform a genetic and phylodynamic survey of the monkeypox virus Clade IIb B.1, which is the lineage causing the current multi-country outbreak. Results suggest that outbreaks seem to be isolated and localized in several epidemic clusters with geographic consistency. Currently, monkeypox appears to be a virus with a flattened genetic variability in terms of evolutionary path, with a very slow rate of growth in the population size. This scenario confirms that the monkeypox virus lacks the evolutionary advantage, given by the high level of mutation rate, which is very strong in RNA viruses. Of course, constant genome-based monitoring must be performed over time in order to detect the change in its genetic composition, if any.

Adrenomedullin (ADM) is a peptide hormone produced primarily in the adrenal glands, playing a crucial role in various physiological processes. As well as improving vascular integrity and decreasing vascular permeability, ADM acts as a vasodilator, positive inotrope, diuretic, natriuretic and bronchodilator, antagonizing angiotensin II by inhibiting aldosterone secretion. ADM also has antihypertrophic, anti-apoptotic, antifibrotic, antioxidant, angiogenic and immunoregulatory effects and antimicrobial properties. ADM expression is upregulated by hypoxia, inflammation-inducing cytokines, viral or bacterial substances, strength of shear stress, and leakage of blood vessels. These pathological conditions are established during systemic inflammation that can result from infections, surgery, trauma/accidents or burns. The ability to rapidly identify infections and the prognostic, predictive power makes it a valuable tool in severe viral and bacterial infections burdened by high incidence and mortality. This review sheds light on the pathophysiological processes that in severe viral or bacterial infections cause endothelitis up to the development of organ damage, the resulting increase in ADM levels dosed through its more stable peptide mid-regional proadrenomedullin (MR-proADM), the most significant studies that attest to its diagnostic and prognostic accuracy in highlighting the severity of viral or bacterial infections and appropriate therapeutic insights.

Background: Acute heart failure (AHF) is a major cause of hospitalization and mortality worldwide. Early and accurate diagnosis, as well as effective risk stratification, are essential for optimizing clinical management and improving patient outcomes. In this context, biomarkers have gained increasing interest in recent years as they can provide important diagnostic and prognostic information in patients with AHF. Aim and Methods: The primary objective of the present study was to compare the levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP), mid-regional pro-adrenomedullin (MR-proADM), and C-reactive protein (CRP) between patients diagnosed with acute heart failure (AHF) and those without AHF and sepsis. Furthermore, the study aimed to assess the diagnostic and prognostic value of the use of a multimarker approach in AHF patients. To achieve these objectives, a total of 145 patients with AHF and 127 patients without AHF and sepsis, serving as the control group, were consecutively enrolled in the study. Results: Levels of MR-proADM (median: 2.07; (25th–75th percentiles: 1.40–3.02) vs. 1.11 (0.83–1.71) nmol/L, p < 0.0001), and NT-proBNP (5319 (1691–11,874) vs. 271 (89–931.5) pg/mL, p < 0.0001) were significantly higher in patients with AHF compared to controls, whereas CRP levels did not show significant differences. The mortality rate in the AHF group during in-hospital stay was 12%, and the rate of new re-admission for AHF within 30 days after discharge was 10%. During in-hospital follow-up, Cox regression analyses showed that levels of NT-proBNP > 10,132 pg/mL (hazard ratio (HR) 2.97; 95% confidence interval (CI): 1.13–7.82; p = 0.0284) and levels of MR-proADM > 2.8 nmol/L (HR: 8.57; CI: 2.42–30.28; p = 0.0009) predicted mortality. The combined use of MR-proADM and NT-proBNP provided significant additive predictive value for mortality and new re-admission for AHF at 30 days after discharge. A logistic regression analysis showed that the presence of NT-proBNP pg/mL > 12,973 pg mL and/or MR-proADM > 4.2 nmol/L predicted hospital re-admission within 30 days (OR: 3.23; CI: 1.05–9.91; p = 0.041). Conclusion: The combined assay of MR-proADM and NT-proBNP could be helpful in accurately identifying AHF and in defining prognosis and re-admission for AHF. The complementary use of these biomarkers can provide a useful clinical evaluation of AHF while also orienting clinicians to the pathophysiology underlying heart damage and assisting them in tailoring therapy.

The high incidence of atrial fibrillation (AFib) following cardiac surgery (postoperative atrial fibrillation, POAF) relies on specific surgical features. However, in the setting of POAF, the role of the microbiome in the modulation of cardiac fibrosis is still not clear. This study aimed to analyze the effect of the microbiome and its main metabolic product (trimethylamine-N-oxide, TMAO) in the fibrosis of myocardial tissue, to investigate its role in POAF. Patients undergoing elective cardiac surgery with cardiopulmonary bypass, central atrio-caval cannulation and no history of AFib, were included. A fragment of the right atrium was analyzed for qualitative and mRNA-quantitative evaluation. A preoperative blood sample was analyzed with enzyme-linked immunosorbent assay (ELISA). A total of 100 patients have been included, with POAF occurring in 38%. Histologically, a higher degree of fibrosis, angiogenesis and inflammation has been observed in POAF. Quantitative evaluation showed increased mRNA expression of collagen-1, collagen-3, fibronectin, and transforming growth factor beta (TGFb) in the POAF group. ELISA analysis showed higher levels of TMAO, lipopolysaccharide and TGFb in POAF, with similar levels of sP-selectin and zonulin. TMAO ≥ 61.8 ng/mL (odds ratio, OR 2.88 [1.35–6.16], p = 0.006), preoperative hemoglobin < 13.1 g/dL (OR 2.37 [1.07–5.24], p = 0.033) and impaired right ventricular function (OR 2.38 [1.17–4.83], p = 0.017) were independent predictors of POAF. Also, TMAO was significantly associated with POAF by means of increased fibrosis. Gut microbiome product TMAO is crucial for myocardial fibrosis, which is a key factor for POAF. Patients in preoperative sinus rhythm who will develop POAF have increased genetic expression of pro-fibrotic genes and enhanced fibrosis in histological staining. Elevated TMAO level (≥61.8 ng/mL) is an independent risk factor for POAF.

Multidrug-resistant Klebsiella pneumoniae is a significant healthcare challenge that particularly affects vulnerable patients through opportunistic nosocomial infections. Surveillance is crucial for monitoring the prevalence of these infections. Eighty-four KPC K. pneumoniae strains (2019–2022) were collected from patients admitted in Fondazione Policlinico Universitario Campus Bio-Medico. Strains were identified by MALDI-TOF and tested for antimicrobial susceptibility, and gene amplification was performed to identify the different blaKPC variants. Phylogenetic reconstructions were carried out using Bayesian methods. Additionally, to create a Bayesian skyline plot (BSP), additional analyses were conducted, running a simulation of 100 million generations under a Bayesian skyline model along with the uncorrelated log-normal relaxed clock model. To identify potential subgroups within genetic clusters and evaluate genetic variability among sequences, principal coordinate analysis (PCoA) was performed. In total, 84 Klebsiella pneumoniae isolates were classified as multidrug-resistant (MDR), characterized by resistance to three or more antibiotic classes, including carbapenems, and testing positive for KPC gene presence, and were included in the study. The Bayesian evolutionary tree for K. pneumoniae showed strongly supported branches but no genetic structure related to sampling dates or hospital departments. Phylogenetic analysis revealing a 73-year evolutionary span of K. pneumoniae strains. PCoA analysis identified three genetic outliers from 2022 and one from 2021, indicating higher genetic distances. The Bayesian skyline plot revealed increased genetic variability peaking at the end of 2019, followed by stabilization from early 2020 onward, with no significant changes in genetic variability thereafter. Overall, the study found no genetic structure correlating with sampling date or hospital department, suggesting significant variability in pathogen introduction during the pandemic. The increase in multidrug-resistant K. pneumoniae was linked to the influx of severe COVID-19 cases, prolonged hospitalizations, and heightened broad-spectrum antibiotic use, which likely facilitated resistance development and transmission amidst altered infection control practices.