Mpox: An Overview of Pathogenesis, Diagnosis, and Public Health Implications.

Mpox, caused by viruses of the genus Orthopoxvirus, is an emerging threat to human and animal health. With increasing urbanization and more frequent interaction between humans and wild animals, the risk of Mpox transmission to humans has increased significantly. This review aims to examine in depth the epidemiology, pathogenesis, and diagnosis of Mpox, with a special focus on recent discoveries and advances in understanding the disease. Molecular mechanisms involved in viral replication will be examined, as well as risk factors associated with interspecific transmission and spread of the disease in human populations. Currently available diagnostic methods will also be discussed, with a critical analysis of their limitations and possible future directions for improving the accuracy and timeliness of diagnosis. Finally, this review will explore the public health implications associated with Mpox, emphasizing the importance of epidemiological surveillance, vaccination, and emergency preparedness to prevent and manage possible outbreaks. Understanding the epidemiology and control strategies for Mpox is critical to protecting the health of human and animal communities and mitigating the risk of interspecific transmission and spread of the disease.

What about chronotropic incompetence in heart failure with mildly reduced ejection fraction? Clinical and prognostic implications from the Metabolic Exercise combined with Cardiac and Kidney Indexes score dataset.

AIMS: Chronotropic incompetence (CI) is a strong predictor of outcome in heart failure with reduced ejection fraction, however no data on its clinical and prognostic impacts in heart failure with mildly reduced ejection fraction (HFmrEF) are available. Therefore, the study aims to investigate, in a large multicentre HFmrEF cohort, the prevalence of CI as well as its relationship with exercise capacity and its prognostic role over the cardiopulmonary exercise testing (CPET) parameters. METHODS AND RESULTS: Within the Metabolic Exercise combined with Cardiac and Kidney Indexes (MECKI) database, we analysed data of 864 HFmrEF out of 1164 stable outpatients who performed a maximal CPET at the cycle ergometer and who had no significant rhythm disorders or comorbidities. The primary study endpoint was cardiovascular (CV) death. All-cause death was also explored. Chronotropic incompetence prevalence differed depending on the method (peak heart rate, pHR% vs. pHR reserve, pHRR%) and the cut-off adopted (pHR% from ≤75% to ≤60% and pHRR% ≤ 65% to ≤50%), ranging from 11% to 62%. A total of 84 (9.7%) CV deaths were collected, with 39 (4.5%) occurring within 5 years. At multivariate analysis, both pHR% [hazard ratio 0.97 (0.95-0.99), P < 0.05] and pHRR% [hazard ratio 0.977 (0.961-0.993), P < 0.01] were associated with the primary endpoint. A pHR% ≤ 75% and a pHRR% ≤ 50% represented the most accurate cut-off values in predicting the outcome. CONCLUSION: The study suggests an association between blunted exercise-HR response, functional capacity, and CV death risk among patients with HFmrEF. Whether the CI presence might be adopted in daily HFmrEF management needs to be addressed in larger prospective studies.

Chronotropic incompetence is an easy-to-obtain additive parameter for cardiovascular death risk stratification in heart failure with mildly reduced ejection fraction (HFmrEF). Peak heart rate and peak heart rate reserve are associated with exercise capacity in HFmrEF. Peak heart rate and peak heart rate reserve are associated with cardiovascular death in HFmrEF.

Monitoring the West Nile virus outbreaks in Italy using open access data.

This paper introduces a comprehensive dataset on West Nile virus outbreaks that have occurred in Italy from September 2012 to November 2022. We have digitized bulletins published by the Italian National Institute of Health to demonstrate the potential utilization of this data for the research community. Our aim is to establish a centralized open access repository that facilitates analysis and monitoring of the disease. We have collected and curated data on the type of infected host, along with additional information whenever available, including the type of infection, age, and geographic details at different levels of spatial aggregation. By combining our data with other sources of information such as weather data, it becomes possible to assess potential relationships between West Nile virus outbreaks and environmental factors. We strongly believe in supporting public oversight of government epidemic management, and we emphasize that open data play a crucial role in generating reliable results by enabling greater transparency.

Update on the Phylodynamic and Genetic Variability of Marburg Virus.

The COVID-19 pandemic has not only strained healthcare systems in Africa but has also intensified the impact of emerging and re-emerging diseases. Specifically in Equatorial Guinea, mirroring the situation in other African countries, unique zoonotic outbreaks have occurred during this challenging period. One notable resurgence is Marburg virus disease (MVD), which has further burdened the already fragile healthcare system. The re-emergence of the Marburg virus amid the COVID-19 pandemic is believed to stem from a probable zoonotic spill-over, although the precise transmission routes remain uncertain. Given the gravity of the situation, addressing the existing challenges is paramount. Though the genome sequences from the current outbreak were not available for this study, we analyzed all the available whole genome sequences of this re-emerging pathogen to advocate for a shift towards active surveillance. This is essential to ensure the successful containment of any potential Marburg virus outbreak in Equatorial Guinea and the wider African context. This study, which presents an update on the phylodynamics and the genetic variability of MARV, further confirmed the existence of at least two distinct patterns of viral spread. One pattern demonstrates a slower but continuous and recurring virus circulation, while the other exhibits a faster yet limited and episodic spread. These results highlight the critical need to strengthen genomic surveillance in the region to effectively curb the pathogen's dissemination. Moreover, the study emphasizes the importance of prompt alert management, comprehensive case investigation and analysis, contact tracing, and active case searching. These steps are vital to support the healthcare system's response to this emerging health crisis. By implementing these strategies, we can better arm ourselves against the challenges posed by the resurgence of the Marburg virus and other infectious diseases.

Influence of exertional oscillatory breathing and its temporal behavior in patients with heart failure and reduced ejection fraction.

BACKGROUND: Exertional oscillatory breathing (EOV) represents an emerging prognostic marker in heart failure (HF) patients, however little is known about EOV meaning with respect to its disappearance/persistence during cardiopulmonary exercise test (CPET). The present single-center study evaluated EOV clinical and prognostic impact in a large cohort of reduced ejection fraction HF patients (HFrEF) and, contextually, if a specific EOV temporal behavior might be an addictive risk predictor. METHODS AND RESULTS: Data from 1.866 HFrEF patients on optimized medical therapy were analysed. The primary cardiovascular (CV) study end-point was cardiovascular death, heart transplantation or LV assistance device (LVAD) implantation at 5-years. For completeness a secondary end-point of total mortality at 5- years was also explored. EOV presence was identified in 251 patients (13%): 142 characterized by EOV early cessation (Group A) and 109 by EOV persistence during the whole CPET (Group B). The entire EOV Group showed worse clinical and functional status than NoEOV Group (n = 1.615) and, within the EOV Group, Group B was characterized by a more severe HF. At CV survival analysis, EOV patients showed a poorer outcome than the NoEOV Group (events 27.1% versus 13.1%, p < 0.001) both unpolished and after matching for main confounders. Instead, no significant differences were found between EOV Group A and B with respect to CV outcome. Conversely the analysis for total mortality failed to be significant. CONCLUSIONS: Our analysis, albeit retrospective, supports the inclusion of EOV into a CPET-centered clinical and prognostic evaluation of the HFrEF patients. EOV characterizes per se a more advanced HFrEF stage with an unfavorable CV outcome. However, the EOV persistence, albeit suggestive of a more severe HF, does not emerge as a further prognostic marker.

Molecular In-Depth on the Epidemiological Expansion of SARS-CoV-2 XBB.1.5.

Since the beginning of the pandemic, the generation of new variants periodically recurs. The XBB.1.5 SARS-CoV-2 variant is one of the most recent. This research was aimed at verifying the potential hazard of this new subvariant. To achieve this objective, we performed a genome-based integrative approach, integrating results from genetic variability/phylodynamics with structural and immunoinformatic analyses to obtain as comprehensive a viewpoint as possible. The Bayesian Skyline Plot (BSP) shows that the viral population size reached the plateau phase on 24 November 2022, and the number of lineages peaked at the same time. The evolutionary rate is relatively low, amounting to 6.9 × 10-4 subs/sites/years. The NTD domain is identical for XBB.1 and XBB.1.5 whereas their RBDs only differ for the mutations at position 486, where the Phe (in the original Wuhan) is replaced by a Ser in XBB and XBB.1, and by a Pro in XBB.1.5. The variant XBB.1.5 seems to spread more slowly than sub-variants that have caused concerns in 2022. The multidisciplinary molecular in-depth analyses on XBB.1.5 performed here does not provide evidence for a particularly high risk of viral expansion. Results indicate that XBB.1.5 does not possess features to become a new, global, public health threat. As of now, in its current molecular make-up, XBB.1.5 does not represent the most dangerous variant.

Genome-based survey of the SARS-CoV-2 BF.7 variant from Asia.

The SARS-CoV-2 BF.7 variant represents one of the most recent subvariant under monitoring. At the beginning of the 2023 it caused several concerns especially in Asia because of a resurge in COVID-19 cases. Here we perform a genome-based integrative approach on SARS-CoV-2 BF.7 to shed light on this emerging lineage and produce some consideration on its real dangerousness. Both genetic and structural data suggest that this new variant currently does not show evidence of an high expansion capability. It is very common in Asia, but it appears less virulent than other Omicron variants as proved by its relatively low evolutionary rate (5.62 × 10-4 subs/sites/years). The last plateau has been reached around December 14, 2022 and then the genetic variability, and thus the viral population size, no longer increased. As already seen for several previous variants, the features that may be theoretically related to advantages are due to genetic drift that allows to the virus a constant adaptability to the host, but is not strictly connected to a fitness advantage. These results have further pointed that the genome-based monitoring must continue uninterruptedly to be prepared and well documented on the real situation.

The challenges of open data for future epidemic preparedness: The experience of the 2022 Ebolavirus outbreak in Uganda.

On 20 September 2022, the Ministry of Health in Uganda, together with the World Health Organization-Regional Office for Africa (WHO AFRO) confirmed an outbreak of EVD due to Sudan ebolavirus in Mubende District, after one fatal case was confirmed. Real-time information are needed to provide crucial information to understand transmissibility, risk of geographical spread, routes of transmission, risk factors of infection, and provide the basis for epidemiological modelling that can inform response and containment planning to reduce the burden of disease. We made an effort to build a centralized repository of the Ebola virus cases from verified sources, providing information on dates of symptom onset, locations (aggregated to the district level), and when available, the gender and status of hospitals, reporting bed capacity and isolation unit occupancy rate according to the severity status of the patient. The proposed data repository provides researchers and policymakers timely, complete, and easy-accessible data to monitor the most recent trends of the Ebola outbreak in Ugandan districts with informative graphical outputs. This favors a rapid global response to the disease, enabling governments to prioritize and adjust their decisions quickly and effectively in response to the rapidly evolving emergency, with a solid data basis.

Postexposure-vaccine-prophylaxis against COVID-19.

During the COVID-19 pandemic, postexposure-vaccine-prophylaxis is not a practice. Following exposure, only patient isolation is imposed. Moreover, no therapeutic prevention approach is applied. We asked whether evidence exists for reduced mortality rate following postexposure-vaccine-prophylaxis. To estimate the effectiveness of postexposure-vaccine-prophylaxis, we obtained data from the Israeli Ministry of Health registry. The study population consisted of Israeli residents aged 12 years and older, identified for the first time as PCR-positive for SARS-CoV-2, between December 20th, 2020 (the beginning of the vaccination campaign) and October 7th, 2021. We compared "recently injected" patients-that proved PCR-positive on the same day or on 1 of the 5 consecutive days after first vaccination (representing an unintended postexposure-vaccine-prophylaxis)s-to unvaccinated control group. Among Israeli residents identified PCR-positive for SARS-CoV-2, 11 687 were found positive on the day they received their first vaccine injection (BNT162b2) or on 1 of the 5 days thereafter. In patients over 65 years, 143 deaths occurred among 1412 recently injected (10.13%) compared to 255 deaths among the 1412 unvaccinated (18.06%), odd ratio (OR) 0.51 (95% confidence interval [CI]: 0.41-0.64; p < 0.001). A significant reduction in the death toll was observed among the 55-64 age group, with 8 deaths occurring among the 1320 recently injected (0.61%) compared to 24 deaths among the 1320 unvaccinated control (1.82%), OR 0.33 (95% CI: 0.13-0.76; p = 0.007). Postexposure-vaccine-prophylaxis is effective against death in COVID-19 infection.

Estimating the undetected infections in the Monkeypox outbreak.

While the number of detected Monkeypox infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of its spread. The aim of this study is to estimate the true number of Monkeypox (detected and undetected) infections in most affected countries. The question being asked is: How many cases have actually occurred? We propose a lower bound estimator for the true number of Monkeypox cases. The estimator is data-driven and can be easily computed from the cumulative distributions of weekly cases. We focused on the ratio of the total estimated cases to the observed cases on July 31, 2022: The proportion of undetected cases was relevant in all countries, with countries whose estimated true number of infections could be more than three times the observed one. We provided a practical contribution to the understanding of the current Monkeypox wave and reliable estimates on how many undetected cases are going around in several countries, where the epidemic spreads differently.

Short-term forecasts of Monkeypox cases in multiple countries: keep calm and don't panic.

We propose a parametric regression model for incidence indicators based on the use of the Richards' curve (a generalized logistic function) to analyse the current Monkeypox epidemic data for the most 10 affected countries worldwide. At present, results show that the outbreak is under control in most countries.

Genetic and Structural Data on the SARS-CoV-2 Omicron BQ.1 Variant Reveal Its Low Potential for Epidemiological Expansion.

The BQ.1 SARS-CoV-2 variant, also known as Cerberus, is one of the most recent Omicron descendant lineages. Compared to its direct progenitor BA.5, BQ.1 has some additional spike mutations in some key antigenic sites, which confer further immune escape ability over other circulating lineages. In such a context, here, we perform a genome-based survey aimed at obtaining a complete-as-possible nuance of this rapidly evolving Omicron subvariant. Genetic data suggest that BQ.1 represents an evolutionary blind background, lacking the rapid diversification that is typical of a dangerous lineage. Indeed, the evolutionary rate of BQ.1 is very similar to that of BA.5 (7.6 × 10-4 and 7 × 10-4 subs/site/year, respectively), which has been circulating for several months. The Bayesian Skyline Plot reconstruction indicates a low level of genetic variability, suggesting that the peak was reached around 3 September 2022. Concerning the affinity for ACE2, structure analyses (also performed by comparing the properties of BQ.1 and BA.5 RBD) indicate that the impact of the BQ.1 mutations may be modest. Likewise, immunoinformatic analyses showed moderate differences between the BQ.1 and BA5 potential B-cell epitopes. In conclusion, genetic and structural analyses on SARS-CoV-2 BQ.1 suggest no evidence of a particularly dangerous or high expansion capability. Genome-based monitoring must continue uninterrupted for a better understanding of its descendants and all other lineages.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021.

INTRODUCTION: Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. METHODS: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. RESULTS: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. DISCUSSION: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

SARS-CoV-2 Variants in COVID-19 Disease: A Focus on Disease Severity and Vaccine Immunity in Patients Admitted to the Emergency Department.

Tracking SARS-CoV-2 variants along with vaccinations are fundamental for severe COVID-19 disease prevention. A study was performed that focused on 43 patients with the SARS-CoV-2 infection who were admitted to the Emergency Department. RT-PCR-positive nasopharyngeal samples were sequenced using the MiSeq II system for variant detection. The main reason for Emergency Department admission was COVID-19 (67%), followed by other causes (33%); 51% patients were unvaccinated or vaccinated with a single dose and 49% had completed the vaccination course with two or three doses. Among the vaccinated group, 38% were admitted for COVID-19, versus 94.5% of the unvaccinated group. After admission, 50% of the vaccinated group and 36% of the unvaccinated group were discharged and allowed to go home, and 80% of the unvaccinated had no major comorbidities; 63% needed hospital admission and 5% required a stay in the Intensive Care Unit. Of these, 37% were vaccinated with 3 doses, 11% with two doses, 4% with a single dose, and 48% were unvaccinated. The 70% of the vaccinated patients who were admitted to hospital presented major comorbidities versus 38% of the unvaccinated group. Two unvaccinated patients that needed intensive care had relevant comorbidities and died. Genome sequencing showed the circulation of three omicron and two pure sub-lineages of omicron, including 22 BA.1, 12 BA.1.1, and 7 BA.2. Data showed the SARS-CoV-2 national and international migration patterns and how vaccination was useful for severe COVID-19 disease prevention.

Genetic Variability of the Monkeypox Virus Clade IIb B.1.

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.

Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.