Epidemic Outbreaks Research Articles

Recent situations of flight operation and noise exposure observed by the pilot noise monitors at Noi Bai International Airport in Vietnam

This paper introduces the result of data analysis on recent situations of aircraft operation and noise exposure at Noi Bai International Airport (NIA), using observations by the pilot noise monitoring equipment installed in 2019 under a cooperation project on airport environment preservation and aircraft noise monitoring in Vietnam. The results of the noise monitoring over the period from 2019 to 2023 reflect the significant changes in aircraft operation at NIA due to the pandemic of COVID-19. Noise exposure decreased after the epidemic outbreak, with a significant drop at the time when the government ordered travel restrictions, and gradually recovered under a new normal context. The result of data analysis suggests the degree of change in the sound environment in the vicinity of NIA under the different scenarios of aircraft operation. It also provides us with the information necessary to create noise prediction maps. The level of noise exposure observed at each monitoring station was largely in line with the calculated noise levels. Besides, analysis of aircraft operation and noise exposure situations by period revealed that the level of noise exposure varies with the time of day. Additionally, the analysis raises concerns about the impact of nighttime noise, which may disrupt the sleep of residents living near the NIA.

Demographic Profile of Patients with Dengue Fever Presented in a Tertiary Care Hospital of Bangladesh

Background: Dengue is an important tropical infection caused by an arbovirus having four serotypes (DENV-1, DENV-2, DENV-3, DENV-4) transmitted through the bite of infected female Aedes mosquito. Dengue is a challenging disease with multisystemic, varied, atypical, and sometimes life-threatening presentations. It presents as varied clinical spectrum of dengue fever (DF), dengue hemorrhagic fever (DHF), dengue shock syndrome (DSS) and expanded dengue syndrome (EDS) with atypical presentations, thus posing a diagnostic dilemma. Each year, thousands of dengue infections are reported and there are several outbreaks of dengue in several countries including Bangladesh and this imply the global importance of this infection. The objectives of this study was to determine the sociodemographic variables of patient suffering from DF during an epidemic outbreak. Methods: This hospital review analyzed the hospital records of DF cases of BIRDEM General Hospital, Dhaka from June 2019 to December 2019. Patients with suspected dengue fever attending at the outdoor/emergency or admitted indoor were taken as study subjects. Data were collected from hospital records. Study protocol was approved by the Institutional Review Board. All the relevant data regarding history and examination findings of the patients, the laboratory reports were collected. Statistical package for social science (SPSS) version 22, was used for data entry and statistical analysis. Results: Total 292 dengue patients were enrolled in this study. Mean age of all patient was 35.63±15.22 years (11-85 year) with male predominance (54.8%). Maximum patients were from urban residence (83.9%), non-smoker (73.6%), had active lifestyle (63.4%) and normal body mass index (68.5%). Overall, 65.1% patients had no comorbidity and 89.4% had no concurrent acute illness. Conclusion: Although the results of this study cannot be generalized to other cities of Bangladesh, our findings will allow public health agencies in Bangladesh to concentrate their efforts to battle against dengue. J Bangladesh Coll Phys Surg 2023; 41: 63-68

Comparative Epidemiological Assessment of Monkeypox Infections on a Global and Continental Scale Using Logistic and Gompertz Mathematical Models.

The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.

A novel room-based epidemic model: Quarantine, testing, and vaccination strategies

Epidemic outbreaks pose significant challenges to public health and socio-economic stability, necessitating a comprehensive understanding of disease transmission dynamics and effective control strategies. This article discusses the limitations of traditional compartmental and network-based models and, inspired by the opinion formation models, introduces a room-based model that incorporates social gatherings and intuitive quarantine measures. Through simulations and analysis, we examine the impact of various model parameters, and confinement measures like quarantine and preventive measures like testing, and vaccination on disease spread. Additionally, we explore centrality-based testing and immunization strategies, demonstrating their effectiveness in reducing the spread of diseases compared to a random approach. Finally, we propose a combined strategy, that outperforms the existing strategies. It takes both global and local properties of the network structure into account, highlighting the potential for integrated control measures in epidemic management. This research not only contributes to a deeper understanding of epidemic models, but also provides insights into devising successful intervention strategies, including quarantine measures, testing methodologies, and vaccine programs to combat emerging epidemics and pandemics.

Data science training needs in sub-Saharan Africa: Implications for biomedical research and therapeutics capacity

Data use is becoming increasingly valuable worldwide and has positively improved decision-making in various sectors. Data science has revolutionized the study of epidemiology and disease control. Despite the unlimited potential of data science, Africa lags in its innovation and technological advancements. Although sub-Saharan Africa (SSA) has not taken full advantage of data science in healthcare delivery and biomedical research, attempts have been made to harness this opportunity. This article reviews the current level, potentials, and gaps in data science, genomics, and bioinformatics in SSA. We also identified needs associated with developing tools and building capacity in the region, while also acknowledging advances made in these areas to date. We identified opportunities in health data science in SSA, including benefits in combating diseases that burden this region. Applying data science has the potential to create comprehensive healthcare reports and convert them into relevant critical insights that can then be used to provide better care, reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life in general. In order to achieve this, artificial intelligence and machine learning to build and apply models are some of the common mechanisms applied in all domains of medical research, alongside computational tools and models to simulate systems or expedite biomedical research like bioinformatics and computational biology. Data science, a body of knowledge that uses modern tools and techniques for data management and utilization of big data for innovative interventions and biomedical research is upcoming in Sub-Saharan Africa. Its importance in understanding epidemiology of diseases, public health, and surveillance as well as other domains in allied medical disciplines were highlighted in this review. The dearth in data science skills, expertise, and institutions engaged in training data science needs urgent intervention to optimize the potential uses to improve medicine and healthcare delivery in SSA.

Development of a composite scoring system to rank communities at high risk of zero-dose children in Cameroon: A geospatial analysis.

Despite growing efforts to improve access to vaccination, millions of children, especially in developing countries, have not received a single dose of diphtheria, tetanus, and pertussis (DTP) vaccine. Consequently, they are often called zero-dose children (ZDC). With limited health resources, prioritising communities for rapid and mass zero-dose catch-up vaccination in missed communities to avert epidemic outbreaks is complicated by unreliable denominators used to compute vaccination coverages. Incorporating other indicators of access and utilisation of vaccination services can help with identifying and ranking missed communities based on the likelihood of finding ZDC. We described the process of generating a scoring method to rank health areas in Cameroon based on their likelihood of containing ZDC. We used geospatial analysis to compute and aggregate health area characteristics, including hard-to-reach (HTR) areas (defined as areas of settlement above a one- (for urban areas) or 15-kilometre radius (for rural areas) beyond a vaccinating health facility), amount of area covered by slums and new area settlement, and percentage of children unvaccinated for DTP-1. We attributed a weight based on the ability to limit accessibility or utilisation of vaccination services to each characteristic and computed the score as a weighted average of health area characteristics. The health area score ranged from 0 to 1, with higher scores representing a higher likelihood of containing ZDC. We stratified the analysis by rural and urban health areas. We observed substantial district and regional variations in health area scores, with hotspots health areas (administrative level 4) observed in the Far North (0.83), North (0.81), Adamawa (0.80), East (0.75), and South West (0.67) regions. The Adamawa region had the highest percentage of health areas with the highest score (78%), followed by the East (50%), West (48%), and North (46%) regions. For most regions (Far North, South, South West, Littoral, West, and North West), DTP-1 contributed the most to the score. However, HTR settlement areas within a health area contributed substantially to the overall score in the East, North, and Adamawa regions. We found substantial variations in health area scores with hotspots in the Far North, North, Adamawa, East, and South West regions. Although DTP-1 could be used as an indicator to identify health areas with ZDC for most communities, HTR settlement area was a valuable indicator in ranking priority health areas in the East, North, and Adamawa regions, further emphasising the need to consider other indicators before prioritisation.

Coupling media coverage and susceptibility for modeling epidemic dynamics: An application to COVID-19

During an epidemic outbreak, social media is highly influential on human behavior and hence affects the spread of the epidemic course. In order to examine the impact of social media on disease transmission, a qualitative SIS-M type model with media-induced susceptibility is established, dealing with the amount of media coverage as an independent variable. The dynamic analysis of the proposed model is conducted theoretically and numerically. It is found that the growth rate of media coverage as well as the dissemination rate among the susceptible population may destabilize the endemic equilibrium under certain conditions, and consequently results in the existence of Hopf bifurcation. Moreover, the proposed model is applied to fit the daily confirmed cases of COVID-19 as well as the related news items for two cities in mainland China, and the estimated variation in daily confirmed cases triggered by the change of concerned parameters is investigated, revealing the effectiveness of our model and the performance of social media on mitigating the disease spread.

Characterizing the role of human behavior in the effectiveness of contact-tracing applications.

Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded. To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence. The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.

Provision of sanitary standards by the gendarmerie railway police of the Russian Empire in the late XIX-early XX centuries.

The article is devoted to the reconstruction of the historical experience of the participation of gendarmerie police officers in ensuring sanitary order on the railways of the Russian Empire. The purpose of this article is to study measures to prevent the spread of dangerous infectious diseases taken by the railway gendarmerie, both in cooperation with medical and sanitary services, and independently. The object of the study is the historical experience of the official activities of the gendarmerie railway police of the Russian Empire. The subject is the role of gendarmes in ensuring sanitary standards on railways. Along with general scientific methods of analysis, synthesis, induction, deduction and concretization, the author used historical-systemic and historical-genetic research methods. The theoretical basis of the study, along with the published materials, were the office documents of the gendarmerie railway police, which are stored in the State Archives of the Russian Federation. It is concluded that the ranks of the gendarmerie railway police performed a significant role in ensuring sanitary standards by bypassing the entrusted sites, assisting doctors in veterinary examinations, inspections of premises for the detention of prisoners of war, preventing outbreaks of epidemics on the railways that would pose a threat to the health and life of the population on a global scale.

First report of Zymoseptoria tritici causing Septoria tritici blotch in wheat in Paraguay.

Wheat (Triticum aestivum) is the third most cultivated field crop in Paraguay; it is grown on over 450,000 hectares with an annual production of 927,776 tons (fao.org/faostat). In 1952, Septoria tritici blotch (STB) was associated with the fungus Septoria tritici solely based on microscopic observation of conidia (Viedma and Delgado 1987). However, no morphometric or molecular studies have been performed in Paraguay up to date. Over the following decades, STB epidemic outbreaks were recorded, with a reduction in wheat production of up to 70% (Viedma and Delgado 1987). During winter 2021, leaf blotch symptoms were observed with an incidence above 50% in wheat fields in Capitán Miranda, Itapúa, Paraguay. Scattered, spherical, buried, and light brown necrotic spots with dark edges were observed on the leaves. Pycnidia with prominent central ostiole were observed. Leaves with symptoms were washed with 1% sodium hypochlorite for 1 min, rinsed with sterile distilled water, and incubated in wet chambers to induce sporulation of the fungus. Pycnidia produced greyish to white cirri. Isolated conidia were thin, elongated, and hyaline, ranging from 26.9-72.7 × 1.5-2.9 μm with one to three septa. Monosporic colonies on potato dextrose agar (PDA, ; Difco laboratories, Detroit, MI) media varied in color from white to pink, dark gray to black, or black with stroma-like structures. Based on morphology, the fungus was characterized as Zymoseptoria tritici (Hoorne et al. 2002; Gilchrist-Saavedra et al. 2005). Fungal DNA was extracted from mycelia, and the internal transcribed spacer (ITS), translation elongation factor 1-α (TEF1-α), 28S rRNA gene (LSU), actin gene (act), calmodulin (CaM) were amplified using ITS1/ITS4, EF1-728F/EF-2, LSU1Fd/ LR5, ACT-512F/ACT-783R, CAL-228F/CAL737R primers, respectively. PCR amplicons were sequenced at Macrogen (Seoul, Republic of Korea) and deposited in the NCBI GenBank database (ITS: OQ360718; TEF1-α: OQ999044, LSU: OQ996413, act: OQ999046, CaM: OQ999045). Sequences were aligned with several isolates of Septoria spp. previously reported (Verkley et al. 2013; Stukenbrock et al. 2012) using ClustalW. The alignments were concatenated with Bioedit (Hall 1999). The UPGMA method with 1,000 bootstrap replications, was used to construct the phylogenetic tree using MEGA11 with Readeriella mirabilis as the outgroup. The isolate from Paraguay grouped into the Zymoseptoria tritici clade with 96% bootstrap support. To confirm pathogenicity, ten wheat plants cv. Itapúa 80 were grown in pots for three weeks in growth chambers (22 ± 2°C; 16 h photoperiod). Subsequently, these plants were inoculated with 1×107 conidia ml-1 suspension, and ten non-inoculated plants served as control. Seven days after inoculation (DAI), symptoms were observed displaying oval necrotic lesions and approximately 14 DAI abundant pycnidia were observed on and around the lesions. Segments of symptomatic leaves were placed in moisture chambers overnight to enhance cirri development. Conidia were mounted on a slide and observed under the compound microscope. Individual cirrhus were transferred to plates containing PDA and produced colonies like those used in the inoculation (Hoorne et al. 2002). We confirmed that the causal agent of STB from wheat fields in Paraguay was Zymoseptoria tritici. This pathogen causes annual wheat disease epidemics in Paraguay; therefore, optimizing surveillance for early detection and understanding its distribution will improve integrated management.

SARS-CoV-2 transmission via maritime cold chains: A statistical analysis of nucleic acid detection results of cold chain food imported from Fuzhou ports

Numerous epidemic outbreaks related to cold chains have occurred since the coronavirus disease 2019 (COVID-19) outbreak, suggesting the potential danger of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission through cold chain foods (CCFs). By analyzing SARS-CoV-2 RNA contamination of CCFs imported from Fuzhou ports, this study evaluated the contamination and transmission of SARS-CoV-2 RNA via maritime cold chains, with the aim of provide suggestions for CCFs supervision and public health management. The statistical analysis included 131,385 samples. The majority of the CCFs imported into Fuzhou ports was aquatic raw food that originated in Southeast Asia (57.08 %), South America (19.87 %), and South Asia (11.22 %). South Asia had the highest positivity rate of 0.37 %, followed by Southeast Asia (0.21 %) and South America (0.08 %). The positivity rate showed that the outer packaging of CCFs was the most easily contaminated, accounting for 81.33 % of all positive samples. This suggested that CCFs storage and loading processes were the weak links vulnerable to SARS-CoV-2 contamination. The positivity rates in outer packaging, inner packaging, and content of raw food were 0.48 %, 0.08 %, and 0.05 %, respectively, which were obviously higher than those of processed and refined food. This indicated that increasing the mechanization of factories and implementing sensible worker management practices may decrease viral contamination. The monthly positivity rates varied widely from 0 % (March 2021) to 0.40 % (January 2021), with an average of 0.19 %. The positivity rates in outer packaging, inner packaging and content of crustaceans from Southeast Asia were 2.47 %, 0.41 %, and 0.69 %, which were approximately 5–14 times higher than those of fish and cephalopods. Meanwhile, the monthly detection number show that SARS-CoV-2 epidemic prevention strategies affected the trade of imported CCFs.

Critically spanning epidemic outbreak cluster in random geometric networks

Critically spanning epidemic outbreak cluster in random geometric networks

Yemen’deki Özel Ortaokullarda E-Öğrenme Sistemlerinin Uygulanmasında Karşılaşılan Zorlukların Belirlenmesi

Education is one of the most important components of societies and one of the most important reasons for the Renaissance and development of nations. Countries, especially developed countries, strive to improve and develop their educational environments and introduce technology and modern tools into their educational systems. In contrast, the emergence of the COVID-19 pandemic has had a major impact on the education system around the world. Therefore, many countries have resorted to distance learning to mitigate the impact of the epidemic outbreak and avoid the suspension of the educational process. In developing and less developed countries such as Yemen, the e-learning process faces many obstacles. Yemen was among the countries where formal education was suspended due to the pandemic and there was no effective alternative to full continuity of education. Schools in Yemen face some challenges in terms of implementing electronic learning (e-learning), but private schools have some elements for implementing e-learning. 
 This study aims to identify the challenges facing the implementation of e-learning in Yemeni private schools. A qualitative research method was used to achieve the objective of this study. The working group of the study was 28 volunteer participants (9 managers and 19 teachers) in 13 private secondary schools in Mukalla city in Yemen. The semi-structured interview form which consists of 33 open-ended questions was used as a data collection tool within the scope of the research. The data obtained after the interviews were analyzed using descriptive and content analysis techniques. It was noted that the private secondary schools in Yemen face individual, content, curriculum, and technical challenges in front of implementing learning systems. As a result of the study, the problems encountered and proposed solutions were identified, as well as suggestions for researchers for future studies.

The Medicine Wheel: informing the management of tuberculosis outbreaks in Indigenous communities

ABSTRACT Many Indigenous communities in Canada experience endemic tuberculosis with superimposed periodic epidemic outbreaks. Failures in outbreak management have resulted in the “seeding” of future infection and disease. In this paper we present a model that may be used in planning, implementation and review of tuberculosis outbreak management in Cree Indigenous communities in Canada, based on the Medicine Wheel, a paradigm for holistic living. In the context of tuberculosis management, the Medicine Wheel provides a path for the establishment of respectful cross-cultural relationships, the expression of values through action, true community engagement and partnership, and the establishment of culture-based processes of transparency, accountability and change.

Linking within- and between-host scales for understanding the evolutionary dynamics of quantitative antimicrobial resistance

Understanding both the epidemiological and evolutionary dynamics of antimicrobial resistance is a major public health concern. In this paper, we propose a nested model, explicitly linking the within- and between-host scales, in which the level of resistance of the bacterial population is viewed as a continuous quantitative trait. The within-host dynamics is based on integro-differential equations structured by the resistance level, while the between-host scale is additionally structured by the time since infection. This model simultaneously captures the dynamics of the bacteria population, the evolutionary transient dynamics which lead to the emergence of resistance, and the epidemic dynamics of the host population. Moreover, we precisely analyze the model proposed by particularly performing the uniform persistence and global asymptotic results. Finally, we discuss the impact of the treatment rate of the host population in controlling both the epidemic outbreak and the average level of resistance, either if the within-host scale therapy is a success or failure. We also explore how transitions between infected populations (treated and untreated) can impact the average level of resistance, particularly in a scenario where the treatment is successful at the within-host scale.

Testing lockdown measures in epidemic outbreaks through mean-field models considering the social structure

Lately, concepts such as lockdown, quarantine, and social distancing have become very relevant since they have been associated with essential measures in the prevention and mitigation of COVID-19. While some conclusions about the effectiveness of these measures could be drawn from field observations, many mathematical models aimed to provide some clues. However, the reliability of these models is questioned, especially if the social structure is not included in them. In this work, we propose a mesoscopic model that allows the evaluation of the effect of measures such as social distancing and lockdown when the social topology is taken into account. The model is able to predict successive waves of infections without the need to account for reinfections, and it can qualitatively reproduce the wave patterns observed across many countries during the COVID-19 pandemic. Subsequent waves can have a higher peak of infections if the restrictiveness of the lockdown is above a certain threshold. The model is flexible and can implement various social distancing strategies by adjusting the restrictiveness and the duration of lockdown measures or specifying whether they occur once or repeatedly. It also includes the option to consider essential workers that do not isolate during a lockdown.

Trend analysis of the current epidemic situation and analysis of factors underlying local uneven spread of covid cases

This research has been carried out by analyzing the specific processes of the local epidemic dynamics of COVID by comparing the qualitative differences in fluctuations in 2020 and 2023. Methods of nonlinear dynamics of the development of epidemic processes in a rapidly changing situation were used to identify and qualify trends and unique situations that sometimes changed extremely fast. A distinctive feature of the modern pandemic is a rather sharp change in local trends: the effect of the fading of the primary outbreak of the disease and the sudden sharp onset of a new epidemic wave after a long trend of decreasing daily infections. Minimization of the exposure to viral infections did not prevent spreading the virus but created the illusion of success. The existing experience in generating forecasts of epidemic outbreaks based on models of past epidemic processes could not help when faced with a new evolving virus. The previously obtained understanding of the development and completion of epidemic processes of influenza virus strains more likely hindered the prediction of the scenario for the completion of the spread of a new infection, which is also associated with the eventual nature of the process and a variety of dynamic situations. A victory over COVID in the phase of the minimum after the wave, which was announced by many countries, turned out to be premature. New Zealand and Japan, which opted for a strict lockdown strategy in 2020, had a surge of COVID cases in early 2023 because new strains came into circulation. Outbreaks of respiratory diseases known as the Spanish flu and swine flu pandemics, data from which were used by many countries to make predictions, had run their course naturally in two or three waves of illness. At the beginning of 2023, against the backdrop of a global positive trend, some countries have reported a record rise in both mortality and daily morbidity due to the emergence of locally circulating “alarm” strains. A current stage on isolation of stable regional strains substantiates the classification of a series of differentiated properties of the dynamics of regional epidemic situations. Among the observed epidemic effects, extreme phenomena in the form of instantaneous bifurcation destruction of established regimes such as a sharp transition from long-term damped oscillations to a new exponential outbreak in some infections are separately highlighted. The selected options for the development of epidemic transient oscillatory processes are separately introduced in equation forms with delay for local epidemic trends. Equations are proposed to describe three variants of development of the observed stages of local epidemics. The task of constructing a generalized predictive model of a pandemic to describe interrelated regional processes at this stage seems insoluble.

Rombo encephalitis and cerebellar abscess due to Listeria monocytogenes with fulminant course in an immunocompetent elderly patient: A case report

Listeria Monocytogenes (LM) is an opportunistic bacterium that can cause from mild infections to severe clinical pictures with neurological involvement, being responsible for sporadic cases, epidemic outbreaks and a high mortality and case fatality rate. Neurolisteriosis may present as meningitis, meningoencephalitis, rhomboencephalitis or brain abscesses. LM rhomboencephalitis is a rare disease for which advanced age and cellular immunosuppression are common risk factors, although it may occur in patients without prior immunosuppression. Spread to the CNS occurs haematogenously through the blood-brain barrier, by direct invasion or by retrograde brain migration through nerve tracts. Different diagnostic subtyping methods are available, although DNA sequencing is the technique of choice for epidemiological studies. The combination of ampicillin with gentamicin for 6-8 weeks is usually the recommended treatment, and neurosurgery may improve the prognosis in patients with brain abscess, although there are no clinical practice guidelines available with a high level of evidence. The diagnosis of LM rhomboencephalitis should be suspected in any patient with an infectious condition with evidence of meningeal or brainstem involvement. Mortality from neurolisteriosis is increasing in Europe due to the ageing population and the increase in immunocompromised patients, and can reach up to 51%. The case described in this article is exceptional, presenting acute rhomboencephalitis with cerebellar abscesses in a healthy patient with no risk factors and a fulminant course.

The US Biological and Chemical Agression Against Cuba in 1963–1996

For almost 60 years, the United States has tried to overthrow the government of Fidel Castro in Cuba, using a variety of means, including chemical and biological agents. At the same time, their use has always been officially denied by Washington. The purpose of this article is to summarize the available sources proving the involvement of the United States, as well as the groups of the Cuban opposition created and supported by the US, in biological and chemical sabotage against both the economy and the people of Cuba. Sources, research methods. The author used a variety of open sources, including the statements by Cuban officials, materials from the Cuban and American press, declassified documents from the US government agencies, as well as public confessions of former CIA agents and Cuban opposition figures. The research method is the cross analysis. The discussion of the results. The declassified US documents show that the organizers of «Operation 40» and «Operation Mongoose» planned to use biological agents and parasitic plants against plantations of crops and livestock. Sugar exported to the USSR was also poisoned. In addition, the head of one of the Cuban terrorist organizations admitted that he had imported biological agents to Cuba, which provoked an epidemic of dengue hemorrhagic fever in 1981. The testimoies of several other former CIA agents also point to the use of African swine fever in 1971 to kill pigs in Cuba. The atypical course of the Dengue fever epidemic in 1981, as well as the absence of a strain (Dengue-2) of this virus in the Western Hemisphere at that time, indicate the planned nature of the sabotage, which resulted in an outbreak on the island. Conclusions. During the Cold War, the United States actively used chemical and biological agents against agriculture and the people of Cuba. The facts considered allow us to draw conclusions about the nature of the methods that the CIA used to organize and conduct biosabotage: such operations were planned «in the long run» and had large-scale goals – for example, to eliminate sugar production on the island or cause famine; highly contagious pathogens of human and animal diseases were used for biological sabotage; artificially caused outbreaks of epidemics and epizootics were disguised as natural ones; representatives of the local opposition, as well as mercenaries, were involved in the implementation of sabotage; to infect farm animals, vaccines contaminated with virulent strains directly at biofactories, as well as contaminated feed were used.

The 8-bromobaicalein alleviated chikungunya-induced musculoskeletal inflammation and reduced the viral load in healthy adult mice

ABSTRACT Chikungunya virus is a re-emerging arbovirus that has caused epidemic outbreaks in recent decades. Patients in older age groups with high viral load and severe immunologic response during acute infection are likely to develop chronic arthritis and severe joint pain. Currently, no antiviral drug is available. Previous studies suggested that a flavone derivative, 8-bromobaicalein, was a potential dengue and Zika replication inhibitor in a cell-based system targeting flaviviral polymerase. Here we characterized that 8-bromobaicalein inhibited chikungunya virus replication with EC50 of 0.49 ± 0.11 µM in Vero cells. The molecular target predicted at viral nsP1 methyltransferase using molecular binding and fragment molecular orbital calculation. Additionally, oral administration of 250 mg/kg twice daily treatment alleviated chikungunya-induced musculoskeletal inflammation and reduced viral load in healthy adult mice. Pharmacokinetic analysis indicated that the 250 mg/kg administration maintained the compound level above EC99.9 for 12 h. Therefore, 8-bromobaicalein should be a potential candidate for further development as a pan-arboviral drug.