Convergence model of AI and IoT for virus disease control system.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.

The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country's overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely-exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country's economy.

Respiratory syncytial virus outbreak DURING THE COVID-19 PANDEMIC. How has it changed?

INTRODUCTION: The epidemiology of respiratory syncytial virus (RSV) infection has changed during the COVID-19 pandemic. Our objectives were to describe the RSV epidemic in 2021 and compare it with the previous years to the pandemic. METHODS: Retrospective study performed in Madrid (Spain) in a large paediatric hospital comparing the epidemiology and clinical data of RSV admissions during 2021 and the two previous seasons. RESULTS: 899 children were admitted for RSV infection during the study period. During 2021, the outbreak peaked in June and the last cases were identified in July. Previous seasons were detected in autumn-winter. The number of admissions in 2021 was significantly lower than in previous seasons. There were no differences between seasons regarding age, sex or disease severity. CONCLUSION: RSV hospitalizations during 2021 in Spain moved to summer with no cases in autumn and winter 2020-2021. Unlike other countries, clinical data were similar between epidemics.

[Influence of the COVID-19 pandemic on the epidemiology of acute Bronchiolitis.] / Influencia de la pandemia COVID-19 sobre la epidemiología de la bronquiolitis aguda.

INTRODUCTION: The COVID-19 pandemic has changed the circulation of some viruses associated with acute bronchiolitis. We analyzed the epidemiology of bronchiolitis admissions during the COVID-19 pandemic compared with 8 previous epidemic seasons. METHODS: An observational and ambispective study was performed, including infants admitted with bronchiolitis in a tertiary hospital during 2 periods: COVID-19 pandemic (15th March 2020 to 3st August 2021) and pre-pandemic (1st September 2012 to 14th March 2020). Demographic, clinical data and etiologies were collected. RESULTS: Five hundred ten patients were hospitalized with bronchiolitis: 486 in the pre-pandemic period with an average of 61 admissions per season vs 24 during the pandemic, observing a 60.7% reduction in bronchiolitis admissions. During the pandemic, bronchiolitis outbreak was delayed until spring-summer 2021. Respiratory syncytial virus was the most frequent etiological agent in both periods. CONCLUSION: We observed a change in the seasonality of bronchiolitis during the pandemic COVID-19, possibly influenced by control measures against SARS-CoV-2.

Immunization technologies: Time to consider new preventative solutions for respiratory syncytial virus infections.

New technologies for the prevention of infectious diseases are emerging to address unmet medical needs, in particular, the use of long-acting monoclonal antibodies (mAb) to prevent Respiratory Syncytial Virus (RSV) lower respiratory tract disease in infants during their first RSV season. The lack of precedent for mAbs for broad population protection creates challenges in the assessment of upcoming prophylactic long-acting mAbs for RSV, with associated consequences in legislative and registration categorization, as well as in recommendation, funding, and implementation pathways. We suggest that the legislative and regulatory categorization of preventative solutions should be decided by the effect of the product in terms of its impact on the population and health-care systems rather than by the technology used or its mechanism of action. Immunization can be passive and active, both having the same objective of prevention of infectious diseases. Long-acting prophylactic mAbs work as passive immunization, as such, their recommendations for use should fall under the remit of National Immunization Technical Advisory Groups or other relevant recommending bodies for inclusion into National Immunization Programs. Current regulations, policy, and legislative frameworks need to evolve to embrace such innovative preventative technologies and acknowledge them as one of key immunization and public health tools.

Mild Hyperglycaemia in Hospitalised Children with Moderate COVID-19 Infection.

Background and Objectives: COVID-19 infection may influence many physiological processes, including glucose metabolism. Acute hyperglycaemia has been related to a worse prognosis in patients with severe COVID-19 infection. The aim of our study was to find out if moderate COVID-19 infection is associated with hyperglycaemia. Materials and Methods: A total of 235 children were enrolled in the study between October 2021 and October 2022, 112 with confirmed COVID-19 infection and 123 with other RNA viral infection. In all patients, types of symptoms, glycaemia at the time of admission, and basic anthropometric and biochemical parameters were recorded. Results: Average glycaemia was significantly higher in COVID-19 patients compared to other viral infections (5.7 ± 1.12 vs. 5.31 ± 1.4 mmol/L, p = 0.011). This difference was more obvious in subgroups with gastrointestinal manifestations (5.6 ± 1.11 vs. 4.81 ± 1.38 mmol/L, p = 0.0006) and with fever (5.76±1.22 vs. 5.11±1.37 mmol/L, p = 0.002), while no significant difference was found in subgroups with mainly respiratory symptoms. The risk of hyperglycaemia (>5.6 mmol/L) was higher in COVID-19 patients compared to other viral infections (OR = 1.86, 95%CI = 1.10-3.14, p = 0.02). The risk of hyperglycaemia was significantly higher in COVID-19 compared to other viral infections in the subgroups of patients with fever (OR = 3.59, 95% CI 1.755-7.345, p = 0.0005) and with gastrointestinal manifestations (OR = 2.48, 95% CI 1.058-5.791, p = 0.036). Conclusion: According to our results, mild hyperglycaemia was significantly more common in children with moderate COVID-19 infection compared to other RNA virus respiratory and gastrointestinal infections, especially when accompanied by fever or gastrointestinal symptoms.

Scaling and Sustaining Facilitated Telemedicine to Expand Treatment Access Among Underserved Populations: A Qualitative Study.

Background: Opioid treatment programs are an essential component of the management of opioid use disorder (OUD). They have also been proposed as "medical homes" to expand health care access for underserved populations. We utilized telemedicine as a method to increase access for hepatitis C virus (HCV) care among people with OUD. Methods: We interviewed 30 staff and 15 administrators regarding the integration of facilitated telemedicine for HCV into opioid treatment programs. Participants provided feedback and insight for sustaining and scaling facilitated telemedicine for people with OUD. We utilized hermeneutic phenomenology to develop themes related to telemedicine sustainability in opioid treatment programs. Results: Three themes emerged on sustaining the facilitated telemedicine model: (1) Telemedicine as a Technical Innovation in Opioid Treatment Programs, (2) Technology Transcending Space and Time, and (3) COVID-19 Disrupting the Status Quo. Participants identified skilled staff, ongoing training, technology infrastructure and support, and an effective marketing campaign as key to maintaining the facilitated telemedicine model. Participants highlighted the study-supported case manager's role in managing the technology to transcend temporal and geographical challenges for HCV treatment access for people with OUD. COVID-19 fueled changes in health care delivery, including facilitated telemedicine, to expand the opioid treatment program's mission as a medical home for people with OUD. Conclusions: Opioid treatment programs can sustain facilitated telemedicine to increase health care access for underserved populations. COVID-19-induced disruptions promoted innovation and policy changes recognizing telemedicine's role in expanding health care access to underserved populations. ClinicalTrials.gov Identifier: NCT02933970.

Comprehensive surveillance of acute respiratory infections during the COVID-19 pandemic: a methodological approach using sentinel networks, Castilla y León, Spain, January 2020 to May 2022.

BackgroundSince 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.AimTo describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.MethodsUsing principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.ResultsARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.ConclusionExtracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.

Using Nanomaterials for SARS-CoV-2 Sensing via Electrochemical Techniques.

Advancing low-cost and user-friendly innovations to benefit public health is an important task of scientific and engineering research. According to the World Health Organization (WHO), electrochemical sensors are being developed for low-cost SARS-CoV-2 diagnosis, particularly in resource-limited settings. Nanostructures with sizes ranging from 10 nm to a few micrometers could deliver optimum electrochemical behavior (e.g., quick response, compact size, sensitivity and selectivity, and portability), providing an excellent alternative to the existing techniques. Therefore, nanostructures, such as metal, 1D, and 2D materials, have been successfully applied in in vitro and in vivo detection of a wide range of infectious diseases, particularly SARS-CoV-2. Electrochemical detection methods reduce the cost of electrodes, provide analytical ability to detect targets with a wide variety of nanomaterials, and are an essential strategy in biomarker sensing as they can rapidly, sensitively, and selectively detect SARS-CoV-2. The current studies in this area provide fundamental knowledge of electrochemical techniques for future applications.

Neutralizing antibody levels and epidemiological information of patients with breakthrough COVID-19 infection in Toyama, Japan.

Breakthrough infection (BI) after coronavirus disease 2019 (COVID-19) vaccination has exploded owing to the emergence of various SARS-CoV-2 variants and has become a major problem at present. In this study, we analyzed the epidemiological information and possession status of neutralizing antibodies in patients with BI using SARS-CoV-2 pseudotyped viruses (SARS-CoV-2pv). Analysis of 44 specimens diagnosed with COVID-19 after two or more vaccinations showed high inhibition of infection by 90% or more against the Wuhan strain and the Alpha and Delta variants of pseudotyped viruses in 40 specimens. In contrast, almost no neutralizing activity was observed against the Omicron BA.1 variant. Many cases without neutralizing activity or BI were immunosuppressed individuals. The results of this study show that contact with an infected person can result in BI even when there are sufficient neutralizing antibodies in the blood. Thus, even after vaccination, sufficient precautions must be taken to prevent infection.

Virus infection participates in the occurrence and development of human diseases through monoamine oxidase.

Monoamine oxidase (MAO) is a membrane-bound mitochondrial enzyme that maintains the steady state of neurotransmitters and other biogenic amines in biological systems through catalytic oxidation and deamination. MAO dysfunction is closely related to human neurological and psychiatric diseases and cancers. However, little is known about the relationship between MAO and viral infections in humans. This review summarises current research on how viral infections participate in the occurrence and development of human diseases through MAO. The viruses discussed in this review include hepatitis C virus, dengue virus, severe acute respiratory syndrome coronavirus 2, human immunodeficiency virus, Japanese encephalitis virus, Epstein-Barr virus, and human papillomavirus. This review also describes the effects of MAO inhibitors such as phenelzine, clorgyline, selegiline, M-30, and isatin on viral infectious diseases. This information will not only help us to better understand the role of MAO in the pathogenesis of viruses but will also provide new insights into the treatment and diagnosis of these viral diseases.

A Dual-Approach Strategy to Optimize the Safety and Efficacy of Anti-Zika Virus Monoclonal Antibody Therapeutics.

Antibody-dependent enhancement of infection (ADE) is clinically relevant to Dengue virus (DENV) infection and poses a major risk to the application of monoclonal antibody (mAb)-based therapeutics against related flaviviruses such as the Zika virus (ZIKV). Here, we tested a two-tier approach for selecting non-cross-reactive mAbs combined with modulating Fc glycosylation as a strategy to doubly secure the elimination of ADE while preserving Fc effector functions. To this end, we selected a ZIKV-specific mAb (ZV54) and generated three ZV54 variants using Chinese hamster ovary cells and wild-type (WT) and glycoengineered ΔXF Nicotiana benthamiana plants as production hosts (ZV54CHO, ZV54WT, and ZV54ΔXF). The three ZV54 variants shared an identical polypeptide backbone, but each exhibited a distinct Fc N-glycosylation profile. All three ZV54 variants showed similar neutralization potency against ZIKV but no ADE activity for DENV infection, validating the importance of selecting the virus/serotype-specific mAbs for avoiding ADE by related flaviviruses. For ZIKV infection, however, ZV54CHO and ZV54ΔXF showed significant ADE activity while ZV54WT completely forwent ADE, suggesting that Fc glycan modulation may yield mAb glycoforms that abrogate ADE even for homologous viruses. In contrast to the current strategies for Fc mutations that abrogate all effector functions along with ADE, our approach allowed the preservation of effector functions as all ZV54 glycovariants retained antibody-dependent cellular cytotoxicity (ADCC) against the ZIKV-infected cells. Furthermore, the ADE-free ZV54WT demonstrated in vivo efficacy in a ZIKV-infection mouse model. Collectively, our study provides further support for the hypothesis that antibody-viral surface antigen and Fc-mediated host cell interactions are both prerequisites for ADE, and that a dual-approach strategy, as shown herein, contributes to the development of highly safe and efficacious anti-ZIKV mAb therapeutics. Our findings may be impactful to other ADE-prone viruses, including SARS-CoV-2.

COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images.

The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.

Zoonotic febrile illnesses misdiagnosed as COVID-19: a review of reported clinical cases.

COVID-19 is a zoonotic coronavirus disease caused by SARS-CoV-2. Its fast spreading by aerosol transmission has made it a highly contagious disease, causing the most recent 2020 pandemic. Although it mainly affects the respiratory system, atypical forms of the disease have been described, including developing an undifferentiated febrile illness without respiratory symptoms, that can represent a diagnostic challenge, mainly in tropical areas where several zoonotic febrile diseases are circulating. Thus, despite the broad clinical spectrum of COVID-19, in the tropics, other zoonotic etiologies should always be considered as differential diagnoses. According to our case reports review, eight different zoonotic febrile diseases misdiagnosed as COVID-19 have been reported in the available scientific literature of four databases. These were only suspected due to the epidemiological history. Thus, making a complete and detailed clinical history of a febrile patient in the tropics is essential to suspect the etiology and request the necessary confirmatory tests. Therefore, COVID-19 must be included as a differential diagnosis of undifferentiated febrile illness in the tropics, but other zoonotic infectious diseases must not be ruled out.

[Antiviral activity of basidial fungus Inonotus obliquus aqueous extract against SARS-CоV-2 virus (Coronaviridae: Betacoronavirus: Sarbecovirus) in vivo in BALB/c mice model].

INTRODUCTION: The COVID-19 pandemic combined with seasonal epidemics of respiratory viral diseases requires targeted antiviral prophylaxis with restorative and immunostimulant drugs. The compounds of natural origin are low-toxic, but active against several viruses at the same time. One of the most famous compounds is Inonotus obliquus aqueous extract. The fruit body of basidial fungus I. obliquus is called Chaga mushroom. The aim of the work ‒ was to study the antiviral activity of I. obliquus aqueous extract against the SARS-CoV-2 virus in vivo. MATERIALS AND METHODS: Antiviral activity of I. obliquus aqueous extract sample (#20-17) was analyzed against strain of SARS-CoV-2 Omicron ВА.5.2 virus. The experiments were carried out in BALB/c inbred mice. The SARS-CoV-2 viral load was measured using quantitative real-time PCR combined with reverse transcription. The severity of lung tissue damage was assessed by histological methods. RESULTS: The peak values of the viral load in murine lung tissues were determined 72 hours after intranasal inoculation at dose of 2,85 lg TCID50. The quantitative real-time PCR testing has shown a significant decrease in the viral load compared to the control group by 4,65 lg copies/ml and 5,72 lg copies/ml in the lung tissue and nasal cavity samples, respectively. Histological methods revealed that the decrease in the number and frequency of observed pathomorphological changes in murine lung tissues depended on the introduction of the compound under study. CONCLUSION: The results obtained indicate the possibility of using basidial fungus Inonotus obliquus aqueous extract as a preventive agent against circulating variants of SARS-CoV-2 virus.

A phase 1, randomized, placebo-controlled, dose-ranging study to evaluate the safety and immunogenicity of an mRNA-based chikungunya virus vaccine in healthy adults.

BACKGROUND: Chikungunya, a mosquito-borne viral disease caused by the chikungunya virus (CHIKV), causes a significant global health burden, and there is currently no approved vaccine to prevent chikungunya disease. In this study, the safety and immunogenicity of a CHIKV mRNA vaccine candidate (mRNA-1388) were evaluated in healthy participants in a CHIKV-nonendemic region. METHODS: This phase 1, first-in-human, randomized, placebo-controlled, dose-ranging study enrolled healthy adults (ages 18-49 years) between July 2017 and March 2019 in the United States. Participants were randomly assigned (3:1) to receive 2 intramuscular injections 28 days apart with mRNA-1388 in 3 dose-level groups (25 µg, 50 µg, and 100 µg) or placebo and were followed for up to 1 year. Safety (unsolicited adverse events [AEs]), tolerability (local and systemic reactogenicity; solicited AEs), and immunogenicity (geometric mean titers [GMTs] of CHIKV neutralizing and binding antibodies) of mRNA-1388 versus placebo were evaluated. RESULTS: Sixty participants were randomized and received ≥ 1 vaccination; 54 (90 %) completed the study. mRNA-1388 demonstrated favorable safety and reactogenicity profiles at all dose levels. Immunization with mRNA-1388 induced substantial and persistent humoral responses. Dose-dependent increases in neutralizing antibody titers were observed; GMTs (95 % confidence intervals [CIs]) at 28 days after dose 2 were 6.2 (5.1-7.6) for mRNA-1388 25 µg, 53.8 (26.8-108.1) for mRNA-1388 50 µg, 92.8 (43.6-197.6) for mRNA-1388 100 µg, and 5.0 (not estimable) for placebo. Persistent humoral responses were observed up to 1 year after vaccination and remained higher than placebo in the 2 higher mRNA-1388 dose groups. The development of CHIKV-binding antibodies followed a similar trend as that observed with neutralizing antibodies. CONCLUSIONS: mRNA-1388, the first mRNA vaccine against CHIKV, was well tolerated and elicited substantial and long-lasting neutralizing antibody responses in healthy adult participants in a nonendemic region. CLINICALTRIALS: gov: NCT03325075.

Solid-Phase Optical Sensing Techniques for Sensitive Virus Detection.

Viral infections can pose a major threat to public health by causing serious illness, leading to pandemics, and burdening healthcare systems. The global spread of such infections causes disruptions to every aspect of life including business, education, and social life. Fast and accurate diagnosis of viral infections has significant implications for saving lives, preventing the spread of the diseases, and minimizing social and economic damages. Polymerase chain reaction (PCR)-based techniques are commonly used to detect viruses in the clinic. However, PCR has several drawbacks, as highlighted during the recent COVID-19 pandemic, such as long processing times and the requirement for sophisticated laboratory instruments. Therefore, there is an urgent need for fast and accurate techniques for virus detection. For this purpose, a variety of biosensor systems are being developed to provide rapid, sensitive, and high-throughput viral diagnostic platforms, enabling quick diagnosis and efficient control of the virus's spread. Optical devices, in particular, are of great interest due to their advantages such as high sensitivity and direct readout. The current review discusses solid-phase optical sensing techniques for virus detection, including fluorescence-based sensors, surface plasmon resonance (SPR), surface-enhanced Raman scattering (SERS), optical resonators, and interferometry-based platforms. Then, we focus on an interferometric biosensor developed by our group, the single-particle interferometric reflectance imaging sensor (SP-IRIS), which has the capability to visualize single nanoparticles, to demonstrate its application for digital virus detection.

Expansion of hantavirus infection during the SARS-CoV-2 pandemic in Bosnia and Herzegovina, 2021.

Introduction. Bosnia and Herzegovina (B and H) has been recognized for decades as a country with a high risk of diseases caused by hantaviruses.Gap statement. The severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2) pandemic has diverted attention from many pathogens, including hantavirus.Aim. To provide a socio-demographic, temporal, geographical and clinical laboratory overview of the expansion of hantavirus infection cases during the SARS-CoV-2 pandemic in B and H in 2021.Methodology. The RecomLine HantaPlus IgG, IgM immuno-line assay (Mikrogen, Germany) was used to detect IgG and IgM antibodies to hantavirus serotypes in human sera from clinically suspected cases.Results. In 2021 (January-October), the number of confirmed cases of hantavirus infection and tested persons (92/140; 65,71 %) was higher than in the previous 2 years, 2020 (2/20; 10.00 %) and 2019 (10/61; 16.39 %). Most of the infected persons were men (84/92; 91.30 %). Hantavirus infections were recorded from January to October 2021, and the peak was reached in July (25/92; 27.17 %). Six out of 10 cantons in the Federation of Bosnia and Herzegovina (FB and H) were affected, namely Sarajevo Canton, Central Bosnia Canton, Neretva Canton, Zenica-Doboj Canton, Posavina Canton and Bosnian-Podrinje Canton Gorazde, in descending order. Of the 38/92 (41.30 %) infected patients with characteristic clinical manifestations of haemorrhagic fever, including renal (mainly) or pulmonary syndrome, 32/92 (34.78 %) were hospitalized in the Clinical Center of the University of Sarajevo. Two cases were detected with dual infection, hantavirus (Puumala) with Leptospira in one and SARS-CoV-2 in another case. The largest number of infections was related to Puumala (PUUV) (83/92; 90.22 %), while the rest of the infections were caused by the hantavirus Dobrava serotype (DOBV).Conclusion. The reported infections were probably caused by exposure of individuals to at-risk areas inhabited by contaminated rodents as natural reservoirs of hantavirus. As a highly endemic area, B and H requires continuous monitoring and increased awareness of this problem.

Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs against them: An in silico approach.

Monkeypox virus (mpox virus) outbreak has rapidly spread to 82 non-endemic countries. Although it primarily causes skin lesions, secondary complications and high mortality (1-10%) in vulnerable populations have made it an emerging threat. Since there is no specific vaccine/antiviral, it is desirable to repurpose existing drugs against mpox virus. With little knowledge about the lifecycle of mpox virus, identifying potential inhibitors is a challenge. Nevertheless, the available genomes of mpox virus in public databases represent a goldmine of untapped possibilities to identify druggable targets for the structure-based identification of inhibitors. Leveraging this resource, we combined genomics and subtractive proteomics to identify highly druggable core proteins of mpox virus. This was followed by virtual screening to identify inhibitors with affinities for multiple targets. 125 publicly available genomes of mpox virus were mined to identify 69 highly conserved proteins. These proteins were then curated manually. These curated proteins were funnelled through a subtractive proteomics pipeline to identify 4 highly druggable, non-host homologous targets namely; A20R, I7L, Top1B and VETFS. High-throughput virtual screening of 5893 highly curated approved/investigational drugs led to the identification of common as well as unique potential inhibitors with high binding affinities. The common inhibitors, i.e., batefenterol, burixafor and eluxadoline were further validated by molecular dynamics simulation to identify their best potential binding modes. The affinity of these inhibitors suggests their repurposing potential. This work can encourage further experimental validation for possible therapeutic management of mpox.