A droplet digital PCR chip with passive bubble removal for absolute nucleic acid quantification

Droplet digital polymerase chain reaction (ddPCR) is an extremely sensitive method for the precisely determining the concentration of target nucleic acids. However, air bubbles between droplets during amplification can cause significant droplet loss and decreased accuracy in results. In the present study, an all-in-one microfluidic chip that integrates emulsification, passive bubble removal, droplet monolayer storage, on-chip nucleic acid amplification, and droplet fluorescence signal readout is proposed. The integrated passive bubble removal structures automatically complete the trapping and guiding of the bubbles, ensuring that the droplets do not touch the bubbles during amplification and thus is not lost. The ddPCR device with optimized key parameters proved to be effective and efficient by completely removing bubbles between droplets and having a dead volume of less than 1 %. The ability of the ddPCR chip to accurately quantify nucleic acids was evaluated by measuring plasmids with the SARS-CoV-2N gene at concentrations ranging from 10 to 50 000 copies/μL. The innovative ddPCR device satisfies the requirement for accurate nucleic acid quantification and is expected to accelerate the popularity of dPCR due to its low processing difficulty, ease of use and high robustness.

An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Determinants of point-of-care ultrasound lung sliding amplitude in mechanically ventilated patients.

BACKGROUND: Although lung sliding seen by point-of-care ultrasound (POCUS) is known to be affected to varying degrees by different physiologic and pathologic processes, it is typically only reported qualitatively in the critical care setting. Lung sliding amplitude quantitatively expresses the amount of pleural movement seen by POCUS but its determinants in mechanically ventilated patients are largely unknown. METHODS: This was a single-center, prospective, observational pilot study examining 40 hemithoraces in 20 adult patients receiving mechanical ventilation. Each subject had lung sliding amplitude measured in both B-mode and by pulsed wave Doppler at their bilateral lung apices and bases. Differences in lung sliding amplitude were correlated with anatomical location (apex vs base) as well as physiologic parameters including positive end expiratory pressure (PEEP), driving pressure, tidal volume and the ratio of arterial partial pressure of oxygen (PaO2) to fraction of inspired oxygen (FiO2). RESULTS: POCUS lung sliding amplitude was significantly lower at the lung apex compared to the lung base in both B-mode (3.6 ± 2.0 mm vs 8.6 ± 4.3 mm; p < 0.001) and the pulsed wave Doppler mode (10.3 ± 4.6 cm/s vs 13.9 ± 5.5 cm/s; p < 0.001) corresponding to expected distribution of ventilation to the lung bases. Inter-rater reliability of B-mode measurements was excellent (ICC = 0.91) and distance traversed in B-mode had a significant positive correlation with pleural line velocity (r2 = 0.32; p < 0.001). There was a non-statistically significant trend towards lower lung sliding amplitude for PEEP ≥ 10 cmH2O, as well as for driving pressure ≥ 15 cmH2O in both ultrasound modes. CONCLUSION: POCUS lung sliding amplitude was significantly lower at the lung apex than the lung base in mechanically ventilated patients. This was true when using both B-mode and pulsed wave Doppler. Lung sliding amplitude did not correlate with PEEP, driving pressure, tidal volume or PaO2:FiO2 ratio. Our findings suggest that lung sliding amplitude can be quantified in mechanically ventilated patients in a physiologically predictable way and with high inter-rater reliability. A better understanding of POCUS derived lung sliding amplitude and its determinants may aid in the more accurate diagnosis of lung pathologies, including pneumothorax, and could serve as a means of further reducing radiation exposure and improving outcomes in critically ill patients.

Identification and characterization of quaternary ammonium compounds in Flemish indoor dust by ion-mobility high-resolution mass spectrometry.

Quaternary ammonium compounds (QACs) are a class of surfactants commonly used in disinfecting and cleaning products. Their use has substantially increased during the COVID-19 pandemic leading to increasing human exposure. QACs have been associated with hypersensitivity reactions and an increased risk of asthma. This study introduces the first identification, characterization and semi-quantification of QACs in European indoor dust using ion-mobility high-resolution mass spectrometry (IM-HRMS), including the acquisition of collision cross section values (DTCCSN2) for targeted and suspect QACs. A total of 46 indoor dust samples collected in Belgium were analyzed using target and suspect screening. Targeted QACs (n = 21) were detected with detection frequencies ranging between 4.2 and 100 %, while 15 QACs showed detection frequencies > 90 %. Semi-quantified concentrations of individual QACs showed a maximum of 32.23 µg/g with a median ∑QAC concentration of 13.05 µg/g and allowed the calculation of Estimated Daily Intakes for adults and toddlers. Most abundant QACs matched the patterns reported in indoor dust collected in the United States. Suspect screening allowed the identification of 17 additional QACs. A dialkyl dimethyl ammonium compound with mixed chain lengths (C16:C18) was characterized as a major QAC homologue with a maximum semi-quantified concentration of 24.90 µg/g. The high detection frequencies and structural variabilities observed call for more European studies on potential human exposure to these compounds. For all targeted QACs, drift tube IM-HRMS derived collision cross section values (DTCCSN2) are reported. Reference DTCCSN2 values allowed the characterization of CCS-m/z trendlines for each of the targeted QAC classes. Experimental CCS-m/z ratios of suspect QACs were compared with the CCS-m/z trendlines. The alignment between the two datasets served as an additional confirmation of the assigned suspect QACs. The use of the 4bit multiplexing acquisition mode with consecutive high-resolution demultiplexing confirmed the presence of isomers for two of the suspect QACs.

Quantifier la confiance et le consentement durant la crise épidémique

Quantifying trust and consent during the epidemic crisis. By distinguishing between symbolic trust in public authorities and consent to comply with certain recommendations, this article seeks to revisit how to quantitatively study the relationship between individuals and institutions during the Covid-19 epidemic crisis. Using the first two waves of the Epicov survey, one in May 2020 and the other in November 2020, attitudes of trust towards public authorities can be compared to attitudes towards confinement constraints and the obligation to wear a mask. After presenting the conditions and indicators underlying the statistical measurement of the relationship to institutions in a multidisciplinary survey, the article outlines the social determinants of institutional trust and finally shows that there is no mechanical correlation between the relationship of trust to state institutions and the propensity to comply with the rules they enact. While compliance with the rules of first incarceration is not associated with trust in public authorities, compliance with wearing a mask is, both in the street and in the private sphere. The article thus argues for careful attention to the statistical categorization of trust and partially challenges the causal relationship between trust in institutions and compliance. © The Author(s) 2023.

Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period

Diagnostic and decision-making processes in the 2019 Coronavirus treatment have combined new standards using patient chest images, clinical and laboratory data. This work presents a systematic review aimed at studying the Artificial Intelligence (AI) approaches to the patients' diagnosis or evolution with Coronavirus 2019. Five electronic databases were searched, from December 2019 to October 2020, considering the beginning of the pandemic when there was no vaccine influencing the exploration of Artificial Intelligence-based techniques. The first search collected 839 papers. Next, the s were reviewed, and 138 remained after the inclusion/exclusion criteria was performed. After thorough reading and review by a second group of reviewers, 64 met the study objectives. These papers were carefully analyzed to identify the AI techniques used to interpret the images, clinical and laboratory data, considering a distribution regarding two variables: (i) diagnosis or outcome and (ii) the type of data: clinical, laboratory, or imaging (chest computed tomography, chest X-ray, or ultrasound). The data type most used was chest CT scans, followed by chest X-ray. The chest CT scan was the only data type that was used for diagnosis, outcome, or both. A few works combine Clinical and Laboratory data, and the most used laboratory tests were C-reactive protein. AI techniques have been increasingly explored in medical image annotation to overcome the need for specialized manual work. In this context, 25 machine learning (ML) techniques with a highest frequency of usage were identified, ranging from the most classic ones, such as Logistic Regression, to the most current ones, such as those that explore Deep Learning. Most imaging works explored convolutional neural networks (CNN), such as VGG and Resnet. Then transfer learning which stands out among the techniques related to deep learning has the second highest frequency of use. In general, classification tasks adopted two or three datasets. COVID-19 related data is present in all papers, while pneumonia is the most common non-COVID-19 class among them.

EVALUATION OF AUTOMATED QUANTIFICATION OF CT PATTERNS ASSOCIATED WITH COVID-19 FROM CHEST CT

Introduction: Computed Tomography (CT) is rapid and sensitive enough to identify COVID-19 pneumonia in its early stages. But because of the disease's high case load, it is difficult for the talented radiologists to report the cases. Therefore, using Artificial Intelligence (AI) to support radiologists' work will be crucial for producing prompt and precise results. Objective: To determine diagnostic effectiveness of AI in identifying different COVID-19 CT patterns and to correlate the AI findings with the findings appreciated by skilled Radiologists. Material and Methods: A prospective study consisting of 500 patients with RT-PCR positive COVID- 19 patients were evaluated, after obtaining informed consent. Data was analysed and represented in the form of frequencies and proportions. Collected data were analysed by Pearson's correlation coefficient (r), Intra Class Correlation (ICC) coefficient, Bland--Altman analysis. Results: AI can assess the severity of disease quickly and with good accuracy compared to manual analysis by decreasing the time taken to analyse the scan by 50%, and overall accuracy of approximately 90%. Conclusion: We conclude that as manual analysis of Chest CT in COVID-19 high case load scenario is comparatively more time-consuming, there is a need for a quick, accurate, and automated technique for identification and quantification of common findings in COVID-19. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Assessing Ecological and Socio-Economic Attributes in Sustainable Management of Solid Medical Waste in Urban Environment

Unreliable system of solid waste management has hindered performance of public health system in developing countries. This condition was exacerbated by the covid-19 pandemic which posed risk to healthcare staff and public that makes the management of medical waste worsening. This study seeks to analyze the existing conditions of community health centre solid medical waste management from ecological, economic and social aspects in Pekanbaru and to design a solid medical waste management model for community health centres in Pekanbaru by identifying and quantifying ecological and socio-economic attributes to help solid medical monitor waste. A mixed method approach is used in this study with inferential analysis. Data analysis was used to analyze the relationship of ecological, economic and social factors to the management of solid medical waste at community health centres in Pekanbaru. The analysis process included univariate and bivariate analysis using a computerized program. The findings show that monitoring through the waste monitoring application can help monitor waste management in community health centres. As an implication, a solid medical waste management model can be used and implemented to support sustainable solid medical waste management. © 2023 WITPress. All rights reserved.

Medical diagnostic value of digital PCR (dPCR): A systematic review

Digital polymerase chain reaction (dPCR) is an emerging technique for the absolute quantification of target nucleic acids. dPCR got attention as a precise quantification tool in preclinical research, particularly when used to detect genetic mutations and result in highly precise measurements. In dPCR, the statistic of Poisson distribution was followed for the random distribution of molecules in different partitions, which is essential for dPCR quantification. Amplified target sequences in different partitions are identified by fluorescence and each partition functions as a separate PCR microreactor. Without the need for calibration, the percentage of PCR-positive partitions is sufficient to estimate the concentration of the target sequence. The present revolution in digital quantification was made possible by advancements in microfluidics, which provided effective partitioning techniques. In this paper, the contrast of the underlying ideas of quantitative real-time PCR with dPCR for the measurement of nucleic acids quantity Polymerase chain reaction (q-PCR). This review study briefly introduced the background of dPCR and compared different types of PCR, particularly the quantity of real-time qPCR and digital PCR. The fundamental concept of dPCR is also explained and also briefly compares the advantages of dPCR over qPCR and analyzes the applications of dPCR as a diagnostic tool for cancer and different types of viral species.

Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models.

During the SARS-CoV-2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are mis-specified. We introduce an SIR-type model with the infected population structured by 'infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly mis-specify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs.

PURPOSE: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS: GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.

Evaluation of low-cost SARS-CoV-2 RNA purification methods for viral quantification by RT-qPCR and next-generation sequencing analysis: Implications for wider wastewater-based epidemiology adoption.

Based Epidemiology (WBE) consists of quantifying biomarkers in sewerage systems to derive real-time information on the health and/or lifestyle of the contributing population. WBE usefulness was vastly demonstrated in the context of the COVID-19 pandemic. Many methods for SARS-CoV-2 RNA determination in wastewater were devised, which vary in cost, infrastructure requirements and sensitivity. For most developing countries, implementing WBE for viral outbreaks, such as that of SARS-CoV-2, proved challenging due to budget, reagent availability and infrastructure constraints. In this study, we assessed low-cost methods for SARS-CoV-2 RNA quantification by RT-qPCR, and performed variant identification by NGS in wastewater samples. Results showed that the effect of adjusting pH to 4 and/or adding MgCl2 (25 mM) was negligible when using the adsorption-elution method, as well as basal physicochemical parameters in the sample. In addition, results supported the standardized use of linear rather than plasmid DNA for a more accurate viral RT-qPCR estimation. The modified TRIzol-based purification method in this study yielded comparable RT-qPCR estimation to a column-based approach, but provided better NGS results, suggesting that column-based purification for viral analysis should be revised. Overall, this work provides evaluation of a robust, sensitive and cost-effective method for SARS-CoV-2 RNA analysis that could be implemented for other viruses, for a wider WEB adoption.

COVID-19 Chest CT Quantification: Triage and Prognostic Value in Different Ages.

Objective: We evaluated the triage and prognostic performance of seven proposed computed tomography (CT)-severity score (CTSS) systems in two different age groups.Design: Retrospective study.Setting: COVID-19 pandemic.Participants: Admitted COVID-19, PCR-positive patients were included, excluding patients with heart failure and significant pre-existing pulmonary disease.Methods: Patients were divided into two age groups: ≥65 years and ≤64 years. Clinical data indicating disease severity at presentation and at peak disease severity were recorded. Initial CT images were scored by two radiologists according to seven CTSSs (CTSS1-CTSS7). Receiver operating characteristic (ROC) analysis for the performance of each CTSS in diagnosing severe/critical disease on admission (triage performance) and at peak disease severity (prognostic performance) was done for the whole cohort and each age group separately.Results: Included were 96 patients. Intraclass correlation coefficient (ICC) between the two radiologists scoring the CT scan images were good for all the CTSSs (ICC=0.764-0.837). In the whole cohort, all CTSSs showed an unsatisfactory area under the curve (AUC) in the ROC curve for triage, excluding CTSS2 (AUC=0.700), and all CTSSs showed acceptable AUCs for prognostic usage (0.759-0.781). In the older group (≥65 years; n=55), all CTSSs excluding CTSS6 showed excellent AUCs for triage (0.804-0.830), and CTSS6 was acceptable (AUC=0.796); all CTSSs showed excellent or outstanding AUCs for prognostication (0.859-0.919). In the younger group (≤64 years; n=41), all CTSSs showed unsatisfactory AUCs for triage (AUC=0.487-0.565) and prognostic usage (AUC=0.668-0.694), excluding CTSS6, showing marginally acceptable AUC for prognostic performance (0.700).Conclusion: Those CTSSs requiring more numerous segmentations, namely CTSS2, CTSS7, and CTSS5 showed the best ICCs; therefore, they are the best when comparison between two separate scores is needed. Irrespective of patients' age, CTSSs show minimal value in triage and acceptable prognostic value in COVID-19 patients. CTSS performance is highly variable in different age groups. It is excellent in those aged ≥65 years, but has little if any value in younger patients. Multicenter studies with larger sample size to evaluate results of this study should be conducted.

Circulating pyruvate is a potent prognostic marker for critical COVID-19 outcomes.

Background: Coronavirus-19 (COVID-19) disease is driven by an unchecked immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus which alters host mitochondrial-associated mechanisms. Compromised mitochondrial health results in abnormal reprogramming of glucose metabolism, which can disrupt extracellular signalling. We hypothesized that examining mitochondrial energy-related signalling metabolites implicated in host immune response to SARS-CoV-2 infection would provide potential biomarkers for predicting the risk of severe COVID-19 illness. Methods: We used a semi-targeted serum metabolomics approach in 273 patients with different severity grades of COVID-19 recruited at the acute phase of the infection to determine the relative abundance of tricarboxylic acid (Krebs) cycle-related metabolites with known extracellular signaling properties (pyruvate, lactate, succinate and α-ketoglutarate). Abundance levels of energy-related metabolites were evaluated in a validation cohort (n=398) using quantitative fluorimetric assays. Results: Increased levels of four energy-related metabolites (pyruvate, lactate, a-ketoglutarate and succinate) were found in critically ill COVID-19 patients using semi-targeted and targeted approaches (p<0.05). The combined strategy proposed herein enabled us to establish that circulating pyruvate levels (p<0.001) together with body mass index (p=0.025), C-reactive protein (p=0.039), D-Dimer (p<0.001) and creatinine (p=0.043) levels, are independent predictors of critical COVID-19. Furthermore, classification and regression tree (CART) analysis provided a cut-off value of pyruvate in serum (24.54 µM; p<0.001) as an early criterion to accurately classify patients with critical outcomes. Conclusion: Our findings support the link between COVID-19 pathogenesis and immunometabolic dysregulation, and show that fluorometric quantification of circulating pyruvate is a cost-effective clinical decision support tool to improve patient stratification and prognosis prediction.

Longitudinal Monitoring of DNA Viral Loads in Transplant Patients Using Quantitative Metagenomic Next-Generation Sequencing.

INTRODUCTION: Immunocompromised patients are prone to reactivations and (re-)infections of multiple DNA viruses. Viral load monitoring by single-target quantitative PCRs (qPCR) is the current cornerstone for virus quantification. In this study, a metagenomic next-generation sequencing (mNGS) approach was used for the identification and load monitoring of transplantation-related DNA viruses. METHODS: Longitudinal plasma samples from six patients that were qPCR-positive for cytomegalovirus (CMV), Epstein-Barr virus (EBV), BK polyomavirus (BKV), adenovirus (ADV), parvovirus B19 (B19V), and torque teno-virus (TTV) were sequenced using the quantitative metagenomic Galileo Viral Panel Solution (Arc Bio, LLC, Cambridge, MA, USA) reagents and bioinformatics pipeline combination. Qualitative and quantitative performance was analysed with a focus on viral load ranges relevant for clinical decision making. RESULTS: All pathogens identified by qPCR were also identified by mNGS. BKV, CMV, and HHV6B were additionally detected by mNGS, and could be confirmed by qPCR or auxiliary bioinformatic analysis. Viral loads determined by mNGS correlated with the qPCR results, with inter-method differences in viral load per virus ranging from 0.19 log10 IU/mL for EBV to 0.90 log10 copies/mL for ADV. TTV, analysed by mNGS in a semi-quantitative way, demonstrated a mean difference of 3.0 log10 copies/mL. Trends over time in viral load determined by mNGS and qPCR were comparable, and clinical thresholds for initiation of treatment were equally identified by mNGS. CONCLUSIONS: The Galileo Viral Panel for quantitative mNGS performed comparably to qPCR concerning detection and viral load determination, within clinically relevant ranges of patient management algorithms.

Lassa Fever. Part 2. Laboratory Diagnostics, Treatment, Development of Medications

Globalization and high-speed means of transportation contribute to the spread of infections dangerous to humans. Airborne pathogens have pandemic potential as currently shown in case of the novel coronavirus SARS-CoV-2. Natural focal Lassa fever (LF) common in West African countries, in 35 cases was registered in non-endemic geographical areas because any person infected with Lassa virus (LASV) is a long-term source of infection (up to two months). Cases of person-to-person infection in endemic territories are described. In Germany, the facts of secondary virus transmission from patients to doctors have been recorded during the examination and blood collection from an apparently healthy person as well as during the autopsy of a deceased subjects due to severe LF course. Nonspecific malaise symptoms in LF are also characteristic of numerous other diseases common on the African continent, e.g., malaria and typhoid fever or viral infections such as yellow fever, Chikungunya, dengue and Zika, monkey pox and Ebola virus disease. In this regard, there may be similar dermatological manifestations. Timely detection of cases and differential diagnosis are crucial to ensure safe patient care and use of affordable antiviral therapy for LL provided by the drug Ribavirin. Research methods for studying LASV use polymerase chain reaction (PCR) for detecting viral RNA, electron microscopy, isolation of infectious virus cultured sensitive cells, indirect immunofluorescence reaction, enzyme immunoassay (ELISA) and immuno-chromatographic assays for the detection of antibodies and/or antigen as well as immunoblotting. Currently, test kits based on molecular and genetic methods are mainly used for LF laboratory diagnostics. Since the 1980s, ribavirin has been used to treat patients with LF. The serum accumulation of the drug in large quantities causes hemolysis, development of anemia and impaired renal function. In this regard, treatment options are being considered with decline in its concentration due to combined use with other antiviral drugs. A search for new therapeutic agents capable of inhibiting viral replication at disease early stage has been in progress due to lack of any approved vaccines.

Quantification Annotation in ISO 24617-12, Second Draft

This paper describes the continuation of a project that aims at establishing an interoperable annotation scheme for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework. After a break, caused by the Covid-19 pandemic, the project was relaunched in early 2022 with a second working draft, which deals with certain issues in the annotation of quantification in a more satisfactory way than the original first working draft.

Don't Be a Victim during a Pandemic! Analysing Security and Privacy Threats in Twitter during COVID-19

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a large-scale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users' security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries' strategies to instigate malicious activities change with the country's pandemic situation. © 2013 IEEE.

Assessment of Mobile Air Cleaners to Reduce the Concentration of Infectious Aerosol Particles Indoors

Airborne transmission via aerosol particles without close human contact is a possible source of infection with airborne viruses such as SARS-CoV-2 or influenza. Reducing this indirect infection risk, which is mostly present indoors, requires wearing adequate respiratory masks, the inactivation of the viruses with radiation or electric charges, filtering of the room air, or supplying ambient air by means of ventilation systems or open windows. For rooms without heating, ventilation, and air conditioning (HVAC) systems, mobile air cleaners are a possibility for filtering out aerosol particles and therefore lowering the probability of indirect infections. The main questions are as follows: (1) How effectively do mobile air cleaners filter the air in a room? (2) What are the parameters that influence this efficiency? (3) Are there room situations that completely prevent the air cleaner from filtering the air? (4) Does the air cleaner flow make the stay in the room uncomfortable? To answer these questions, particle imaging methods were employed. Particle image velocimetry (PIV) was used to determine the flow field in the proximity of the air cleaner inlet and outlet to assess regions of unpleasant air movements. The filtering efficiency was quantified by means of particle image counting as a measure for the particle concentration at multiple locations in the room simultaneously. Moreover, different room occupancies and room geometries were investigated. Our results confirm that mobile air cleaners are suitable devices for reducing the viral load indoors. Elongated room geometries, e.g., hallways, lead to a reduced filtering efficiency, which needs to be compensated by increasing the volume flow rate of the device or by deploying multiple smaller devices. As compared to an empty room, a room occupied with desks, desk separation walls, and people does not change the filtering efficiency significantly, i.e., the change was less than 10%. Finally, the flow induced by the investigated mobile air cleaner does not reach uncomfortable levels, as by defined room comfort standards under these conditions, while at the same time reaching air exchange rates above 6, a value which is recommended for potentially infectious environments.

PUBLIC LIABILITY FOR LACK OF VACCINATION BY PRIVATE HEALTH DOCTORS DURING THE PANDEMIC (PROPOSAL FOR AN OBJECTIFIED DETERMINATION OF MORAL DAMAGES)

The aim of this article is to analyse the repercussions that the recent judgement no. 5/2022 of the Administrative Court of Alicante has on the public compensation system on two different levels. From the internal point of view, the treatment of the concept of compensable damage, the lack of identification and individualised quantification of the damages claimed and the function that seems to be attributed to the civil liability of the Administration in the light of all this are of interest. In addition to the above, the recent sentences handed down to the Valencian public administration-in different jurisdictional orders-on the occasion of its management of the coronavirus pandemic, invite reflection on the advisability of a unified treatment of public financial liability, which avoids excessive fragmentation of the institution, as well as the awarding of disparate compensation to victims of non-pecuniary damage suffered in similar circumstances. In this sense, and in order to achieve a certain uniformity in the calculation of compensation amounts, it is proposed to adopt, in accordance with the legal authorisation of article 34.2 of the LRJSP, some of the formulas of the system for the valuation of damages caused to persons in traffic accidents of the TRLRCSCVM. © 2023, Iustel. All rights reserved.