The effect of early treatment with ivermectin on viral load, symptoms and humoral response in patients with non-severe COVID-19: A pilot, double-blind, placebo-controlled, randomized clinical trial.

Background Ivermectin inhibits the replication of SARS-CoV-2 in vitro at concentrations not readily achievable with currently approved doses. There is limited evidence to support its clinical use in COVID-19 patients. We conducted a Pilot, randomized, double-blind, placebo-controlled trial to evaluate the efficacy of a single dose of ivermectin reduce the transmission of SARS-CoV-2 when administered early after disease onset. Methods Consecutive patients with non-severe COVID-19 and no risk factors for complicated disease attending the emergency room of the Clínica Universidad de Navarra between July 31, 2020 and September 11, 2020 were enrolled. All enrollments occurred within 72 h of onset of fever or cough. Patients were randomized 1:1 to receive ivermectin, 400 mcg/kg, single dose (n = 12) or placebo (n = 12). The primary outcome measure was the proportion of patients with detectable SARS-CoV-2 RNA by PCR from nasopharyngeal swab at day 7 post-treatment. The primary outcome was supported by determination of the viral load and infectivity of each sample. The differences between ivermectin and placebo were calculated using Fisher's exact test and presented as a relative risk ratio. This study is registered at ClinicalTrials.gov: NCT04390022. Findings All patients recruited completed the trial (median age, 26 [IQR 19-36 in the ivermectin and 21-44 in the controls] years; 12 [50%] women; 100% had symptoms at recruitment, 70% reported headache, 62% reported fever, 50% reported general malaise and 25% reported cough). At day 7, there was no difference in the proportion of PCR positive patients (RR 0·92, 95% CI: 0·77-1·09, p = 1·0). The ivermectin group had non-statistically significant lower viral loads at day 4 (p = 0·24 for gene E; p = 0·18 for gene N) and day 7 (p = 0·16 for gene E; p = 0·18 for gene N) post treatment as well as lower IgG titers at day 21 post treatment (p = 0·24). Patients in the ivermectin group recovered earlier from hyposmia/anosmia (76 vs 158 patient-days; p < 0.001). Interpretation Among patients with non-severe COVID-19 and no risk factors for severe disease receiving a single 400 mcg/kg dose of ivermectin within 72 h of fever or cough onset there was no difference in the proportion of PCR positives. There was however a marked reduction of self-reported anosmia/hyposmia, a reduction of cough and a tendency to lower viral loads and lower IgG titers which warrants assessment in larger trials. Funding ISGlobal, Barcelona Institute for Global Health and Clínica Universidad de Navarra

Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence

Research question Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs.h(-1); p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring

Nef- VIH-1 como principal orquestador de la disfunción celular durante la inmunopatogenia de la infección por el VIH-1

Human immunodeficiency virus-1 (HIV-1) is one of the most studied viruses during the past three decades, their characterization has allowed to deepen in their pathogenic mechanisms and extrapolate to other viral infectious diseases, however their pathogenic mechanisms, and the knowledge of immune system dysfunction, remain unclear yet. Despite of the advances in this field there are still many unanswered questions, especially those that are associated with the immune response evasion mechanisms and the role of accessory proteins, such as Nef. In this review we show the most recent findings of the immunopathogenic mechanism of the infection and the role of Nef as a modulator of the immune response based on their functional domains.El virus de la inmunodeficiencia humana-1 (VIH-1) es uno de los agentes infecciosos más estudiado en las últimas tres décadas, lo que ha permitido un avance en la profundización en sus mecanismos patogénicos y extrapolarlos al de otras enfermedades virales, sin embargo y a pesar de los avances en este campo aún existen muchas interrogantes por resolver, en especial aquellas que están asociadas con la evasión de la respuesta inmune y en el papel de las proteínas accesorias como Nef. En esta revisión se abordaran los aspectos más recientes de la inmunopatogenia de la infección y además el papel de Nef como modulador de la respuesta inmune basados en sus dominios funcionales

Uncommon and Neglected Venezuelan Viral Diseases: Etiologic Agents, Physiopathological, Clinical and Epidemiological Characteristics

Viral infectious diseases are common in Venezuela, influenza, dengue, yellow fever, HIV infection, viral Hepatitis, chikungunya fever and many others represent public health problems in the country and therefore, have been well documented. However, other rarer and even unique or lethal viral illnesses present in Venezuela are usually poorly understood or even unknown. This review described Venezuelan Hemorrhagic Fever, Venezuelan Equine Encephalitis, Hantavirus Infections and Mayaro fever, named as neglected diseases, emphasizing the etiologic agents and their most relevant pathogenic mechanisms, clinical and epidemiological characteristics. Although there is not an official report about the re-emergence of these diseases, falling living standards and unsanitary conditions, together with limited accessibility to hygiene products and medical supplies, put us on alert about the re-emergence of these neglected diseases.Las enfermedades infecciosas virales son comunes en Venezuela, influenza, dengue, fiebre amarilla, infección por VIH, hepatitis viral, fiebre chikungunya y muchas otras representan problemas de salud pública en el país y por lo tanto, han sido bien documentadas. Sin embargo, otras enfermedades virales más raras e incluso únicas y letales presentes en Venezuela son generalmente poco estudiadas y hasta desconocidas. Esta revisión describe alguna de estas enfermedades olvidadas tales como la fiebre hemorrágica venezolana, la encefalitis equina venezolana, las infecciones por hantavirus y la fiebre de Mayaro, haciendo hincapié en los agentes etiológicos y en sus mecanismos patogénicos más relevantes, características clínicas y epidemiológicas. Aunque no hay informes oficiales sobre el resurgimiento de estas enfermedades, la caída de los niveles de vida y las condiciones insalubres, junto con el acceso limitado a los productos de higiene y suministros médicos, debe alertar sobre el resurgimiento de estas enfermedades olvidadas

Longitudinal passive cough monitoring and its implications for detecting changes in clinical status

Research question What is the impact of the duration of cough monitoring on its accuracy in detecting changes in the cough frequency? Materials and methods This is a statistical analysis of a prospective cohort study. Participants were recruited in the city of Pamplona (Northern Spain), and their cough frequency was passively monitored using smartphone-based acoustic artificial intelligence software. Differences in cough frequency were compared using a one-tailed Mann–Whitney U test and a randomisation routine to simulate 24-h monitoring. Results 616 participants were monitored for an aggregated duration of over 9 person-years and registered 62 325 coughs. This empiric analysis found that an individual's cough patterns are stochastic, following a binomial distribution. When compared to continuous monitoring, limiting observation to 24 h can lead to inaccurate estimates of change in cough frequency, particularly in persons with low or small changes in rate. Interpretation Detecting changes in an individual's rate of coughing is complicated by significant stochastic variability within and between days. Assessing change based solely on intermittent sampling, including 24-h, can be misleading. This is particularly problematic in detecting small changes in individuals who have a low rate and/or high variance in cough pattern

Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence

Research question Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs·h−1; p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring

Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence

Research question Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs.h(-1); p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring