Chikungunya Outbreak in the Republic of the Congo, 2019—Epidemiological, Virological and Entomological Findings of a South-North Multidisciplinary Taskforce Investigation

The Republic of Congo (RoC) declared a chikungunya (CHIK) outbreak on 9 February 2019. We conducted a ONE-Human-Animal HEALTH epidemiological, virological and entomological investigation. Methods: We collected national surveillance and epidemiological data. CHIK diagnosis was based on RT-PCR and CHIKV-specific antibodies. Full CHIKV genome sequences were obtained by Sanger and MinION approaches and Bayesian tree phylogenetic analysis was performed. Mosquito larvae and 215 adult mosquitoes were collected in different villages of Kouilou and Pointe-Noire districts and estimates of Aedes (Ae.) mosquitos’ CHIKV-infectious bites obtained. We found two new CHIKV sequences of the East/Central/South African (ECSA) lineage, clustering with the recent enzootic sub-clade 2, showing the A226V mutation. The RoC 2019 CHIKV strain has two novel mutations, E2-T126M and E2-H351N. Phylogenetic suggests a common origin from 2016 Angola strain, from which it diverged around 1989 (95% HPD 1985–1994). The infectious bite pattern was similar for 2017, 2018 and early 2019. One Ae. albopictus pool was RT-PCR positive. The 2019 RoC CHIKV strain seems to be recently introduced or be endemic in sylvatic cycle. Distinct from the contemporary Indian CHIKV isolates and in contrast to the original Central-African strains (transmitted by Ae. aegypti), it carries the A226V mutation, indicating an independent adaptive mutation in response to vector replacement (Ae. albopictus vs Ae. aegypti)

Prolonged higher dose methylprednisolone vs. conventional dexamethasone in COVID-19 pneumonia: a randomised controlled trial (MEDEAS)

Dysregulated systemic inflammation is the primary driver of mortality in severe COVID-19 pneumonia. Current guidelines favor a 7-10-day course of any glucocorticoid equivalent to dexamethasone 6 mg·day-1. A comparative RCT with a higher dose and a longer duration of intervention was lacking

Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications

The human brain’s role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals’ cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200–300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90–140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones

The capacity of action observation to drag the trainees' motor pattern toward the observed model

Abstract Action Observation Training (AOT) promotes the acquisition of motor abilities. However, while the cortical modulations associated with the AOT efficacy are well known, few studies investigated the AOT peripheral neural correlates and whether their dynamics move towards the observed model during the training. We administered seventy-two participants (randomized into AOT and Control groups) with training for learning to grasp marbles with chopsticks. Execution practice was preceded by an observation session, in which AOT participants observed an expert performing the task, whereas controls observed landscape videos. Behavioral indices were measured, and three hand muscles' electromyographic (EMG) activity was recorded and compared with the expert. Behaviorally, both groups improved during the training, with AOT outperforming controls. The EMG trainee-model similarity also increased during the training, but only for the AOT group. When combining behavioral and EMG similarity findings, no global relationship emerged; however, behavioral improvements were "locally" predicted by the similarity gain in muscles and action phases more related to the specific motor act. These findings reveal that AOT plays a magnetic role in motor learning, attracting the trainee's motor pattern toward the observed model and paving the way for developing online monitoring tools and neurofeedback protocols

The Surveillance of Chikungunya Virus in a Temperate Climate: Challenges and Possible Solutions from the Experience of Lazio Region, Italy

CHIKV has become an emerging public health concern in the temperate regions of the Northern Hemisphere as a consequenceof the expansion of the endemic areas of its vectors (mainly Aedes aegypti and Aedes albopictus). In 2017, a new outbreak of CHIKV was detected in Italy with three clusters of autochthonous transmission in the Lazio Region (central Italy), in the cities of Anzio, Rome, and Latina and a secondary cluster in the Calabria Region (south Italy). Given the climate characteristics of Italy, sporadic outbreaks mostly driven by imported cases followed by autochthonous transmission could occur during the summer season. This highlights the importance of a well-designed surveillance system, which should promptly identify autochthonous transmission. The use of a surveillance system integrating different surveillance tools, including entomological surveillance in a one health approach, together with education of the health care professionals should facilitate the detection, response, and control of arboviruses spreading

Measles Cases during Ebola Outbreak, West Africa, 2013–2106

The recent Ebola outbreak in West Africa caused breakdowns in public health systems, which might have caused outbreaks of vaccine-preventable diseases. We tested 80 patients admitted to an Ebola treatment center in Freetown, Sierra Leone, for measles. These patients were negative for Ebola virus. Measles virus IgM was detected in 13 (16%) of the patients

Frequency of HD and HIV+ presenting a T-cell response to different influenza A virus strains.

<p><b>* chi-square test:</b></p><p>**p = 0.0023.</p><p>Frequency of HD and HIV+ presenting a T-cell response to different influenza A virus strains.</p

Association between age and cellular immune response to different influenza A virus strains.

<p>The potential and the shape of association between age and T-cell response against A/H3N2/Min/11/10, A/H3N2/Ind/08/11 and A/H1N1/Cal/07/09 is shown for HD (left panels) and HIV+ (right panels) was analyzed by linear regression model. Departure from linear trend was assessed by model based likelihood ratio test (LTR) to assess the model including age as polynomial predictor.</p

Cellular immune response to different influenza A virus strains.

<p>(A): The frequency of T-cell response specific for A/H3N2/Min/11/10, A/H3N2/Ind/08/11 and for A/H1N1/Cal/07/09 was evaluated by ELISpot assay in HD (n = 45, white bars) and HIV+ patients (n = 46 grey bars). Results are indicated as SCFs/10⁶ peripheral mononuclear cells (PBMCs). Statistical analysis was performed using non parametric Mann-Whitney assay. ** p<0.0001. (B): The correlation between T-cell response against A/H3N2/Min/11/10 and A/H3N2/Ind/08/11 is shown, with Pearson's r² and significance, as well as the calculated regression line. HD and HIV+ are reported as white and black circles, respectively.</p

Correlation between humoral immune response to H3N2v strains.

<p>(A): The correlation between HAI titers to A/H3N2/Min/11/10 and to A/H3N2/Ind/08/11 is shown in HD. Pearson's r² and significance, as well as the calculated regression line. (B): The correlation between HAI titers to A/H3N2/Min/11/10 and to A/H3N2/Ind/08/11 is shown in HIV+. Pearson's r² and significance, as well as the calculated regression line.</p