Characterizing Autism Spectrum Disorder through Fusion of Local Cortical Activation and Global Functional Connectivity using Game-Based Stimuli and a Mobile EEG System.

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment and brain activity in ASD. This study aims to develop a method for assessing and identifying ASD using a social cognitive game-based paradigm combined with electroencephalography (EEG) signaling features. Typically developing (TD) participants and autistic preadolescents and teenagers were recruited to participate in a social game while 12-channel EEG signals were recorded. The EEG signals underwent preprocessing to analyze local brain activities, including event-related potentials (ERPs) and time-frequency features. Additionally, the global brain network's functional connectivity between brain regions was evaluated using phase-lag indices (PLIs). Subsequently, machine learning models were employed to assess the neurophysiological features. Results indicated pronounced ERP components, particularly the late positive potential (LPP), in parietal regions during social training. Autistic preadolescents and teenagers exhibited lower LPP amplitudes and larger P200 amplitudes compared to TD participants. Reduced theta synchronization was also observed in the ASD group. Aberrant functional connectivity within certain time intervals was noted in the ASD group. Machine learning analysis revealed that support-vector machines achieved a sensitivity of 100%, specificity of 91.7%, and accuracy of 95.8% as part of the performance evaluation when utilizing ERP and brain oscillation features for ASD characterization. These findings suggest that social interaction difficulties in autism are linked to specific brain activation patterns. Traditional behavioral assessments face challenges of subjectivity and accuracy, indicating the potential use of social training interfaces and EEG features for cognitive assessment in ASD.

Total Infectome Characterization of Respiratory Infections during the 2022-23 COVID-19 Outbreak in China Revealed Extensive Coinfections with Links to SARS-CoV-2 Status, Age, and Disease Severity.

Between 7 December 2022 and 28 February 2023, China experienced a new wave of COVID-19 that swept across the entire country and resulted in an increasing amount of respiratory infections and hospitalizations. The purpose of this study is to reveal the intensity and composition of coinfecting microbial agents. In total, 196 inpatients were recruited from The Third People's Hospital of Shenzhen, and 169 respiratory and 73 blood samples were collected for metagenomic next-generation sequencing. The total "Infectome" was characterized and compared across different groups defined by the SARS-CoV-2 detection status, age groups, and severity of disease. Our results revealed a total of 22 species of pathogenic microbes (4 viruses, 13 bacteria, and 5 fungi), and more were discovered in the respiratory tract than in blood. The diversity of the total infectome was highly distinguished between respiratory and blood samples, and it was generally higher in patients that were SARS-CoV-2-positive, older in age, and with more severe disease. At the individual pathogen level, HSV-1 seemed to be the major contributor to these differences observed in the overall comparisons. Collectively, this study reveals the highly complex respiratory infectome and high-intensity coinfection in patients admitted to the hospital during the period of the 2023 COVID-19 pandemic in China.

The clinical outcome of COVID-19 is strongly associated with microbiome dynamics in the upper respiratory tract.

OBJECTIVES: The respiratory tract is the portal of entry for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a variety of respiratory pathogens other than SARS-CoV-2 have been associated with severe cases of COVID-19 disease, the dynamics of the upper respiratory microbiota during disease the course of disease, and how they impact disease manifestation, remain uncertain. METHODS: We collected 349 longitudinal upper respiratory samples from a cohort of 65 COVID-19 patients (cohort 1), 28 samples from 28 recovered COVID-19 patients (cohort 2), and 59 samples from 59 healthy controls (cohort 3). All COVID-19 patients originated from the earliest stage of the epidemic in Wuhan. Based on a modified clinical scale, the disease course was divided into five clinical disease phases (pseudotimes): "Healthy" (pseudotime 0), "Incremental" (pseudotime 1), "Critical" (pseudotime 2), "Complicated" (pseudotime 3), "Convalescent" (pseudotime 4), and "Long-term follow-up" (pseudotime 5). Using meta-transcriptomics, we investigated the features and dynamics of transcriptionally active microbes in the upper respiratory tract (URT) over the course of COVID-19 disease, as well as its association with disease progression and clinical outcomes. RESULTS: Our results revealed that the URT microbiome exhibits substantial heterogeneity during disease course. Two clusters of microbial communities characterized by low alpha diversity and enrichment for multiple pathogens or potential pathobionts (including Acinetobacter and Candida) were associated with disease progression and a worse clinical outcome. We also identified a series of microbial indicators that classified disease progression into more severe stages. Longitudinal analysis revealed that although the microbiome exhibited complex and changing patterns during COVID-19, a restoration of URT microbiomes from early dysbiosis toward more diverse status in later disease stages was observed in most patients. In addition, a group of potential pathobionts were strongly associated with the concentration of inflammatory indicators and mortality. CONCLUSION: This study revealed strong links between URT microbiome dynamics and disease progression and clinical outcomes in COVID-19, implying that the treatment of severe disease should consider the full spectrum of microbial pathogens present.

Meta-transcriptomic analysis of companion animal infectomes reveals their diversity and potential roles in animal and human disease.

Companion animals such as cats and dogs harbor diverse microbial communities that can potentially impact human health due to close and frequent contact. To better characterize their total infectomes and assess zoonotic risks, we characterized the overall infectomes of companion animals (cats and dogs) and evaluated their potential zoonotic risks. Meta-transcriptomic analyses were performed on 239 samples from cats and dogs collected across China, identifying 24 viral species, 270 bacterial genera, and two fungal genera. Differences in the overall microbiome and infectome composition were compared across different animal species (cats or dogs), sampling sites (rectal or oropharyngeal), and health status (healthy or diseased). Diversity analyses revealed that viral abundance was generally higher in diseased animals compared to healthy ones, while differences in microbial composition were mainly driven by sampling site, followed by animal species and health status. Disease association analyses validated the pathogenicity of known pathogens and suggested potential pathogenic roles of previously undescribed bacteria and newly discovered viruses. Cross-species transmission analyses identified seven pathogens shared between cats and dogs, such as alphacoronavirus 1, which was detected in both oropharyngeal and rectal swabs albeit with differential pathogenicity. Further analyses showed that some viruses, like alphacoronavirus 1, harbored multiple lineages exhibiting distinct pathogenicity, tissue, or host preferences. Ultimately, a systematic evolutionary screening identified 27 potential zoonotic pathogens in this sample set, with far more bacterial than viral species, implying potential health threats to humans. Overall, our meta-transcriptomic analysis reveals a landscape of actively transcribing microorganisms in major companion animals, highlighting key pathogens, those with the potential for cross-species transmission, and possible zoonotic threats. IMPORTANCE: This study provides a comprehensive characterization of the entire community of infectious microbes (viruses, bacteria, and fungi) in companion animals like cats and dogs, termed the "infectome." By analyzing hundreds of samples from across China, the researchers identified numerous known and novel pathogens, including 27 potential zoonotic agents that could pose health risks to both animals and humans. Notably, some of these zoonotic pathogens were detected even in apparently healthy pets, highlighting the importance of surveillance. The study also revealed key microbial factors associated with respiratory and gastrointestinal diseases in pets, as well as potential cross-species transmission events between cats and dogs. Overall, this work sheds light on the complex microbial landscapes of companion animals and their potential impacts on animal and human health, underscoring the need for monitoring and management of these infectious agents.

Metagenomic analysis of individual mosquito viromes reveals the geographical patterns and drivers of viral diversity.

Mosquito transmitted viruses are responsible for an increasing burden of human disease. Despite this, little is known about the diversity and ecology of viruses within individual mosquito hosts. Here, using a meta-transcriptomic approach, we determined the viromes of 2,438 individual mosquitoes (81 species), spanning ~4,000 km along latitudes and longitudes in China. From these data we identified 393 viral species associated with mosquitoes, including 7 (putative) species of arthropod-borne viruses (that is, arboviruses). We identified potential mosquito species and geographic hotspots of viral diversity and arbovirus occurrence, and demonstrated that the composition of individual mosquito viromes was strongly associated with host phylogeny. Our data revealed a large number of viruses shared among mosquito species or genera, enhancing our understanding of the host specificity of insect-associated viruses. We also detected multiple virus species that were widespread throughout the country, perhaps reflecting long-distance mosquito dispersal. Together, these results greatly expand the known mosquito virome, linked viral diversity at the scale of individual insects to that at a country-wide scale, and offered unique insights into the biogeography and diversity of viruses in insect vectors.