From December 2012 to February 2013, two outbreaks of acute respiratory disease caused by HAdV-7 were reported in China. We investigated possible transmission links between these two seemingly unrelated outbreaks by integration of epidemiological and whole-genome sequencing (WGS) data. WGS analyses showed that the HAdV-7 isolates from the two outbreaks were genetically indistinguishable; however, a 12 bp deletion in the virus-associated RNA gene distinguished the outbreak isolates from other HAdV-7 isolates. Outbreak HAdV-7 isolates demonstrated increased viral replication compared to non-outbreak associated HAdV-7 isolate. Epidemiological data supported that the first outbreak was caused by introduction of the novel HAdV-7 virus by an infected recruit upon arrival at the training base. Nosocomial transmission by close contacts was the most likely source leading to onset of the second HAdV-7 outbreak, establishing the apparent transmission link between the outbreaks. Our findings imply that in-hospital contact investigations should be encouraged to reduce or interrupt further spread of infectious agents when treating outbreak cases, and WGS can provide useful information guiding infection-control interventions.

Objective: Spinal cord stimulation (SCS) is a valuable treatment for patients with disorders of consciousness (DOC). This study used permutation entropy (PeEn) of neural activities to quantify brain responses to SCS. Method: We recruited 14 patients with DOC, including seven patients in minimally conscious state (MCS) and seven patients in vegetative state/unawareness state (VS/UWS). All patients received a single session of 20 min’ continuous SCS. We recorded resting state EEG before, during and after SCS. In this study, PeEn was first calculated to describe overall neural activities changes in SCS. The brain was then divided into frontal, central, parietal and occipital regions to explore spatial SCS modulation effects. Finally, a correlation analysis was conducted between CRS-R values and changes in PeEn on each of the four regions. Results: SCS was associated with short-term changes in neural activities in DOC. When SCS was on, PeEn increased as compared to the baseline. When SCS was shut off, PeEn decreased. The PeEn of all patients after SCS was higher than before SCS, and changes of PeEn for MCS were more significant than those for VS, especially in the frontal region. Conclusion: PeEn from EEG data could be used to evaluate SCS modulation effects, and EEG complexity might be a critical index to describe brain responses to SCS in DOC.

Accurate prognostic prediction in patients with disorders of consciousness (DOC) is a core clinical concern and a formidable challenge in neuroscience. Resting-state EEG has shown promise in identifying electrophysiological prognostic markers and may be easily deployed at the bedside. However, the lack of brain dynamic modeling and the spatial mixture of signals in scalp EEG have constrained our exploration of biomarkers and comprehension of the mechanisms underlying consciousness recovery. Here, we introduce EEG source space analysis and brain dynamics to investigate the brain networks of patients with DOC (n = 178) with different outcomes (six-month follow-up), followed by graph theory and high-order topological analysis to explore the relationship between network structure and prognosis, and finally assess the importance of features. We show that a positive prognosis is associated with large-scale lower levels of low-frequency hypersynchrony. Moreover, we provide evidence that this pattern is driven not by all brain states but only by specific states. Analyses reveal that the positive prognosis is attributed to the network retaining lower segregation, higher integration, and stronger stability compared to the negative prognosis. Furthermore, our results highlight the importance of brain networks derived from brain dynamics in prognosis. The prognosis models based on clinical and neural features can achieve acceptable and even excellent performance under different outcome definitions (AUC = 0.714–0.893). Overall, our study offers new perspectives for the identification of prognostic biomarkers and provides avenues for profound insights into the mechanisms underlying consciousness improvement or recovery.

Frontal transcranial direct current stimulation (tDCS) and parietal tDCS are effective for treating disorders of consciousness (DoC); however, the relative efficacies of these techniques have yet to be determined. This paper compares the neuromodulation effects of frontal and parietal tDCS on DoC. Twenty patients with DoC were recruited and randomly assigned to two groups. One group received single-session frontal tDCS and single-session sham tDCS. The other group received single-session parietal tDCS and single-session sham tDCS. Before and after every tDCS session, we recorded coma recovery scale-revised (CRS-R) values and an electroencephalogram. CRS-R was also used to evaluate the state of consciousness at 9–12-month follow-up. Both single-session frontal and parietal tDCS caused significant changes in the genuine permutation cross-mutual information (G_PCMI) of local frontal and across brain regions (p < 0.05). Furthermore, the changes in G_PCMI values were significantly correlated to the CRS-R scores at 9–12-month follow-up after frontal and parietal tDCS (p < 0.05). The changes in G_PCMI and CRS-R scores were also correlated (p < 0.05). Both frontal tDCS and parietal tDCS exert neuromodulatory effects in DoC and induce significant changes in electrophysiology. G_PCMI can be used to evaluate the neuromodulation effects of tDCS.

Objective: General anesthesia (GA) is necessary for surgery, even for patients in a minimally conscious state (MCS). The characteristics of the electroencephalogram (EEG) signatures of the MCS patients under GA are still unclear. Methods: The EEG during GA were recorded from 10 MCS patients undergoing spinal cord stimulation surgery. The power spectrum, phase-amplitude coupling (PAC), the diversity of connectivity, and the functional network were investigated. Long term recovery was assessed by the Coma Recovery Scale-Revised at one year after the surgery, and the characteristics of the patients with good or bad prognosis status were compared. Results: For the four MCS patients with good prognostic recovery, slow oscillation (0.1-1 Hz) and the alpha band (8-12 Hz) in the frontal areas increased during the maintenance of a surgical state of anesthesia (MOSSA), and “peak-max” and “trough-max” patterns emerged in frontal and parietal areas. During MOSSA, the six MCS patients with bad prognosis demonstrated: increased modulation index, reduced diversity of connectivity (from mean±SD of 0.877±0.003 to 0.776±0.003, p<0.001), reduced function connectivity significantly in theta band (from mean±SD of 1.032±0.043 to 0.589±0.036, p<0.001, in prefrontal-frontal; and from mean±SD of 0.989±0.043 to 0.684±0.036, p<0.001, in frontal-parietal) and reduced local and global efficiency of the network in delta band. Conclusions: A bad prognosis in MCS patients is associated with signs of impaired thalamocortical and cortico-cortical connectivity – as indicated by inability to produce inter-frequency coupling and phase synchronization. These indices may have a role in predicting the long-term recovery of MCS patients.