In this paper, we describe the formation of Au nanoparticle-graphene oxide (Au-GO) and -reduced GO (Au-rGO) composites by noncovalent attachment of Au nanoparticles premodified with 2-mercaptopyridine to GO and rGO sheets, respectively, viaπ-π stacking and other molecular interactions. Compared with in situ reduction of HAuCl4 on the surface of graphene sheets that are widely used to prepare Au-GO composites, the approach developed by us offers well controlled size, size distribution, and morphology of the metal nanoparticles in the metal-GO nanohybrids. Moreover, we investigated surface enhanced Raman scattering (SERS) and catalysis properties of the Au-graphene composites. We have demonstrated that the Au-GO composites are superior SERS substrates to the Au NPs. Similarly, a comparative study on the catalytic activities of the Au, Au-GO, and Au-rGO composites in the reduction of o-nitroaniline to 1,2-benzenediamine by NaBH4 indicates that both Au-GO and Au-rGO composites exhibit significantly higher catalytic activities than the corresponding Au nanoparticles.

Stroke is one of the major causes of disability and mortality worldwide. It is well known that ischemic stroke can cause gray matter injury. However, stroke also elicits profound white matter injury, a risk factor for higher stroke incidence and poor neurological outcomes. The majority of damage caused by stroke is located in subcortical regions and, remarkably, white matter occupies nearly half of the average infarct volume. Indeed, white matter is exquisitely vulnerable to ischemia and is often injured more severely than gray matter. Clinical symptoms related to white matter injury include cognitive dysfunction, emotional disorders, sensorimotor impairments, as well as urinary incontinence and pain, all of which are closely associated with destruction and remodeling of white matter connectivity. White matter injury can be noninvasively detected by MRI, which provides a three-dimensional assessment of its morphology, metabolism, and function. There is an urgent need for novel white matter therapies, as currently available strategies are limited to preclinical animal studies. Optimal protection against ischemic stroke will need to encompass the fortification of both gray and white matter. In this review, we discuss white matter injury after ischemic stroke, focusing on clinical features and tools, such as imaging, manifestation, and potential treatments. We also briefly discuss the pathophysiology of WMI and future research directions.

Background: Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently a pandemic affecting over 200 countries. Many cities have established designated fever clinics to triage suspected COVID-19 patients from other patients with similar symptoms. However, given the limited availability of the nucleic acid test as well as long waiting time for both the test and radiographic examination, the quarantine or therapeutic decisions for a large number of mixed patients were often not made in time. We aimed to identify simple and quickly available laboratory biomarkers to facilitate effective triage at the fever clinics for sorting suspected COVID-19 patients from those with COVID-19-like symptoms. Methods: We collected clinical, etiological, and laboratory data of 989 patients who visited the Fever Clinic at Wuhan Union Hospital, Wuhan, China, from Jan 31 to Feb 21. Based on polymerase chain reaction (PCR) nucleic acid testing for SARS-CoV-2 infection, they were divided into two groups: SARS-CoV-2-positive patients as cases and SARS-CoV-2-negative patients as controls. We compared the clinical features and laboratory findings of the two groups, and analyzed the diagnostic performance of several laboratory parameters in predicting SARS-CoV-2 infection and made relevant comparisons to the China diagnosis guideline of having a normal or decreased number of leukocytes (9¢5 10 9 /L) or lymphopenia (<1¢1 10 9 /L). Findings: Normal or decreased number of leukocytes (9¢5 10 9 /L), lymphopenia (<1¢1 10 9 /L), eosinopenia (<0¢02 10 9 /L), and elevated hs-CRP (4 mg/L) were presented in 95¢0%, 52¢2%, 74¢7% and 86¢7% of COVID-19 patients, much higher than 87¢2%, 28¢8%, 31¢3% and 45¢2% of the controls, respectively. The eosinopenia produced a sensitivity of 74¢7% and specificity of 68¢7% for separating the two groups with the area under the curve (AUC) of 0¢717. The combination of eosinopenia and elevated hs-CRP yielded a sensitivity of 67¢9% and specificity of 78¢2% (AUC=0¢730). The addition of eosinopenia alone or the combination of eosinopenia and elevated hs-CRP into the guideline-recommended diagnostic parameters for COVID-19 improved the predictive capacity with higher than zero of both net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Interpretation: The combination of eosinopenia and elevated hs-CRP can effectively triage suspected COVID-19 patients from other patients attending the fever clinic with COVID-19-like initial symptoms. This finding would be particularly useful for designing triage strategies in an epidemic region having a large number of patients with COVID-19 and other respiratory diseases while limited medical resources for nucleic acid tests and radiographic examination.

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.