Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.

To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.

Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.

The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.

These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.

Human adenovirus type 55 (HAdV-55) has recently caused multiple outbreaks. This study examined polymorphisms in CD46 to determine their involvement in HAdV-55 infection.

A total of 214 study subjects infected with HAdV-55 were included in our study. The study subjects were divided into those with silent infections (n=91), minor infections (n=85), and severe infections (n=38). Ten single nucleotide polymorphisms (SNPs) from CD46 were examined.

Compared with the AA genotype, the TT genotype at rs2724385 (CD46, A/T) was associated with a protective effect against disease occurrence, with an odds ratio (95% confidence interval) of 0.20 (0.04-0.97) (P=0.038). There were no significant differences between the patients with minor and severe infection and those who had silent HAdV-55 infection in the other CD46 SNPs. We next compared the polymorphisms of these genes according to disease severity in HAdV-55-infected patients with clinical symptoms. The results showed that there were no significant differences between minor infections and severe infections.

Our results suggested that the CD46 SNP at rs2724385 is associated with the occurrence of disease in HAdV-55-infected patients. A much larger number of samples is required to understand the role of CD46 polymorphisms in the occurrence and progression of infection by HAdV-55. (Disaster Med Public Health Preparedness. 2018;12:427–430)

To retrospectively analyze the rescue and treatment of pediatric patients by the Chinese Red Cross medical team during the Nepal earthquake relief.

The medical team set up a field hospital; the pediatric clinic consisted of 1 pediatrician and several nurses. Children younger than 18 years old were placed in the pediatric clinic for injury examination and treatment.

During the 7-day period of medical assistance (the second to third week after the earthquake), a total of 108 pediatric patients were diagnosed and treated, accounting for 2.8% of the total patients. The earthquake-related injuries mainly required surgical dressing and debridement. No severe limb fractures or traumatic brain injuries were found. Infection of the respiratory tract, the gastrointestinal tract, and the skin were the most common ailments, accounting for 42.3%, 18.5%, and 16.7%, respectively, of the total treated patients.

Two to 3 weeks after the earthquake, the admitted pediatric patients mainly displayed respiratory and gastrointestinal infections. When developing a rescue plan and arranging medical resources, we should consider the necessity of treating non–disaster-related conditions. (Disaster Med Public Health Preparedness. 2016;page 1 of 4)

The objective of this study was to build a database to collect infectious disease information at the scene of a disaster through the use of 128 epidemiological questionnaires and 47 types of options, with rapid acquisition of information regarding infectious disease and rapid questionnaire customization at the scene of disaster relief by use of a personal digital assistant (PDA).

SQL Server 2005 (Microsoft Corp, Redmond, WA) was used to create the option database for the infectious disease investigation, to develop a client application for the PDA, and to deploy the application on the server side. The users accessed the server for data collection and questionnaire customization with the PDA.

A database with a set of comprehensive options was created and an application system was developed for the Android operating system (Google Inc, Mountain View, CA). On this basis, an infectious disease information collection system was built for use at the scene of disaster relief. The creation of an infectious disease information collection system and rapid questionnaire customization through the use of a PDA was achieved.

This system integrated computer technology and mobile communication technology to develop an infectious disease information collection system and to allow for rapid questionnaire customization at the scene of disaster relief. (Disaster Med Public Health Preparedness. 2017;11:668–673)

To determine dynamic changes in clinical characteristics by examining an outbreak of adenovirus infection that occurred from December 20, 2012, to February 25, 2013, in Tianjin, China.

Active surveillance for febrile respiratory illnesses was conducted, and medical records of patients were collected. Real-time quantitative polymerase chain reaction and sequencing were used for pathogen identification and viral genome study, respectively. Student’s t-test was used to compare the mean values of normally distributed continuous variables. Mann-Whitney U or Kruskal-Wallis tests were used if continuous variables were not normally distributed. Pearson’s chi-square test or Fisher’s exact test was used to compare categorical variables.

The outbreak was sourced from the index case diagnosed as the common cold on December 20, 2012; a total of 856 cases were reported in the following 66 days. The pathogen was identified as human adenovirus (HAdV) 55. The symptoms manifested differently in severe and mild cases. Routine blood examinations, liver function indexes, and heart function indexes showed different dynamic patterns over time in hospitalized patients.

Clinical characteristics and laboratory examinations may reveal unique patterns over the course of HAdV-55 infection. (Disaster Med Public Health Preparedness. 2018;12:464–469)

Early identification of patients with bipolar disorder during their first depressive episode is beneficial to the outcome of the disorder and treatment, but traditionally this has been a great challenge to clinicians. Recently, brain-derived neurotrophic factor (BDNF) has been suggested to be involved in the pathophysiology of bipolar disorder and major depressive disorder (MDD), but it is not clear whether BDNF levels can be used to predict bipolar disorder among patients in their first major depressive episode.

To explore whether BDNF levels can differentiate between MDD and bipolar disorder in the first depressive episode.

A total of 203 patients with a first major depressive episode as well as 167 healthy controls were recruited. After 3 years of bi-annual follow-up, 164 patients with a major depressive episode completed the study, and of these, 21 were identified as having bipolar disorder and 143 patients were diagnosed as having MDD. BDNF gene expression and plasma levels at baseline were compared among the bipolar disorder, MDD and healthy control groups. Logistic regression and decision tree methods were applied to determine the best model for predicting bipolar disorder at the first depressive episode.

At baseline, patients in the bipolar disorder and MDD groups showed lower BDNF mRNA levels (P<0.001 and P = 0.02 respectively) and plasma levels (P = 0.002 and P = 0.01 respectively) compared with healthy controls. Similarly, BDNF levels in the bipolar disorder group were lower than those in the MDD group. These results showed that the best model for predicting bipolar disorder during a first depressive episode was a combination of BDNF mRNA levels with plasma BDNF levels (receiver operating characteristics (ROC) = 0.80, logistic regression; ROC = 0.84, decision tree).

Our findings suggest that BDNF levels may serve as a potential differential diagnostic biomarker for bipolar disorder in a patient's first depressive episode.