Background: The frequent movement of population between countries brings an increasing number of travel-related infections. This study aims to define the spectrum and dynamics of imported infections observed from international travel in the Chinese mainland. Methods: Sick travellers were screened by inbound sentinel surveillance and post-travel clinic visits from 2014 to 18. The infections were classified as respiratory, gastrointestinal, vector-borne, blood/sexually transmitted and mucocutaneous. The analysed variables included the place of origin of the travellers (Chinese or foreign) and the time when travel-related infection was present (at the time of return, during travel and post-travel visits to the clinic). Results: In total, 58 677 cases were identified amongst 1 409 265 253 travellers, with an incidence of 41.64/million, comprising during-travel incidence of 27.44/million and a post-travel incidence of 14.20/million. Respiratory infections constituted the highest proportion of illnesses during travel (81.19%, 31 393 of 38 667), which mainly came from Asian countries and tourists; with influenza virus and rhinovirus infections being mainly diagnosed. Vector-borne diseases constituted the highest proportion of post-travel illnesses (98.14%, 19 638 of 20 010), which were mainly diagnosed from African countries and labourers; with malaria and dengue fever being mainly diagnosed. The differential infection spectrum varied in terms of the traveller’s demography, travel destination and travel purpose. As such, a higher proportion of foreign travellers had blood/sexually transmitted diseases (89.85%, 2832 of 3152), while Chinese citizens had a higher prevalence of vector-borne diseases (85.98%, 19 247 of 22 387) and gastrointestinal diseases (79.36%, 1115 of 1405). The highest incidence rate was observed amongst travellers arriving from Africa, while the lowest was observed amongst travellers arriving from Europe. Conclusions: The findings might help in preparing recommendations for travellers and also aid in primary care or other clinics that prepare travellers before trips abroad. The findings will also help to identify locations and the associated types of infections that might require attention.

Objective: To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.Methods: The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.Results: The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).Conclusion: Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.

Background: Obesity is a prevalent health problem in patients with schizophrenia, and calorie restriction diet (CRD) achieved effective weight loss and metabolic improvement; however, these have not been rigorously evaluated in obese patients with schizophrenia.Objective: To measure the effects of CRD on weight loss and metabolic status in hospitalized obese women with schizophrenia during a 4-week period.Methods: Participants were randomly assigned to two groups in a 1:1 ratio. The intervention group (n = 47) was asked to follow a CRD and the control group (n = 48) a normal diet for 4 weeks. Outcomes of body weight, body composition, as well as metabolic parameters were measured at baseline and following the intervention period.Results: Forty-five participants completed the 4-week research in both the intervention and control groups. Compared to the normal diet, adherence to the CRD significantly decreased body weight (2.38 ± 1.30 kg), body mass index (0.94 ± 0.52 kg/m²), waist circumference (4.34 ± 2.75 cm), hip circumference (3.37 ± 2.36 cm), mid-upper circumferences, triceps skin-fold thickness, fat mass and free fat mass with large effect sizes (p = 0.05).Conclusion: CRD is preventative of weight gain, but not apparent in intervention for metabolic status in hospitalized obese women with schizophrenia.Clinical trial registration: http://www.chictr.org.cn, ChiCTR-INR-16009185.

Background: Posttraumatic stress disorder (PTSD) is associated with the development of dementia. However, the link between PTSD and preclinical Alzheimer’s disease pathology (amyloid β [Aβ]) remains controversial. Moreover, the correlation between the severity of PTSD with Aβ levels remains unknown.Methods: This cross-sectional study sought to investigate the associations of PTSD symptoms with global and regional brain Aβ burden. To this end, data were obtained from participants in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) Study. In addition, we explored the association between the severity of PTSD symptoms and Aβ levels.Results: A total of 4,228 participants aged 65 to 85 years were included in the final analysis. The results showed that PTSD symptoms were significantly associated with higher global Aβ levels (1.15 ± 0.20 vs. 1.09 ± 0.19; β = 0.056; p < 0.001), after adjusting for covariates. The association between PTSD symptoms and Aβ levels was not affected by sex, age, ApoE genotype, or psychiatric diseases. Similarly, PTSD symptoms were significantly associated with Aβ levels in all subregions, including the anterior cingulate, posterior cingulate, parietal cortex, precuneus, temporal cortex, and frontal cortex. In addition, the group with severe PTSD symptoms (1.22 ± 0.24) exhibited higher global Aβ levels than the groups with moderate (1.14 ± 0.19) or mild (1.12 ± 0.20) symptoms or the control (1.08 ± 0.18), with p < 0.001.Conclusion: The findings imply a close relationship between PTSD and brain Aβ levels, irrespective of sex, age, ApoE genotype, or psychiatric diseases. More well-designed studies are needed to further explore the relationship and mechanism underlying the association between PTSD and Aβ burden.