NEW INFORMATION: A new species of Ibana is described and illustrated, representing the first record of the genus for China. Ibanagan sp. n. differs from its congener by the yellowishbrown longitudinal band on the abdomen and the round, contiguous spermathecae. The distribution of the new species in Jiangxi Province is mapped.
AIM: To investigate the utility of a Traffic Light Control (TLC) system as a measurement/assessment of self-perceived eczema control.
METHODS: This is a prospectively study of all Chinese children (aged 6 to 18 years old) with eczema attending the paediatric dermatology clinic of a tertiary hospital from Jan to June 2020. Eczema control, eczema severity, quality of life and biophysical skin condition of consecutive patients at the paediatric dermatology clinic of a teaching hospital were evaluated with the validated Chinese versions of Depressive, Anxiety, Stress Scales (DASS-21), Patient Oriented Eczema Measure (POEM), transepidermal water loss (TEWL), and stratum corneum skin hydration (SH), respectively. With a visual TLC analogy, patients were asked if their eczema is under control (green light), worsening (yellow) or in flare-up (red light).
RESULTS: Among AE patients (n = 36), self-perceived TLC as green (under control), amber (worsening) and red (flare up) reflected acute and chronic severity (SCORAD, NESS, POEM) and quality of life (CDLQI) (p< 0.0001), but not SH, TEWL or Depression, anxiety and stress.
CONCLUSIONS: Eczema control can be semi-quantified with a child-friendly TLC self-assessment system. AE patients reporting worse eczema control have worse acute and chronic eczema severity, more impairment of quality of life; but not the psychologic symptoms of depression, anxiety and stress or skin hydration or transepidermal water loss. TLC can be linked to an eczema action plan to guide patient management.
METHODS AND ANALYSIS: Initially, during Phase I of the study, the serum level of IL-1β, IL-6 and TNF-α; ERP changes in the EEG and fecal microbiota content will be compared between 120 AUD patients and 120 healthy controls. Subsequently in Phase II of the study, 120 AUD patients will be randomized by stratified permuted block randomization into the probiotic, ACT and placebo groups in a 1:1:1 ratio. Participants in the probiotic and placebo groups will be administered one sachet per day of Lactobacillus spp. probiotic and placebo, respectively for 12 weeks. While those in the ACT group will receive one session per week of ACT for 8 weeks. Outcome measures will be administered at four timepoints, such as t0 = baseline assessment prior to intervention, t1 = 8 weeks after intervention began, t2 = 12 weeks after intervention and t3 = 24 weeks after intervention. Primary outcomes are the degrees of alcohol craving, alcohol withdrawal during abstinence and AUD. Secondary outcomes to be assessed are the severity of co-morbid depression and anxiety symptoms; the serum levels of IL-1β, IL-6 and TNF-α; changes in ERP and fecal microbiota content.
TRIAL REGISTRATION NUMBER: NCT05830708 (ClinicalTrials.gov). Registered on April 25, 2023.
MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.
RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.
CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.