Dynamic interpersonal therapy (DIT) is a brief, structured psychodynamic psychotherapy with demonstrated efficacy in treating major depressive disorder (MDD). The aim of the study was to determine whether DIT is an acceptable and efficacious treatment for MDD patients in China.

Patients were randomized to 16-week treatments with either DIT plus antidepressant medication (DIT + ADM; n = 66), general supportive therapy plus antidepressant medication (GST + ADM; n = 75) or antidepressant medication alone (ADM; n = 70). The Hamilton Depression Rating Scale (HAMD) administered by blind raters was the primary efficacy measure. Assessments were completed during the acute 16-week treatment and up to 12-month posttreatment.

The group × time interaction was significant for the primary outcome HAMD (F = 2.900, df1 = 10, df2 = 774.72, p = 0.001) in the acute treatment phase. Pairwise comparisons showed a benefit of DIT + ADM over ADM at weeks 12 [least-squares (LS) mean difference = −3.161, p = 0.007] and 16 (LS mean difference = −3.237, p = 0.004). Because of the unexpected high attrition during the posttreatment follow-up phase, analyses of follow-up data were considered exploratory. Differences between DIT + ADM and ADM remained significant at the 1-, 6-, and 12-month follow-up (ps range from 0.001 to 0.027). DIT + ADM had no advantage over GST + ADM during the acute treatment phase. However, at the 12-month follow-up, patients who received DIT remained less depressed.

Acute treatment with DIT or GST in combination with ADM was similarly efficacious in reducing depressive symptoms and yielded a better outcome than ADM alone. DIT may provide MDD patients with long-term benefits in symptom improvement but results must be viewed with caution.

Multi-criteria decision analysis (MCDA) is a useful tool in complex decision-making situations and has been used in medical fields to evaluate treatment options and drug selection. We aimed to provide valuable insights on the use of MCDA in health care through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain using a scientometric and bibliometric analysis.

Publications related to MCDA in health care were identified by searching the Web of Science Core Collection on 14 July 2021. Three bibliometric software programs (VOSviewer, Bibliometrix, and CiteSpace) were used to conduct the analysis.

A total of 410 publications were identified from 196 academic journals (average yearly growth rate of 32% from 1999 to 2021), with 23,637 co-cited references by 871 institutions from 70 countries or regions. The USA was the most productive country (n=80), while the Universiti Pendidikan Sultan Idris (n=16), Université de Montréal (n= 13), and Syreon Research Institute (n=12) were the most productive institutions. The biggest nodes in every cluster of author networks were Aos Alaa Zaidan, Mireille Goetghebeur, and Zoltan Kalo. The top journals in terms of number of articles (n=17) and citations (n=1,673) were Value in Health and the Journal of Medical Systems, respectively. The research hotspots mainly included the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. In the recent literature there was more emphasis on coronavirus disease 2019 (COVID-19) and fuzzy Technique for Order Preference by Similarities to Ideal Solution (TOPSIS). Big data, telemedicine, TOPSIS, and the fuzzy AHP, which are well-developed and important themes, may be the trends in future research.

This study provides a holistic picture of the MCDA-related literature published in health care. MCDA has a broad application in different topic areas and would be helpful for practitioners, researchers, and decision makers working in health care when faced with complex decisions. It can be argued that the door is still open for improving the role of MCDA in health care, both in its technologies and its application.

According to a WHO report, the number of patients with coronavirus disease 2019 (COVID-19) has reached 456,797,217 worldwide as of 15 March, 2022. In Wuhan, China, large teams of health-care personnel were dispatched to respond to the COVID-19 emergency. This study aimed to determine the sociodemographic and psychological predictors of resilience among frontline nurses fighting the current pandemic.

A total of 143 nurses were recruited from February 15 to February 20, 2020, to participate in this study. The 10-item Connor-Davidson Resilience Scale and the 21-item Depression Anxiety Stress Scale were used to estimate the participants’ resilience and mental wellbeing.

Results showed that the nurses displayed a moderate resilience level. Their median depression, anxiety, and stress scores were 1, 2, and 3, respectively, which were negatively correlated with resilience. Female gender, being dispatched to Wuhan, and depression levels were the significant predictors of resilience.

The results suggest that particular attention should be given to nurses who were dispatched to Wuhan and who exhibited depression symptoms, and appropriate measures should be taken to boost their resilience.

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Gut microbiome and dietary patterns have been suggested to be associated with depression/anxiety. However, limited effort has been made to explore the effects of possible interactions between diet and microbiome on the risks of depression and anxiety.

Using the latest genome-wide association studies findings in gut microbiome and dietary habits, polygenic risk scores (PRSs) analysis of gut microbiome and dietary habits was conducted in the UK Biobank cohort. Logistic/linear regression models were applied for evaluating the associations for gut microbiome-PRS, dietary habits-PRS, and their interactions with depression/anxiety status and Patient Health Questionnaire (PHQ-9)/Generalized Anxiety Disorder-7 (GAD-7) score by R software.

We observed 51 common diet–gut microbiome interactions shared by both PHQ score and depression status, such as overall beef intake × genus Sporobacter [hurdle binary (HB)] (PPHQ = 7.88 × 10−4, Pdepression status = 5.86 × 10−4); carbohydrate × genus Lactococcus (HB) (PPHQ = 0.0295, Pdepression status = 0.0150). We detected 41 common diet–gut microbiome interactions shared by GAD score and anxiety status, such as sugar × genus Parasutterella (rank normal transformed) (PGAD = 5.15 × 10−3, Panxiety status = 0.0347); tablespoons of raw vegetables per day × family Coriobacteriaceae (HB) (PGAD = 6.02 × 10−4, Panxiety status = 0.0345). Some common significant interactions shared by depression and anxiety were identified, such as overall beef intake × genus Sporobacter (HB).

Our study results expanded our understanding of how to comprehensively consider the relationships for dietary habits–gut microbiome interactions with depression and anxiety.

To compare the prevalence of overweight or obesity (ow/ob) with WHO BMI cut-off points, International Obesity Task Force (IOTF) cut-off points and Chinese BMI criteria and examine its potential factors among preschool children in Hunan Province.

A cross-sectional survey including anthropometric measurements and questionnaires about children’s information, caregivers’ socio-demographic characteristics and maternal characteristics. χ2 tests and univariate and multivariate binary logistic regression were performed to evaluate the possible factors of ow/ob.

Hunan, China, from September to October 2019.

In total, 7664 children 2 to 6 years of age.

According to Chinese BMI criteria, about 1 in 7–8 children aged 2–6 years had ow/ob in Hunan, China. The overall estimated prevalence of ow/ob among 2- to 6-year-old children was significantly higher when based on the Chinese BMI criteria compared with the WHO BMI cut-off points and IOTF cut-off points. According to Chinese BMI criteria, ow/ob was associated with residing in urban areas, older age, male sex, eating snacking food more frequently, macrosomia delivery, caesarean birth, heavier maternal prepregnancy weight and pre-delivery weight.

The prevalence of ow/ob in preschool children in Hunan Province remains high. More ow/ob children could be screened out according to Chinese BMI cut-offs compared with WHO and IOTF BMI criteria. In the future, targeted intervention studies with matched controls will be needed to assess the long-term effects of intervention measures to provide more information for childhood obesity prevention and treatment.

Depressive symptoms and cognitive impairment often coexisted in the elderly. This study investigates the effect of late-life depressive symptoms on risk of mild cognitive impairment (MCI).

A total of 14,231 dementia- and MCI free participants aged 60+ from the Survey of Health, Ageing, and Retirement in Europe were followed-up for 10 years to detect incident MCI. MCI was defined as 1.5 standard deviation (SD) below the mean of the standardized global cognition score. Depressive symptoms were assessed by a 12-item Europe-depression scale (EURO-D). Severity of depressive symptoms was grouped as: no/minimal (score 0–3), moderate (score 4–5), and severe (score 6–12). Significant depressive symptoms (SDSs) were defined as EURO-D score ≥ 4.

During an average of 8.2 (SD = 2.4)-year follow-up, 1,352 (9.50%) incident MCI cases were identified. SDSs were related to higher MCI risk (hazard ratio [HR] = 1.26, 95% confidence intervals [CI]: 1.10–1.44) in total population, individuals aged 70+ (HR = 1.35, 95% CI: 1.14–1.61) and women (HR = 1.28, 95% CI: 1.08–1.51) in Cox proportional hazard model adjusting for confounders. In addition, there was a dose–response association between the severity of depressive symptoms and MCI incidence in total population, people aged ≥70 years and women (p-trend <0.001).

Significant depressive symptoms were associated with higher incidence of MCI in a dose–response fashion, especially among people aged 70+ years and women. Treating depressive symptoms targeting older population and women may be effective in preventing MCI.

This study aimed to explore Chinese people’s attitudes to the official application of TCM in coronavirus disease 2019 (COVID-19) treatment.

We collected data referring to TCM on Weibo from 0:00 on January 24, 2020, to 23:59:59 on March 31, 2020 (Beijing time). In addition, this study used DLUT-Emotion ontology to analyze the sentiment orientation and emotions of selected data and then conducted a text analysis.

According to DLUT-Emotion ontology, we examined 3 sentiment orientations of 215,565 valid Weibo posts. Among them, 25,025 posts were judged as positive emotions, accounting for approximately 12%; 22,362 were regarded as negative emotions, accounting for approximately 10%; and 168,178 were judged as neutral emotions, accounting for approximately 78%. Results indicate that the words judged as “Good” have the highest frequency, and words marked as “Happy” have increased over time. The word frequency of “Fear” and “Sadness” showed a significant downward trend.

Weibo users have a relatively positive attitude to the TCM in the COVID-19 treatment in general. Results of text analysis show that data with negative emotions is essentially an expression of public opinions to supporting TCM or not. Texts of “Fear” and “Sadness” do not reflect users’ negative attitudes to TCM.

During the coronavirus disease 2019 (COVID-19) pandemic, Fangcang shelter hospitals were opened in Wuhan, China, to isolate and care for patients with mild or moderate symptoms. The patients and staff in the hospitals faced mental health challenges. This paper reports the experiences and mental health needs from them.

Following the qualitative design, semi-structured interviews were conducted in the EastWest Lake Fangcang Shelter Hospital, Wuhan on March 2020. Data collection and analysis was based on grounded theory. Open coding was adapted and a structured codebook was developed through coding seminars. The themes and subthemes were then confirmed through thematic analysis. The findings were further explained and integrated in a theoretical framework.

A total of 10 COVID-19 patients and 13 staff, including doctors, nurses, psychiatrists, and policemen participated in the interviews. They have common needs, as well as their own needs. The perspectives from the staff also did complement for needs of the patients. The mental health needs were generalized into four themes, that is, basic needs, information and communication, emotional needs, and social support, each with several subthemes. In addition, there were some external factors that regulated the internal needs, which were summarized in a theoretical framework.

The study indicates the directions on hospital management, mental health services, policy making, and social work to meet the mental health needs of the inpatients and staff from temporary shelter hospitals like Fangcang in Wuhan during the COVID-19 pandemic.