Mental health and conspirasism in health care professionals during the spring 2020 COVID-19 lockdown in Greece.

INTRODUCTION: The aim of the study was to investigate mental health and conspiracy theory beliefs concerning COVID-19 among health care professionals (HCPs). MATERIAL AND METHODS: During lockdown, an online questionnaire gathered data from 507 HCPs (432 females aged 33.86 ± 8.63 and 75 males aged 39.09 ± 9.54). STATISTICAL ANALYSIS: A post-stratification method to transform the study sample was used; descriptive statistics were calculated. RESULTS: Anxiety and probable depression were increased 1.5-2-fold and were higher in females and nurses. Previous history of depression was the main risk factor. The rates of believing in conspiracy theories concerning the COVID-19 were alarming with the majority of individuals (especially females) following some theory to at least some extend. CONCLUSIONS: The current paper reports high rates of depression, distress and suicidal thoughts in the HCPs during the lockdown, with a high prevalence of beliefs in conspiracy theories. Female gender and previous history of depression acted as risk factors, while the belief in conspiracy theories might act as a protective factor. The results should be considered with caution due to the nature of the data (online survey on a self-selected but stratified sample).

Self-reported changes in anxiety, depression and suicidality during the COVID-19 lockdown in Greece.

INTRODUCTION: There are only a few published empirical data on COVID-19's effects on the mental health. MATERIAL AND METHODS: During lockdown, an online questionnaire registered demographic, health data, previous psychiatric history, current anxiety, depression and suicidality, believing in conspiracy theories and other domains. Data from 3399 persons were used (81.08% females; aged 34.02 ± 9.72 and 18.27% males; aged 36.38±10.33). Distress and clinical depression were identified with the use of cut-off and a previously developed algorithm respectively. STATISTICAL ANALYSIS: A post-stratification method was used; descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. RESULTS: Clinical depression was present in 9.31% of the stratified sample, while 8.5% had severe distress; increased anxiety was present in more than 45%. Suicidal thoughts increased in 10.40% and decreased in 4.42%. Beliefs in conspiracy theories were widely prevalent; at least half of cases were following various misconceptions. A model for the development of depression was created with general health status, previous history of depression, self-harm and suicidal attempts, family responsibility, economic change, and age acting as risk factors, while keeping a daily routine, pursuing religiousness/spirituality, and believing in conspiracy theories acting as protective factors. CONCLUSIONS: The model developed here revealed multiple vulnerabilities and an interplay leading from simple anxiety to clinical depression and suicidality through distress. This could be of practical utility since most of these factors are modifiable. Future research, as well as interventions, should focus specifically on them.

A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images.

A computer-aided diagnostic system has been developed for the discrimination of normal, infectious and cancer prostate tissues based on texture analysis of transrectal ultrasound images. The proposed system has been designed using a panel of three classifiers, which have been evaluated individually or as a mutli-classifier scheme, using the external cross-validation procedure. Clinical data consisted of 165 transrectal ultrasound images, characterized by an experienced physician as normal (55/165), cancerous (55/165), and infectious (55/165) prostate cases. From each image, the physician delineated the most representative regions of interest, from which, 23 textural features were extracted. Classification was seen as a two level hierarchical decision tree. Normal from infectious and infectious from cancer cases were discriminated at the 1st and 2nd level of the decision tree, respectively. The best classification results for the 1st level were 89.5%, whereas for the 2nd level 90.1%. The utilization of multi-classifier system improved the discrimination of prostate pathologies as compared to individual classifiers; for infectious prostate cases improvement was from 87.3% to 88.7% and for cancer prostate cases improvement was from 84.1% to 91.4%. In terms of overall system performance (the decision tree's node propagating error taken into account), best classification accuracies were 89.5%, 79.6% and 82.7% for the recognition of normal, infectious and cancer cases, respectively. The proposed system might be used as a second opinion tool for assisting diagnosis of different prostate pathologies.