This study aimed to investigate the relationship between somatic symptom disorder (SSD) and sleep disorders, following three research questions: (1) How are these disorders correlated? (2) What are the comorbidities reported in these patients? and (3) What are the most effective pharmacological and non-pharmacological treatments for both conditions? PubMed, Scopus, OVID, Medline, and ProQuest databases were searched for relevant articles published between 1957–2020. Search terms included “somatic symptoms disorder”, “sleep disorders”, “insomnia”, “somatoform”, “somatization”, “therapeutic”, “psychotherapy”, and alternative, formerly used terms for SSD. Forty papers were finally included in the study. Prevalence of insomnia in SSD patients ranged between 20.4–48%, with this being strongly correlated to somatic symptoms and psychosocial disability. The most relevant comorbidities were generalized anxiety disorder, depression, fatigue, negative mood, substance use, orthorexia, alexithymia, anorexia, weight loss, poor eating habits, and acute stress disorder. Patients receiving antidepressant therapy reported significant improvements in insomnia and somatic symptoms. In terms of non-pharmacological interventions, cognitive-behavioral therapy (CBT) showed improvements in sleep outcomes, while the Specialized Treatment for Severe Bodily Distress Syndromes (STreSS) may represent an additional promising option. Future research could include other medical and psychosocial variables to complete the picture of the relationship between sleep disorders and somatic symptoms.

The objective of this study was to investigate online learning satisfaction in a sample of university students and its relationship with depression, anxiety, insomnia, and the average number of hours spent online. A total of 463 medical students were recruited for an online survey conducted from February to March 2022 with the main objective of estimating online learning satisfaction, while secondary outcomes involved assessing the relationship between online learning and depression, anxiety, insomnia, and the average number of hours spent online. A total of 285 participants were female (71.4%) and the mean age was 20.2 years. The results revealed that depression, anxiety, and insomnia are negatively correlated with overall satisfaction with e-learning. The more time students spent online, the greater the overall satisfaction. There are significant differences regarding student perceptions of interactivity in online learning satisfaction outcomes (p < 0.05, η² partial Eta Squared-0.284). The opportunity to learn via chat-box presented differences in overall satisfaction while pleasant aspects of online learning, such as “no travel” and “economy”, were related to satisfaction. The students revealed that the higher the psychopathology scores, the less satisfied they were with online learning, while a higher number of hours spent online contributed positively to satisfaction.

Background: Suicide is often said to kill more young people than cancer, but the size of the difference has rarely been quantified. Methods: we analysed registered deaths at ages 15 to 24 from the WHO Mortality Database, Eurostat for the European Union, and WHO Global Health Estimates as a modelled comparison, and conducted a PRISMA 2020 systematic review of MEDLINE via PubMed and Europe PMC, supplemented by OpenAlex and Crossref, to establish whether the quantity had already been reported. Ages 15 to 24 were used because no multi-country source publishes the 18 to 25 band. Results: in the 66 countries with low ill-defined-cause coding, 24,040 suicide deaths and 12,778 cancer deaths gave a ratio of 1.88 (95% CI 1.84 to 1.92); the ratio was 3.07 in high-income countries and 2.25 in males against 1.32 in females. On a balanced panel of 56 countries it widened by 43 per cent between 2005 and 2022, because registered cancer deaths fell while suicide deaths rose. Of 696 screened records plus a supplementary search, 23 studies were eligible and none of the 12 reports obtained states the ratio. Conclusions: where cause of death is relatively well coded, suicide accounts for close to twice as many deaths at ages 15 to 24 as cancer, and for about three times as many in high-income countries.

Background/Objectives: Suicide remains a major public health concern, with over 700,000 deaths annually worldwide. Current risk assessment is limited by the tendency of at-risk patients to deny suicidal ideation, with denial rates of approximately 50% among ideators and up to 78% among inpatient suicide decedents. Methods: This proof-of-concept study investigated whether deep learning applied to patient speech could distinguish psychiatric inpatients admitted following a recent suicide attempt from patients with severe depression without documented suicidal ideation. A multi-scale two-dimensional convolutional neural network with residual connections and spatial attention (Multi-Scale CNN) was trained on mel-spectrograms generated exclusively from patient speech extracted by automatic speaker diarization followed by manual verification. The dataset comprised recordings from 88 psychiatric inpatients (59 suicide attempters and 29 patients with severe depression without suicidal ideation). Model performance was evaluated using repeated patient-level stratified five-fold cross-validation, ensuring complete separation of participants between folds. Additional female-only, exploratory male-only, and repeated sex-matched analyses were performed to evaluate the potential influence of sex imbalance. Results: Repeated patient-level five-fold cross-validation yielded a mean classification accuracy of 72.7 ± 6.4% with a mean ROC-AUC of 0.824 ± 0.054, a sensitivity of 88.3 ± 9.5%, and a specificity of 41.3 ± 25.0%. Female-only analysis maintained good discriminative performance (ROC-AUC 0.866 ± 0.123), whereas repeated sex-matched analyses produced lower performance (ROC-AUC 0.621 ± 0.156), indicating that sex imbalance contributed to, but did not fully explain, the observed discrimination. The male-only analysis was considered exploratory because of the limited number of male patients in the depression group. Conclusions: These findings support the feasibility of voice-based patient-level analysis as a complementary objective approach for suicide risk assessment. Although the study remains exploratory because of the limited sample size and the absence of external validation, the revised validation strategy and sex-controlled analyses provide a substantially more robust estimate of model performance. Further multicenter prospective studies are required before clinical implementation.