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BACKGROUND: Childhood maltreatment (CM) is linked to long-term adverse health outcomes, including accelerated biological aging and cognitive decline. This study investigates the relationship between CM and various aging biomarkers: telomere length, facial aging, intrinsic epigenetic age acceleration (IEAA), GrimAge, HannumAge, PhenoAge, frailty index, and cognitive performance. METHODS: We conducted a Mendelian randomization (MR) study using published GWAS summary statistics. Aging biomarkers included telomere length (qPCR), facial aging (subjective evaluation), and epigenetic age markers (HannumAge, IEAA, GrimAge, PhenoAge). The frailty index was calculated from clinical assessments, and cognitive performance was evaluated with standardized tests. Analyses included Inverse-Variance Weighted (IVW), MR Egger, and Weighted Median (WM) methods, adjusted for multiple comparisons. RESULTS: CM was significantly associated with shorter telomere length (IVW: ß = - 0.1, 95% CI - 0.18 to - 0.02, pFDR = 0.032) and increased HannumAge (IVW: ß = 1.33, 95% CI 0.36 to 2.3, pFDR = 0.028), GrimAge (IVW: ß = 1.19, 95% CI 0.19 to 2.2, pFDR = 0.040), and PhenoAge (IVW: ß = 1.4, 95% CI 0.12 to 2.68, pFDR = 0.053). A significant association was also found with the frailty index (IVW: ß = 0.31, 95% CI 0.13 to 0.49, pFDR = 0.006). No significant associations were found with facial aging, IEAA, or cognitive performance. CONCLUSIONS: CM is linked to accelerated biological aging, shown by shorter telomere length and increased epigenetic aging markers. CM was also associated with increased frailty, highlighting the need for early interventions to mitigate long-term effects. Further research should explore mechanisms and prevention strategies.

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Introduction: Adverse life events constitute primary risk factors for major depressive disorder (MDD), influencing brain function and structure. Adolescents, with their brains undergoing continuous development, are particularly susceptible to enduring impacts of adverse events. Methods: We investigated differences and correlations among childhood trauma, negative life events, and alterations of brain function in adolescents with first-episode MDD. The study included 23 patients with MDD and 19 healthy controls, aged 10-19 years. All participants underwent resting-state functional magnetic resonance imaging and were assessed using the beck depression inventory, childhood trauma questionnaire, and adolescent self-rating life events checklist. Results: Compared with healthy controls, participants with first-episode MDD were more likely to have experienced emotional abuse, physical neglect, interpersonal relationship problems, and learning stress (all p' < 0.05). These adverse life events were significantly correlated with alterations in brain functions (all p < 0.05). Discussion: This study contributes novel evidence on the underlying process between adverse life events, brain function, and depression, emphasizing the significant neurophysiological impact of environmental factors.

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Inflammatory mechanisms may play crucial roles in the pathophysiology of major depressive disorder (MDD), and cytokine concentrations are correlated with brain alterations. Adolescents and young adults with MDD have higher recurrence and suicide rates than adults, but there has been limited research on the underlying mechanisms. In this study, we aimed to investigate the potential correlations among cytokines, depression severity, and the volumes of the amygdala, hippocampus, and nucleus accumbens in Han Chinese adolescents and young adults with first-episode MDD. Nineteen patients with MDD aged 10-21 years were enrolled from the Psychiatry Department of the First Affiliated Hospital of Chongqing Medical University, along with 18 age-matched healthy controls from a local school. We measured the concentrations of interleukin (IL)-4, IL-6, IL-8, and IL-10 in the peripheral blood, along with the volumes of the amygdala, hippocampus, and nucleus accumbens, as determined by magnetic resonance imaging. We observed that patients with MDD had higher concentrations of IL-6 and a trend towards reduced left amygdala and bilateral hippocampus volumes than healthy controls. Additionally, the concentration of IL-6 was correlated with the left amygdala volume and depression severity, while the left hippocampus volume was correlated with depression severity. This study suggests that inflammation is an underlying neurobiological change and implies that IL-6 could serve as a potential biomarker for identifying early stage MDD in adolescents and young adults.

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Background: Recurrence remains the primary cause of death in patients with breast cancer. Although machine learning can efficiently predict the prognosis of breast cancer patients, the black-box nature of the model may result in a lack of evidence for clinicians when making critical decisions. Methods: In this study, our main objective was twofold: (1) to develop a clinical decision support tool for predicting the prognosis of breast cancer and (2) to identify and explore the key factors that influence breast cancer recurrence. To achieve this, we employed an explainable ensemble learning method called Shapley additive explanation (SHAP), which leverages cooperative game theory. Using real-world data from 1629 breast cancer patients, we analyzed and uncovered the key factors associated with breast cancer recurrence. Subsequently, we used these identified factors to create a recurrence prediction model and establish a decision mechanism for the tool. The proposed method not only provides accurate recurrence predictions but also offers transparent explanations for these predictions. Results: By utilizing four key factors, namely, tumor size, clinical stage III, number of lymph node metastases, and age, our decision support tool for predicting breast cancer recurrence achieved significant improvements. The extra-tree model exhibited an increased area under the receiver operating characteristic curve (AUC) of 0.97, while the Random Forest model demonstrated an improved AUC of 0.96. We also offer a decision mechanism for a recurrence prediction model based on the identified key factors. This transparent and interpretable decision-making process facilitated by our explainable ensemble learning model enhances trust and promotes its applicability in clinical settings. Conclusions: The proposed explainable ensemble learning method shows promising results in predicting breast cancer recurrence, outperforming existing methods with high accuracy and transparency. This advancement has the potential to significantly improve clinical decision-making and patient outcomes in breast cancer treatment.

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BACKGROUND: Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. METHODS: We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. RESULTS: We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. CONCLUSIONS: Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

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