Qingdao Municipal Hospital

Childhood trauma (CT) is a critical risk for major depressive disorder (MDD), yet the cognitive mechanisms linking CT to adolescent depression remain poorly understood. While alterations in attentional processing are core features of MDD, it remains unclear whether attentional dysfunctions, particularly in the domain of social cognition, mediate the link between CT and adolescent depression.

This study employed a dual-method approach: (1) categorical determination of attentional alterations in adolescent with MDD, and (2) dimensional examination of the mediating effect of attentional alterations in CT-depression pathways.

122 adolescents including 72 first-episode treatment-naïve MDD and 50 healthy controls were recruited in this study.

Attentional processing was assessed using an anti-saccade paradigm with pro-saccade (bottom-up allocation) and anti-saccade (top-down control) conditions across non-social shapes and social-emotional faces. CT exposure and depressive symptoms were measured via Childhood Trauma Questionnaire (CTQ) and Self-rating Depression Scale (SDS), respectively.

Compared to healthy controls, adolescents with MDD exhibited lower accuracy, longer response latency, and a novel trial sequential analysis revealed more errors in switch trials and slower post-error responses during the anti-saccade task. Only top-down attentional control, not bottom-up allocation, specifically mediated the association between CT and depression.

Adolescents with MDD showed reduced cognitive flexibility and impaired post-error adjustment, particularly in tasks requiring top-down attentional control in dynamic or emotionally changing context. Top-down attentional dysfunction constitutes a specific mechanistic pathway linking CT to depression in adolescence, highlighting executive attention as a critical early intervention target for trauma-related depression.

Diagnosis of autism spectrum disorder (ASD) primarily relies on subjective behavioral assessments, posing risks of delayed and missed diagnoses. The combination of functional near-infrared spectroscopy (fNIRS) technology and machine learning (ML) offers a novel approach for objective ASD identification; however, its overall diagnostic efficacy and key influencing factors require systematic evaluation.

Following the PRISMA guidelines, we systematically searched PubMed, EMBASE, Web of Science, The Cochrane Library, and Wiley Online Library databases to comprehensively collect English-language literature on fNIRS-based machine learning techniques for ASD diagnosis published from the inception of our database to December 2025. Data were extracted to calculate pooled sensitivity (sen), specificity (spe), positive likelihood ratio (LR + ), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and their 95% confidence intervals (CI). Pooled receiver operating characteristic (ROC) curves were plotted, and area under the curve (AUC) was calculated to assess diagnostic value. The I² test assessed study heterogeneity, which was explored through meta-regression and subgroup analyses. Publication bias was evaluated using Deeks funnel plot asymmetry tests. The review protocol was registered in PROSPERO (CRD420251250866).

A total of 17 studies were included in the systematic review, with 15 studies included in the meta-analysis. The pooled analysis showed that the sensitivity of fNIRS-based ML techniques for diagnosing ASD was 0.92 (95% CI 0.87-0.95),with a specificity of 0.94 (95% CI 0.90-0.97) and a composite area under the receiver operating characteristic curve (AUC) of 0.98 (95% CI 0.96-0.99). Based on these findings, fNIRS-based ML techniques demonstrate good diagnostic value for ASD.

fNIRS-based ML techniques demonstrate excellent diagnostic accuracy for ASD, presenting a highly promising objective diagnostic tool. Future research should focus on establishing standardized, multicenter datasets and analytical workflows, while exploring multimodal data fusion and model interpretability to advance this technology toward stable, reliable clinical diagnostic support.

As a leading gynecological malignancy worldwide, cervical cancer represents a major threat to female health. Further research is essential to elucidate its pathogenic mechanisms and develop effective therapeutic interventions. The objective is to examine the correlation between miR-524-5p and both the diagnostic and prognostic aspects of cervical cancer, while also elucidating its underlying mechanisms. Quantitative detection of miR-524-5p and LRP6 levels were performed via RT-qPCR, ELISA and Western blot. The diagnostic and prognostic potential of serum miR-524-5p in cervical cancer was assessed using ROC, Kaplan-Meier curves, and multivariate Cox regression. Bioinformatic analysis coupled with dual-luciferase reporter assays validated the direct targeting of LRP6 by miR-524-5p. The CCK-8 and Transwell assays were employed to assess the biological behavior of cervical cancer cells. Serum miR-524-5p was downregulated in cervical cancer compared to normal controls, and it had higher diagnostic value and worse survival outcome. LRP6 was identified as a direct target of miR-524-5p and demonstrated negative regulation by it. The upregulation of miR-524-5p was found to exert a marked inhibitory effect on the proliferative capacity of CaSki and HeLa cells, as well as their migratory and invasive potential. Additionally, the Wnt/β-catenin pathway was inhibited. However, co-transfection of miR-524-5p and LRP6 overexpression partially reversed the repressive effect of miR-524-5p mimic on cell biological functions. miR-524-5p demonstrates predictive potential for early detection and prognosis in cervical cancer. Functionally, it affects the Wnt/β-catenin pathway by directly targeting LRP6, thereby inhibiting malignant cell behavior.