Qing He

Analysis of the Development Trajectory and Influencing Factors of Depression in Patients With Cervical Cancer During Concurrent Chemoradiotherapy

Background: This study aims to analyse the developmental trajectory of depression in patients undergoing concurrent chemoradiotherapy (CCRT) for cervical cancer and its influencing factors. Methods: A retrospective analysis of clinical data was performed on 160 patients with cervical cancer who received CCRT at our hospital between July 2023 and June 2025. Individuals with depression were assigned to the depressed group, whereas those without depression were assigned to the non-depressed group. Employing latent class growth modelling to identify depression trajectories in cervical cancer patients undergoing CCRT. The factors influencing the latent classes of depression trajectories in patients were analysed through logistic regression. Results: The depressed group had higher rates of household monthly income per capita of less than 5000 RMB (1 USD = 7.1 RMB), stage III/IV tumour stage and avoidance/submission coping methods than the non depressed group (p = 0.001, 0.030, < 0.001) and had lower Multidimensional Scale of Perceived Social Support (MSPSS) scores (p = 0.001). Three distinct depression trajectories were identified: a low-level stable group (n = 31), a moderate-level increasing group (n = 54) and a high-level decreasing group (n = 29). The logistic regression analysis results indicated that patients with a household income per capita below 5000 RMB, stage III/IV tumour stage, avoidance/submission coping style and lower MSPSS scores exhibited a higher likelihood of entering the medium-level rising group and the high-level declining group compared to the other group (p < 0.05). Conclusions: Depression in patients with cervical cancer exhibits three distinct developmental trajectories. Household income per capita, tumour stage, coping style and MSPSS score may influence these trajectories. Thus, prompt intervention targeting these potential influencing factors is essential for managing the progression of depression.

Integration of label-free surface enhanced Raman spectroscopy (SERS) of extracellular vesicles (EVs) with Raman tagged labels to enhance ovarian cancer diagnostics

We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellular vesicles (EVs). EVs were isolated from patient plasma using size-exclusion chromatography and labeled with polyyne-based Raman tags targeting three ovarian cancer biomarkers: CA-125, HE4, and CA-19-9. Labeled and unlabeled EVs were deposited onto SERS-active substrates, and spectra were collected using a custom confocal Raman microscope. Incorporating the tag-derived signal into SERS analysis enhanced interpretability and added molecular specificity. We evaluated classification performance using various ML models applied to spectral datasets from a cohort of ovarian cancer patients and healthy controls. Combined use of the Raman tag and label-free regions improved classification accuracy compared to either modality alone. Notably, support vector machine (SVM) achieved over 95 % accuracy, sensitivity, and specificity. Compared to ELISA, our SERS platform demonstrated improved sensitivity in detecting EV-associated biomarkers from small sample volumes. This approach addresses a key limitation of SERS-based diagnostics by linking spectral features to known biomarkers, offering improved transparency and performance in ML-enabled liquid biopsy.