Early-stage diabetic retinopathy (DR) presents challenges in clinical diagnosis due to inconspicuous and minute microaneurysms (MAs), resulting in limited research in this area. Additionally, the potential of emerging foundation models, such as the segment anything model (SAM), in medical scenarios remains rarely explored. In this work, we propose a human-in-the-loop, label-free early DR diagnosis framework called GlanceSeg, based on SAM. GlanceSeg enables real-time segmentation of MA lesions as ophthalmologists review fundus images. Our human-in-the-loop framework integrates the ophthalmologist's gaze maps, allowing for rough localization of minute lesions in fundus images. Subsequently, a saliency map is generated based on the located region of interest, which provides prompt points to assist the foundation model in efficiently segmenting MAs. Finally, a domain knowledge filtering (DKF) module refines the segmentation of minute lesions. We conducted experiments on two newly-built public datasets, i.e., IDRiD and Retinal-Lesions, and validated the feasibility and superiority of GlanceSeg through visualized illustrations and quantitative measures. Additionally, we demonstrated that GlanceSeg improves annotation efficiency for clinicians and further enhances segmentation performance through fine-tuning using annotations. The clinician-friendly GlanceSeg is able to segment small lesions in real-time, showing potential for clinical applications.

Background: Individuals vary in aggression traits. Here, we investigated how individual aggression is associated with altered reward and punishment processing in the brain and the neural bases of sex differences. Methods: Aggression was assessed by the Achenbach Adult Self-Report. We curated the Human Connectome Project dataset and modeled the BOLD signals to identify regional responses to reward and punishment blocks in the gambling task (n = 981, 473 men) as well as responses to the identification of negative emotional face in a target-matching task (n = 885, 436 men). Whole-brain statistical significance was assessed at the voxel-level p < 0.001 (uncorrected) with cluster-level family-wise error (FWE) correction at p < 0.05. For regional responses identified in men or women alone, we conducted slope tests to examine the sex differences in their relationships with individual aggression. Results: Whole-brain regression showed higher activation of the left superior temporal gyrus (STG) during gambling loss in men. We extracted the left STG response to reward versus baseline (STG-win β) and to loss versus baseline (STG-loss β), where β is the GLM contrast estimate. Left STG-loss β was significantly correlated with aggression score in men but not in women, and a slope test confirmed the sex difference (Z = 3.54, p < 0.001). STG-loss β but not STG-win β was significantly correlated with aggression score in men, and a slope test confirmed the specificity of this valence-related correlation (Z = 3.66, p < 0.001). Further, the correlations of STG-loss β and of STG response to negative emotional faces versus shapes with aggression score differed in men (Z = 2.70, p = 0.007). Conclusions: STG response to loss represents a neural correlate of aggression in men but not in women. The STG-loss response did not reflect a broader relationship between aggression and negative emotion.

Objective. This study aims to investigate the pulse wave characteristics across the menstrual cycle and pregnancy, comparing their cardiovascular adaptations to elucidate shared physiological mechanisms and potential predictive markers for pregnancy-related cardiovascular risks. Approach. The study analyzes pulse wave characteristics, including hemodynamic parameters (cardiac output (CO) and systemic vascular resistance), waveform features, and pulse wave characteristic indices. The analysis spans two physiological cycles: (1) the menstrual cycle, with parallel evaluations of normal and dysmenorrhea groups; and (2) stratified pregnancy stages (early, mid, and late), with comparative assessments between normal and abnormal blood parameter groups. Main results. Both the menstrual cycle and pregnancy exhibit analogous pulse wave variations, reflecting hemodynamic adjustments in CO and vascular elasticity. Pregnancy stages demonstrate progressive pulse wave alterations, with abnormal blood parameters correlating with distinct waveform deviations. Menstrual cycle patterns provide a foundational model for these adaptive changes. Significance. The findings reveal a physiological continuum between menstrual and gestational cardiovascular adaptations, highlighting pulse wave analysis as a potential tool for early risk stratification. This study advances the understanding of female cardiovascular dynamics, offering implications for targeted health monitoring and intervention strategies.