Background: Transcutaneous auricular vagus nerve stimulation (taVNS) is effective for treating major depressive disorder (MDD). We aimed to explore the modulating effect of prolonged longitudinal taVNS on the striatal subregions’ functional connectivity (FC) in MDD patients. Methods: Sixteen MDD patients were enrolled and treated with taVNS for 8 weeks. Sixteen healthy control subjects (HCs) were recruited without intervention. The resting-state FC (rsFC) based on striatal subregion seed points and the Hamilton Depression Scale (HAMD) were evaluated in the MDD patients and HCs at baseline and after 8 weeks. A two-way ANCOVA test was performed on each rsFC metric to obtain the (group-by-time) interactions. Results: The rsFC values between the left ventral caudate (vCa) and right ventral prefrontal cortex (vPFC), and between the right nucleus accumbens (NAc) and right dorsal medial prefrontal cortex (dmPFC) and ventrolateral prefrontal cortex (vlPFC) are lower in the MDD patients compared to the HCs at baseline, and increase following taVNS; the rsFC values between the left vCa and right, superior occipital gyrus (SOG), and between the left dorsal caudate (dCa) and right cuneus are higher in MDD patients and decrease following taVNS. Conclusions: Prolonged longitudinal taVNS can modulate the striatum rsFC with the prefrontal cortex, occipital cortex, temporal cortex, and intra-striatum, and these changes partly underlie any symptomatic improvements. The results indicate that prolonged longitudinal taVNS may produce beneficial treatment effects by modulating the cortical striatum circuitry in patients with MDD.

Introduction: The incidence of insomnia is getting higher and higher. Long-term insomnia seriously affects people’s health. Drug use is usually accompanied with adverse events. Both acupuncture and cognitive behavioral therapy for insomnia (CBT-I) have been proven to be safe and effective non-pharmacological treatments for insomnia. As the insomniacs’ bad sleep behavior and wrong cognition have not been effectively corrected, acupuncture has a quick effect, high patient compliance but unstable long-term efficacy, while CBT-I is complex, time-consuming, and expensive; additionally, patient compliance is low, and the number of trained therapists is limited, making it difficult to carry out. Therefore, this study aims to use the insomnia TCM system to construct a convenient and feasible traditional Chinese medicine e-aid cognitive behavioral therapy for insomnia (TCM-eCBT-I) for Chinese people, and combine the advantages of acupuncture and TCM-eCBT-I for maintaining long-term efficacy, and three treatments will be evaluated to provide clinicians with a more effective clinical protocol Methods and analysis: This study is a single-center, open-label, randomized controlled trial. Ninety subjects will be recruited and randomly assigned to three groups: the acupuncture group, the TCM-eCBT-I group, and the acupuncture combined with TCM-eCBT-I group, in a ratio of 1:1:1. We will evaluate the Pittsburgh Sleep Quality Index (PSQI) and Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS), Insomnia Severity Index (ISI), sleep diary, Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), and Fatigue Scale-14 Scale (FS-14) scales. All adverse reactions will be assessed through the ADVERSE event table. All outcomes will be evaluated online at 0 weeks, 4 weeks, 8 weeks, 16 weeks, and 28weeks. Ethics and dissemination: This study has been approved by the Institutional Review Board of the Affiliated Hospital of Nanjing University of Chinese Medicine (2020 NL-018-02). Informed consent will be obtained from all the subjects. The results will be shared with sleep researchers, public, and relevant academic institutions through high-impact peer-reviewed publications. Trial registration: Chinese Clinical Trial Registry ChiCTR2000032960. Registered on 17 May 2020

The purpose of this review is to evaluate the effectiveness of large animal models in gynecology research and provide future perspectives. Gynecological diseases are diverse and pose a serious threat to women’s physical and mental health. In addition to the commonly used small animal models, large animal models have gradually entered the field of gynecological research. Results suggest that large animal models offer significant advantages in simulating human physiological processes, despite ethical and practical challenges. This paper reviews the application of large animal models in the study of gynecological diseases, provides a summary of the research characteristics of large animal models, analyses the advantages and challenges of these models in disease research, and compares the research differences between large and small animal models. It also discusses the relationship between these models and new alternative models, with a view to providing more new ideas for the selection of animal models in the study of gynecological diseases.

In the context of ear-based biometric identity authentication, symmetry between the left and right ears emerges as a pivotal factor, particularly when registration involves one ear and authentication utilizes its contralateral counterpart. The extent to which bilateral ear symmetry supports consistent identity verification warrants significant investigation. This study addresses this challenge by proposing a novel framework, the Symmetry Alignment–Feature Interaction Network, designed to enhance authentication robustness. The proposed network incorporates a Symmetry Alignment Module, leveraging differentiable geometric alignment and a dual-attention mechanism to achieve precise feature correspondence between the left and right ears, thereby mitigating the robustness deficiencies of conventional methods under pose variations. Additionally, a Feature Interaction Network is introduced to amplify nonlinear interdependencies between binaural features, employing a difference–product dual-path architecture to enhance feature discriminability through Dual-Path Feature Interaction and Similarity Fusion. Experimental validation on a dataset from the University of Science and Technology of Beijing demonstrates that the proposed method achieves a similarity detection accuracy of 99.03% (a 9.11% improvement over the baseline ResNet18) and an F1 score of 0.9252 in identity authentication tasks. Ablation experiments further confirm the efficacy of the Symmetry Alignment Module, reducing the false positive rate by 3.05%, in combination with the Feature Interaction Network, shrinking the standard deviation of similarity distributions between the positive and negative samples by 67%. A multi-task loss function, governed by a dynamic weighting mechanism, effectively balances feature learning objectives. This work establishes a new paradigm for the authentication of biometric features with symmetry, integrating symmetry modeling with Dual-Path Feature Interaction and Similarity Fusion to advance the precision of ear authentication.