Thyroid diseases are common clinical endocrine disorders， and their pathogenesis is generally considered to be closely related to genetic predisposition factors， immune system disorders， hormone levels， etc. Xiaoyaosan is widely used in the treatment of various thyroid diseases with excellent effects. This study summarized the relevant literature on the treatment of thyroid diseases with modified Xiaoyaosan prescriptions and their active ingredients from aspects such as theoretical analysis， clinical research， and mechanism research. Theoretical analysis revealed that Xiaoyaosan could not only disperse stagnated liver qi but also replenish deficient spleen Qi， which was consistent with the etiology and pathogenesis of thyroid diseases. Clinical studies found that Xiaoyaosan and its modified prescriptions could be widely used in the treatment of multiple thyroid diseases， such as hyperthyroidism， Hashimoto's thyroiditis， and thyroid nodules. Both the use of modified Xiaoyaosan alone and in combination with medications such as methimazole， propylthiouracil， and euthyrox could effectively improve patients' clinical symptoms. In the mechanism research， this study discovered that the whole formula of Xiaoyaosan and its modified prescriptions could inhibit inflammatory reactions， regulate immune balance， and delay liver damage during the treatment of thyroid diseases. The research on Xiaoyaosan for treating thyroid diseases mainly focused on thyroid cancer， autoimmune thyroiditis， hyperthyroidism， and hypothyroidism. The mechanisms of action mainly involved promoting cell apoptosis， inhibiting cell proliferation and migration， arresting the cell cycle， and regulating thyroid hormone levels. In conclusion， this study systematically combs and summarizes the research status of Xiaoyaosan in treating thyroid diseases through literature retrieval， aiming to provide new perspectives and new ideas for the prevention and treatment of thyroid diseases with traditional Chinese medicine.

Objective To analyze the effect of the activation rate of peripheral blood monocytes on the recovery of patients after liver transplantation and to initially explore the possible influencing factors for differences in monocyte activation rates. Methods A total of 139 patients who underwent orthotopic liver transplantation from September 2020 to June 2023 at Department of Liver Surgery and Transplantation of Zhongshan Hospital, Fudan University were selected. The proportion of CD14^＋HLA-DR^＋ monocytes in peripheral blood was defined as the monocyte activation rate. The difference in monocyte activation rates between postoperative day 7 (POD7) and postoperative day 1 (POD1) was calculated as Δ, and patients were divided into Δ＞0 group (n＝73) and Δ＜0 group (n＝66). The two groups were compared in terms of complete blood count, liver and kidney function, coagulation indicators, infection indicators, ICU length of stay, total length of hospitalization, and 90-day mortality. Changes in the proportions of different monocytes subsets (Mo0, Mo1, Mo2, and Mo3) and HLA-DR expression in peripheral blood on POD1 and POD7 were detected using flow cytometry. Results The ICU length of stay in the Δ＜0 group was significantly longer than that in the Δ＞0 group (18[12, 26] days vs 14[10, 20.5] days, P＝0.018). On POD1, the proportion of Mo0 in the Δ＞0 group was significantly lower than that in the Δ＜0 group (P＜0.05); on POD7, the proportion of Mo0 in the Δ＞0 group was significantly lower than that in the Δ＜0 group (P＜0.001), while the proportions of Mo1, Mo2, and Mo3 were significantly higher than those in the Δ＜0 group (P＜0.001). Compared to POD1, the HLA-DR expression level of Mo0 in peripheral blood of patients with liver transplantation significantly decreased on POD7 (P＜0.01), while there was no significant difference in HLA-DR expression levels of Mo1, Mo2, and Mo3. Conclusions Increased proportion of Mo0 (CD14^lowCD16⁻HLA-DR^low) among peripheral blood monocyte subsets may be one of the influencing factors for the differences in monocyte activation rates in patients with liver transplantation. The difference in monocyte activation rate can serve as a new clinical indicator for assessing changes in the immune status and postoperative recovery of patients with liver transplantation.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Objective: To investigate the molecular regulatory mechanism of hypoxia-inducible factor-1α (HIF-1α) in mechanical stress-induced inflammatory cytokine expression in human periodontal ligament cells (hPDLCs), providing a theoretical basis and potential therapeutic target for inflammatory control during orthodontic treatment. Methods: This study was approved by the Institutional Ethics Committee. Primary human periodontal ligament cells (hPDLCs) were isolated and cultured in vitro. Self-renewal capacity was confirmed via colony-forming assays, while osteogenic and adipogenic differentiation potential was evaluated via Alizarin Red S staining, alkaline phosphatase (ALP) activity assays, and Oil Red O staining. An in vitro compressive force stimulation model (1.5 g/cm2, 12 h) was established to compare inflammatory cytokine expression of hPDLCs—interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and HIF-1α—between the Control group (no mechanical stimulation) and the Force group (1.5 g/cm2, 12 h) using quantitative real-time PCR (qRT-PCR), Western blot, and immunofluorescence (IF) staining. Mechanically induced HIF-1α-regulated gene expression changes were analyzed through transcriptomic sequencing. To explore pharmacological inhibition, the small-molecule HIF-1α inhibitor LW-6 was applied at varying concentrations (10 μmol/L, 30 μmol/L, 50 μmol/L) to optimize the treatment dose. Subsequently, qRT-PCR, Western blot, and IF staining were conducted to evaluate inflammatory cytokine of hPDLCs and HIF-1α expression in three groups: Control (no force), Force (1.5 g/cm2, 12 h), and Force+LW6 (1.5 g/cm2, 12 h + 30 μmol/L LW-6). Results: Primary hPDLCs demonstrated self-renewal capacity along with osteogenic and adipogenic differentiation potential. Compared to the Control group, the Force group exhibited significantly increased mRNA and protein expression levels of inflammatory cytokines IL-1β, IL-6, and TNF-α, along with enhanced fluorescence intensity of IL-1β and TNF-α. Transcriptomic analysis revealed that mechanical compressive force activated the HIF-1 signaling pathway, which subsequently mediated inflammatory responses and bone remodeling processes in hPDLCs. Furthermore, the mRNA and protein levels of HIF-1α were considerably elevated in the Force group compared to the Control group. Treatment with LW-6 (10, 30, or 50 μmol/L) effectively suppressed HIF-1α expression, with 30 μmol/L LW-6 identified as the optimal concentration for intervention. In subsequent experiments, the Force group showed significant upregulation in mRNA/protein expression of IL-1β, IL-6, and TNF-α compared to the Control group, as well as intensified HIF-1α, IL-1β, and TNF-α fluorescence signals. Conversely, the Force+LW6 group (mechanical force + 30 μmol/L LW-6) exhibited a notable reduction in inflammatory cytokine expression levels and a weakening of HIF-1α, IL-1β, and TNF-α fluorescence signals compared to the Force group. Conclusion HIF-1α potentiates mechanical stress-induced inflammatory responses in hPDLCs and may serve as a promising therapeutic target for mitigating orthodontic-associated periodontal inflammation.

ObjectiveTo systematically review the modeling methods and analyzes the model alignment with clinical features of primary dysmenorrhea （PD） in both traditional Chinese medicine （TCM） and western medicine， providing theoretical and practical guidance for establishing the animal models of PD that better reflect the diagnostic and therapeutic characteristics of both TCM and western medicine. MethodsThe literature on PD animal models was searched against domestic and international databases such as PubMed， CNKI， and Wanfang Data. According to the diagnostic criteria of TCM and western medicine， the modeling methods in the literature were summarized， evaluated for strengths and weaknesses， and systematically assessed for clinical concordance rates to identify suitable reference models. ResultsThe available animal models of PD showed the average clinical concordance rates of 43.64% and 61.27% with the clinical features in TCM and western medicine， respectively. Commonly used modeling methods included estrogen administration， physical stimulation， and surgical intervention， with the estrogen combined with oxytocin model and the ice-water bath model being the most studied. The model of Qi stagnation and blood stasis syndrome that was established with the comprehensive stimulation method demonstrated the highest clinical concordance rate. ConclusionCurrent PD animal models primarily replicate dysmenorrhea and simulate menstruation， but they differ from human menstruation to some extent and cannot fully reflect the pathogenesis and physiological characteristics of PD. Moreover， except the cold coagulation and dampness stagnation syndrome and Qi stagnation and blood stasis syndrome， no animal models for other TCM syndromes have been reported， which limits comprehensive TCM research on this disease to a certain extent.

ObjectiveTo systematically review the modeling methods and analyzes the model alignment with clinical features of primary dysmenorrhea （PD） in both traditional Chinese medicine （TCM） and western medicine， providing theoretical and practical guidance for establishing the animal models of PD that better reflect the diagnostic and therapeutic characteristics of both TCM and western medicine. MethodsThe literature on PD animal models was searched against domestic and international databases such as PubMed， CNKI， and Wanfang Data. According to the diagnostic criteria of TCM and western medicine， the modeling methods in the literature were summarized， evaluated for strengths and weaknesses， and systematically assessed for clinical concordance rates to identify suitable reference models. ResultsThe available animal models of PD showed the average clinical concordance rates of 43.64% and 61.27% with the clinical features in TCM and western medicine， respectively. Commonly used modeling methods included estrogen administration， physical stimulation， and surgical intervention， with the estrogen combined with oxytocin model and the ice-water bath model being the most studied. The model of Qi stagnation and blood stasis syndrome that was established with the comprehensive stimulation method demonstrated the highest clinical concordance rate. ConclusionCurrent PD animal models primarily replicate dysmenorrhea and simulate menstruation， but they differ from human menstruation to some extent and cannot fully reflect the pathogenesis and physiological characteristics of PD. Moreover， except the cold coagulation and dampness stagnation syndrome and Qi stagnation and blood stasis syndrome， no animal models for other TCM syndromes have been reported， which limits comprehensive TCM research on this disease to a certain extent.

Background Although current evidence suggests a link between outdoor air pollution and depressive symptoms, the effect of solid fuel use (a significant indoor air pollutant) on depressive symptoms in China's rural middle-aged and elderly population remains poorly understood. Objective To explore the association between solid fuel use for cooking and depressive symptoms among middle-aged and elderly people in rural areas of China, and to provide a basis for the prevention and control of depressive symptoms among residents in rural areas. Methods Data were obtained from the 2020 China Family Panel Studies (CFPS), depressive symptoms were assessed using 8-item Center for Epidemiologic Studies Depression Scale (CES-D), and cooking fuel type was self-reported. Subsequently, two-level binary unconditional logistic regression models were fitted to assess the impact of solid fuel use for cooking on depressive symptoms. Results A total of 7991 participants aged 45 years and older [mean age (58.52±9.18) years] were included in the study, with a mean CES-D score of 6.07±4.48 and a mean positive depressive symptoms rate of 34% (40.54% in the solid fuel group and 30.81% in the clean fuel group, χ²=74.40, P<0.001). After controlling for potential confounding covariates, rural middle-aged and elderly residents in the solid fuel group had a higher risk of reporting depressive symptoms than those in the clean fuel group (OR=1.36, 95%CI: 1.20-1.54), an association that was unaffected by participant characteristics and was positive across age and gender groups. Conclusion There is a positive association between solid fuel use for cooking and depressive symptoms in middle-aged and elderly people in rural areas of China. It is recommended that rural areas promote the upgrading of stoves through the installation of ventilation equipment or the switch from solid cooking fuel to clean fuel. This approach is directed toward reducing exposure to household air pollution, alleviating the prevalence of depression among middle-aged and elderly residents in vast rural areas, and enhancing their mental health.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.