ObjectiveTo investigate the migration and distribution characteristics of chemical constituents in blood and hippocampal tissues before and after vinegar processing of Citri Reticulatae Pericarpium Viride（CRPV）， and to explore the potential material basis and mechanisms underlying their regulatory effects on emotional disorders by comparing the effects of raw and vinegar-processed products of CRPV. MethodsUltra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS） was employed to characterize and identify the chemical constituents of raw and vinegar-processed products of CRPV extracts， as well as their migrating components in blood and hippocampal tissues after oral administration. Reference standards， databases， and relevant literature were utilized for compound annotation， with data processing performed using PeakView 1.2 software. Seventy male C57BL/6 mice were randomly divided into seven groups， including the blank group， model group， diazepam group（2.5 mg·kg^-1）， raw CRPV low/high dose groups（0.6， 1.2 g·kg^-1）， and vinegar-processed CRPV low/high dose groups（0.6， 1.2 g·kg^-1）， with 10 mice per group. Except for the blank group， all other groups underwent chronic restraint stress（2 h·d^-1） for 20 d. Each drug-treated group received oral administration at the predetermined dose starting 10 d after modeling， with a total treatment duration of 10 d. Following model-based drug administration， mice underwent open-field， forced swimming， and elevated plus maze tests. After anesthesia with isoflurane， whole brains were collected from each group of mice， and hippocampi were dissected. Reactive oxygen species（ROS） level in hippocampal tissues was quantified by enzyme-linked immunosorbent assay（ELISA）. Hematoxylin-eosin（HE） staining was used to observe hippocampal tissue morphology. Immunofluorescence was performed to detect neuronal nuclei（NeuN） and peroxisome proliferator-activated receptor alpha（PPARα） expressions in hippocampal tissue. Then， pharmacodynamic evaluations were conducted to assess the effects of raw and vinegar-processed CRPV on mood disorders， exploring the potential mechanisms. ResultsVinegar processing caused significant changes in the chemical composition of CRPV， with 18 components showing increased relative content and 35 components showing decreased relative content. The primary changes occurred in flavonoid compounds， including 20 flavonoids， 20 flavonoid glycosides， 3 triterpenes， 3 phenolic acids， 1 alkaloid， and 6 other compounds. Twenty-one components were detected in blood（15 methoxyflavones， 4 flavonoid glycosides， and 2 phenolic acids）， with 17 shared between raw and vinegar-processed CRPV. Seven components reached hippocampal tissues（all common to both forms）. In regulating emotional disorders， Vinegar-processed CRPV exhibited superior antidepressant-like effects compared to raw products. HE staining revealed that both treatments improved hippocampal neuronal morphology， particularly in the damaged CA1 and CA3 regions. Immunofluorescence and ELISA analyses demonstrated that both raw and vinegar-processed CRPV significantly modulated NeuN and PPARα expressions in hippocampal tissue while alleviating oxidative stress induced by excessive ROS（P<0.05）. ConclusionThe chemical composition of CRPV undergoes changes after vinegar processing， but the migrating components in blood and hippocampus are primarily methoxyflavonoids. These components may serve as the potential material basis for activating the PPARα pathway， thereby negatively regulating ROS generation in the hippocampus， reducing oxidative stress， and promoting the development of NeuN-positive neurons. These findings provide experimental evidence for enhancing quality standards， pharmacodynamic material research， and active drug development of raw and vinegar-processed CRPV.

Objective To explore the feasibility and reference value of allogeneic lung transplantation and postoperative monitoring in miniature pigs for lung transplantation research. Methods Two miniature pigs (R1 and R2) underwent left lung allogeneic transplantation. Complement-dependent cytotoxicity tests and blood cross-matching were performed before surgery. The main operative times and partial pressure of arterial oxygen (PaO₂) after opening the pulmonary artery were recorded during surgery. Postoperatively, routine blood tests, biochemical blood indicators and inflammatory factors were detected, and pathological examinations of multiple organs were conducted. Results The complement-dependent cytotoxicity test showed that the survival rate of lymphocytes between donors and recipients was 42.5%-47.3%, and no agglutination reaction occurred in the cross-matching. The first warm ischemia times of D1 and D2 were 17 min and 10 min, respectively, and the cold ischemia times were 246 min and 216 min, respectively. Ultimately, R1 and R2 survived for 1.5 h and 104 h, respectively. Postoperatively, in R1, albumin (ALB) and globulin (GLB) decreased, and alanine aminotransferase increased; in R2, ALB, GLB and aspartate aminotransferase all increased. Urea nitrogen and serum creatinine increased in both recipients. Pathological results showed that in R1, the transplanted lung had partial consolidation with inflammatory cell infiltration, and multiple organs were congested and damaged. In R2, the transplanted lung had severe necrosis with fibrosis, and multiple organs had mild to moderate damage. The expression levels of interleukin-1β and interleukin-6 increased in the transplanted lungs. Conclusions The allogeneic lung transplantation model in miniature pigs may systematically evaluate immunological compatibility, intraoperative function and postoperative organ damage. The data obtained may provide technical references for subsequent lung transplantation research.

Perimenopausal depression （PMD） is an affective disorder that occurs in women during the transition from sexual maturity to old age. It can induce various complications， such as insomnia and cognitive decline. The etiology of PMD is complex. Although multiple hypotheses have been proposed， there is still no unified theory that fully explains its pathogenesis. Research into its mechanisms relies heavily on animal experiments， and establishing reliable animal models is crucial for experimental studies. Appropriate animal models can better simulate human pathophysiological states， rapidly evaluate the efficacy and safety of drugs and intervention methods， grasp the essence of the disease， and uncover its intrinsic connections， thereby exploring more advanced intervention strategies. However， there is a lack of systematic review and summarization of literature related to model construction. Additionally， traditional Chinese medicine （TCM）， adhering to the principles of ''syndrome differentiation and treatment'' and ''holistic concept''， has shown significant efficacy in treating PMD. In recent years， research exploring and analyzing its therapeutic mechanisms has been increasing. Therefore， to gain a clearer and more comprehensive understanding of PMD animal modeling methods and the mechanisms of TCM， this paper reviewed Chinese and English literature on PMD animal models and mechanisms of TCM in PMD treatment. It summarized the construction methods of single-factor and multi-factor PMD models， and discussed the advantages and disadvantages of each modeling approach. Furthermore， it delved into the mechanisms of TCM intervention in PMD， revealing that TCM formulas primarily exert their effects by regulating the hypothalamic-pituitary-adrenal axis， hypothalamic-pituitary-ovarian axis， gut-brain axis， cell signaling pathways， neural circuits， hormone levels， and neurotransmitter levels. This review aims to provide a reference for future research in this field. In summary， by summarizing the progress in the methods for PMD animal model construction and the mechanisms of TCM， the paper seeks to offer new insights into the mechanistic research of TCM intervention in PMD.

Objective: To analyze the association between drinking behaviors and anxiety symptoms, with the mediating role of insomnia symptoms among college freshmen, so as to provide a reference basis for reducing the occurrence of anxiety symptoms in college freshmen. Methods: From October to December 2021, 31 856 freshmen were selected by the purposive sampling method in 22 colleges across 11 provinces (Fujian, Jiangsu, Guangdong, Henan, Anhui, Hubei, Shanxi, Jiangxi, Shaanxi, Yunnan, Chongqing) in China. The Semi quantitative Food Frequency Questionnaire was used to investigate college freshmen drinking behaviors. The Depression Anxiety Stress Scale 21 and the Insomnia Severity Index were used to assess anxiety symptoms and insomnia symptoms in college freshmen. The generalized linear model was employed to analyze the association between drinking behaviors and anxiety symptoms in college freshmen, and the structural equation modeling was used to assess the mediating effect of insomnia symptoms on the association. Results: The detection rate of anxiety symptoms among college freshmen was 28.2%, the detection rates of the mild, moderate, severe and extremely severe were 6.6%, 15.9%, 3.2% and 2.6%, respectively. While 23.6% of college freshmen reported drinking in the past month, the rates were 39.8% among boys and 15.9% among girls. After adjusting for demographic variables (ethnicity, education, major, etc.) and confounding variables (self evaluation of learning burden, number of close friends, screen time, etc.), the results of generalized linear model analysis showed that beer consumption was associated with anxiety symptoms in college freshmen( β =0.09, 95% CI =0.04-0.14), girls( β =0.14, 95% CI =0.07-0.21) and those aged 19-20 years ( β =0.12, 95% CI =0.05-0.19)(all P <0.05). Red wine consumption was associated with anxiety symptoms in male students ( β =0.13, 95% CI =0.02-0.24, P <0.05). Alcohol and beer consumption were associated with insomnia in college freshmen[ β (95% CI ) =0.22(0.08-0.36),0.31(0.23-0.39),both P <0.01]. Insomnia symptoms partially mediated the association between drinking behaviors and anxiety symptoms among college freshmen with a mediating effect value of 0.05, accounting for 50.49% of the total effect. Conclusions Insomnia symptoms partially mediates the association between drinking behaviors and anxiety symptoms in college freshmen. Measures should be taken to simultaneously intervene in the drinking behaviors and insomnia symptoms of college freshmen to prevent the occurrence of their anxiety symptoms.

With the rising incidence of colon cancer, several studies have indicated that aquaporin 1 (AQP1) expression is associated with the development of colon cancer. This study aims to elucidate the potential molecular mechanisms between them. Methods: We screened data from The Cancer Genome Atlas (TCGA) database and retrospectively examined AQP1 protein expression in 127 colon cancer patients to analyze the relationship between AQP1 expression and pathological stages, prognosis. We created stable colon cancer cell lines with differential AQP1 expression, the effect of AQP1 expression on the proliferation and migration of colon cancer cells was assessed by in vitro and in vivo studies, and explored potential molecular mechanisms through Western blotting. Results: High AQP1 expression was associated with poorer survival (overall survival [OS], p = .028) in colon cancer patients from the TCGA database. Similarly, retrospective clinical data indicated that high AQP1 expression was associated with reduced disease-free survival and OS (p = .036 and p = .017, respectively). The low-expressing AQP1 colon cancer cells exhibited a decrease in proliferation and migration ability of colon cancer cells compared to the overexpressing AQP1 group (p < .05) in vitro and in vivo. Immunohistochemistry and western blotting experiments validated heightened expression of N-cadherin, vimentin, and claudin- 1 in the tumor tissues of the overexpressing AQP1 group. Conversely, reduced AQP1 expression resulted in decreased expression of claudin- 1. Conclusions: AQP1 correlates with unfavorable prognosis in colon cancer and potentially enhances the proliferation and migration of colon cancer by up-regulating claudin-1 expression.

Objective To investigate the activity concentrations of natural radionuclides in drinking water (tap water and well water) in urban and rural areas of Hohhot, assess the safety of drinking water, and to provide data support for local drinking water radioactivity monitoring and management. Methods Representative samples of well water and tap water were collected from nine banners/counties/districts in Hohhot. Activity concentrations were measured using a low-background gross α/β counter, an α spectrometer, inductively coupled plasma mass spectrometry, and a radium/radon analyzer. Results A total of nine tap water samples and nine well water samples were analyzed. For the tap water samples, gross α activity concentrations ranged from 0.093 to 0.193 Bq/L, gross β from 0.091 to 0.225 Bq/L, uranium mass concentrations from 2.32 to 10.36 μg/L, thorium mass concentrations from 0.09 to 0.20 μg/L,²¹⁰Po activity concentrations from below the minimum detectable limit to 0.41 mBq/L, and ²²⁶Ra activity concentrations from 8.70 to 13.35 mBq/L. For the well water samples, gross α activity concentrations ranged from 0.111 to 0.203 Bq/L, gross β from 0.111 to 0.270 Bq/L, uranium mass concentrations from 2.31 to 13.28 μg/L, thorium mass concentrations from 0.17 to 0.26 μg/L,²¹⁰Po activity concentrations from 1.03 to 2.12 mBq/L, and ²²⁶Ra activity concentrations from 15.38 to 23.63 mBq/L. Conclusion The activity concentrations of natural radionuclides in both well water and tap water in the Hohhot region were at environmental background levels and met national drinking water hygiene standards.

Cholangiocarcinoma (CCA) is a malignancy characterized by tumor cells originating in the liver or bile ducts, exhibiting features of cholangiocyte differentiation. It poses a significant clinical challenge due to the limited diagnostic and therapeutic options available. Robust animal models are essential for advancing our understanding of CCA pathogenesis and developing effective treatments. This review provides a comprehensive overview of CCA mouse models, highlighting various approaches, including chemical induction, genetically engineered models, and tumor xenografts. Each model is discussed in terms of its establishment techniques, pathological characteristics, and research significance, with a focus on intrahepatic CCA. Chemical induction models, such as diethylnitrosamine- and azoxymethane-induced models, offer insights into tumorigenesis processes, whereas genetically modified models involving alterations in key genes such as Kirsten rat sarcoma viral oncogene homolog, tumor protein 53, and isocitrate dehydrogenase serve as important tools for studying the molecular mechanisms underlying CCA. Xenograft models, including patient-derived xenografts, bridge the gap between experimental research and clinical applications, allowing for precise therapeutic evaluations. By comparing these models, this review underscores their respective advantages and limitations, paving the way for future studies aiming to optimize and innovate CCA modeling strategies.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective To investigate the relationship between tooth loss and the occurrence of esophageal cancer in a natural village in Wenfeng District, Anyang City, Henan Province. Methods A prospective cohort study was conducted to observe the occurrence of tooth loss and esophageal cancer among the asymptomatic residents of the natural village for 16 years from January 2008 to July 2024. Data were analyzed by chi-square test, binary logistic regression, and restricted cubic spline. Results Among the total population of 711 cases, 136 cases were lost to follow-up and 575 cases were included in the final statistics, including 45 cases with esophageal cancer. Significant statistical difference was found between esophageal cancer patients with and without tooth loss (P<0.05). Logistic regression analysis showed that tooth loss was associated with the occurrence of esophageal cancer (OR=3.977, 95%CI: 1.543-10.255). After the adjustment for confounders, tooth loss remained significantly associated with the occurrence of esophageal cancer (OR=3.038, 95%CI: 1.035-8.914). A nonlinear dose-response relationship was observed between the number of teeth lost and the incidence of esophageal cancer. When the number of teeth lost was less than 12, the risk of esophageal cancer increased with the number of teeth lost. When more than 12 teeth were lost, the risk of esophageal cancer decreased as the number of teeth lost increased. Conclusion Tooth loss is a risk factor for the occurrence of esophageal cancer in this natural village. When the number of teeth lost is less than 12, the risk of esophageal cancer increases with the number of teeth lost.

Chronic obstructive pulmonary disease （COPD） is a common clinical chronic airway disease with high morbidity， high mortality， a heavy disease burden， and complex mechanisms that have not been fully elucidated. Clinical problems promote the continuous progress of basic research. The establishment and evaluation of animal models is an important way to delve into the pathogenesis and treatment strategies of COPD. On the basis of the etiology， pathogenesis， diagnostic criteria， and syndrome differentiation of COPD in traditional Chinese medicine （TCM） and Western medicine， this paper summarizes the establishment methods and characteristics of existing animal models of COPD and analyzes the fitting degree of the models with the disease characteristics in Western medicine and syndrome characteristics in TCM. The results showed that the animal models of COPD in Western medicine were mainly induced by single factors and compound factors， and the model with the highest fitting degree was established by cigarette smoke combined with lipopolysaccharide and hormone. The model showed a fitting degree of 84% with the disease characteristics in Western medicine and 70% with the syndrome characteristics in TCM， being consistent with the clinical characteristics of COPD induced by multiple factors. Most of the animal models of COPD in TCM were established on the basis of disease models and combined with TCM etiology and pathogenesis characteristics， and prepared by syndrome differentiation. Among them， the model of accumulation of cold and water retention in lung had the highest fitting degree of 92% with the TCM diagnostic criteria. The models of phlegm-heat obstructing lung and phlegm-stasis obstructing lung had the fitting degree of 94% with clinical manifestations in Western medicine and the highest fitting degree with the diagnostic criteria in Western medicine. Different animal models of COPD have their own advantages and disadvantages， and most of them simply replicate the manifestations of COPD at a certain stage， failing to reflect the multiple causes and the dynamic changes of TCM etiology and pathogenesis. Moreover， the syndromes of these models fail to match the clinical syndromes in TCM. Therefore， establishing the animal models reflecting clinical characteristics of COPD in TCM and Western medicine and improving the model evaluation criteria are important contents to promote the overall development of integrated traditional Chinese and western medicine for COPD.