Ulcerative colitis （UC）， a chronic relapsing inflammatory bowel disease， involves multifaceted pathological mechanisms such as intestinal barrier dysfunction， immune dysregulation， and oxidative stress. Current therapeutic strategies remain limited in efficacy and safety. In recent years， the adenosine monophosphate-activated protein kinase （AMPK） signaling pathway has emerged as a pivotal therapeutic target for UC due to its central role in energy metabolism， inflammatory regulation， and intestinal homeostasis. This article systematically reviewed the mechanisms by which traditional Chinese medicine （TCM） prevented and treated UC through the regulation of the AMPK signaling pathway， with a focus on elucidating AMPK's multidimensional regulatory network in inflammatory signaling crosstalk， alleviating oxidative stress， restoring intestinal immune balance， repairing the intestinal barrier， and modulating gut microbiota. Leveraging its unique advantages of multi-target engagement and low toxicity， TCM demonstrates promising potential in UC treatment and has become a focal area of research. By systematically summarizing and synthesizing the existing literature on TCM-mediated AMPK pathway modulation in UC， this review aims to provide a theoretical foundation for advancing mechanistic research and clinical interventions in UC.

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.

ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.

ObjectiveEarly blight is a common destructive disease in the growth process of Solanaceae crops, which can lead to crop failure and serious losses. Traditional crop disease detection methods are difficult to detect disease characteristics in a timely manner during the incubation period of disease, and thus take scientific and effective prevention and control measures. This study obtained hyperspectral images of early blight of peppers at different infection stages through continuous monitoring with a hyperspectral imager. The earliest identifiable time during the incubation period of early blight in peppers (the earliest identifiable time during the incubation period in this experiment was 24 h after inoculation) was determined using the spectral angle cosine-correlation coefficient and Chebyshev distance. MethodsTaking the symptoms of the latent period of early blight in peppers as the research object, 13 characteristic wavelengths were selected using a genetic algorithm. An identification model of crop disease latent period symptoms based on spectral features was established through optimized combinations of characteristic wavelengths combined with a logistic regression model. Simultaneously, a recognition model of the latent period of early blight in peppers based on image texture features was established using local binary patterns. ResultsThe experiment was tested with 120 samples. The accuracy of the identification model of crop disease latent period symptoms based on spectral features reached over 93% in both the training set and the test set. The accuracy of the identification model of crop disease latent period symptoms based on texture features reached 98.96% and 100% in the training set and test set, respectively. ConclusionBoth spectral features and texture features can be used to detect and identify crop disease latent period symptoms. Texture features more significantly revealed the characteristics of the latent period of the disease compared to spectral features, effectively improving the detection performance of the model. The research results in this article can provide theoretical references for monitoring and identifying other crop disease latent period symptoms.

Objective: Huntington’s disease (HD) is characterized by motor, cognitive, and neuropsychiatric symptoms. Oculomotor impairments and gait variability have been independently considered as potential markers in HD. However, an integrated analysis of eye movement and gait is lacking. We performed multiple examinations of eye movement and gait variability in HTT mutation carriers, analyzed the consistency between these parameters and clinical severity, and then examined the associations between oculomotor impairments and gait deficits. Methods: We included 7 patients with pre-HD, 30 patients with HD and 30 age-matched controls. We collected demographic data and assessed the Unified Huntington’s Disease Rating Scale (UHDRS) score. Examinations, including saccades, smooth pursuit tests, and optokinetic (OPK) tests, were performed to evaluate eye movement function. The parameters of gait include stride length, walking velocity, step deviation, step length, and gait phase. Results: HD patients have significant impairments in the latency and velocity of saccades, the gain of smooth pursuit, and the gain and slow phase velocities of OPK tests. Only the speed of saccades significantly differed between pre-HD patients and controls. There are significant impairments in stride length, walking velocity, step length, and gait phase in HD patients. The parameters of eye movement and gait variability in HD patients were consistent with the UHDRS scores. There were significant correlations between eye movement and gait parameters. Conclusion Our results show that eye movement and gait are impaired in HD patients and that the speed of saccades is impaired early in pre-HD. Eye movement and gait abnormalities in HD patients are significantly correlated with clinical disease severity.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

OBJECTIVE: Varied acupoint selections represent a potential cause of the uncertainty surrounding the efficacy of acupuncture for knee osteoarthritis (OA). Skin temperature, a guiding factor for acupoint selection, may help to address this issue. This study explored thermal sensitization of acupoints used for the treatment of knee OA. METHODS: This cross-sectional case-control study enrolled cases aged 45-75 years with symptomatic knee OA and age- and gender-matched non-knee OA controls in a 1:1 ratio. All participants underwent infrared thermographic imaging. The primary outcome was the relative skin temperature of acupoint (STA), and the secondary outcome was the absolute STA of 11 acupoints. The Z test was used to compare the relative and absolute STAs between the groups. Principal component analysis was used to extract the common factors (CFs, acupoint cluster) in the STAs. A general linear model was used to identify factors affecting the STA in the knee OA cases. For the group comparisons of relative STA, P < 0.0045 (adjusted for 11 acupoints through Bonferroni correction) was considered to indicate statistical significance. For other analyses, P < 0.05 was used as the threshold for statistical significance. RESULTS: The analysis included 308 participants, consisting of 151 cases (mean age: [64.58 ± 6.67] years; male: 25.83%; mean body mass index: [25.70 ± 3.16] kg/m²) and 157 controls (mean age: [63.37 ± 5.96] years; male: 26.11%; mean body mass index: [24.47 ± 2.84] kg/m²). The relative STAs of ST34 (P = 0.0001), EX-LE2 (P < 0.0001), EX-LE5 (P = 0.0006), SP10 (P < 0.0001), BL40 (P = 0.0012) and GB39 (P = 0.0037) were higher in the knee OA group. No difference was found in the STAs of ST35, ST36, SP9, GB33 and GB34. Four CFs were identified for relative STA in both groups. The acupoints within each CF were consistent between the groups. The mean values of the relative STAs across each CF were higher in the knee OA group. In the knee OA cases, no factors were observed to affect the relative STA, while age and gender were found to affect the absolute STA. CONCLUSION Among patients with knee OA, thermal sensitization occurs in the acupoints of the lower extremity, exhibiting localized and regional thermal consistencies. The thermally sensitized acupoints that we identified in this study, ST34, SP10, EX-LE2, EX-LE5, GB39 and BL40, may be good choices for the acupuncture treatment of knee OA. Please cite this article as: Tu JF, Wang XZ, Yan SY, Wang YR, Yang JW, Shi GX, Zhang WZ, Jing LN, Yang LS, Liu DH, Wang LQ, Mi BH. Thermal sensitization of acupoints in patients with knee osteoarthritis: A cross-sectional case-control study. J Integr Med. 2025; 23(3): 289-296.
Humans ; Osteoarthritis, Knee/physiopathology* ; Male ; Cross-Sectional Studies ; Middle Aged ; Female ; Acupuncture Points ; Case-Control Studies ; Aged ; Skin Temperature ; Acupuncture Therapy

OBJECTIVE: This study aimed to investigate the impact of glycemic control and diabetes duration on subsequent myocardial infarction (MI) in patients with both coronary heart disease (CHD) and type 2 diabetes (T2D). METHODS: We conducted a retrospective cohort study of 33,238 patients with both CHD and T2D in Shenzhen, China. Patients were categorized into 6 groups based on baseline fasting plasma glucose (FPG) levels and diabetes duration (from the date of diabetes diagnosis to the baseline date) to examine their combined effects on subsequent MI. Cox proportional hazards regression models were used, with further stratification by age, sex, and comorbidities to assess potential interactions. RESULTS: Over a median follow-up of 2.4 years, 2,110 patients experienced MI. Compared to those with optimal glycemic control (FPG < 6.1 mmol/L) and shorter diabetes duration (< 10 years), the fully-adjusted hazard ratio ( HR) (95% Confidence Interval [95% CI]) for those with a diabetes duration of ≥ 10 years and FPG > 8.0 mmol/L was 1.93 (95% CI: 1.59, 2.36). The combined effects of FPG and diabetes duration on MI were largely similar across different age, sex, and comorbidity groups, although the excess risk of MI associated with long-term diabetes appeared to be more pronounced among those with atrial fibrillation. CONCLUSION Our study indicates that glycemic control and diabetes duration significant influence the subsequent occurrence of MI in patients with both CHD and T2D. Tailored management strategies emphasizing strict glycemic control may be particularly beneficial for patients with longer diabetes duration and atrial fibrillation.
Humans ; Diabetes Mellitus, Type 2/blood* ; Male ; Female ; Middle Aged ; Aged ; Coronary Disease/complications* ; Myocardial Infarction/etiology* ; Retrospective Studies ; China/epidemiology* ; Glycemic Control ; Blood Glucose ; Adult ; Risk Factors ; Time Factors