Objective To understand the status of body image disturbance and its influencing factors in patients with ankylosing spondylitis (AS), so as to provide a scientific basis for the clinical management of AS. Methods A total of 353 AS patients admitted from January 2022 to December 2024 were selected as research subjects. Chinese version of Body Image Disturbance Questionnaire (BIDQ) was used to investigate the body image disturbance in AS patients. Single factor analysis was performed by t test and analysis of variance, and multiple factors were analyzed by multivariate linear regression. Results The total score of BIDQ in 342 AS patients was (25.01±4.22). Multivariate linear regression analysis results showed that self-paid medical expense, nighttime VAS score and negative emotion PANAS score could positively predict body image disturbance in AS patients (standardized regression coefficient=0.413, 0.413, 0.460, P<0.05), and PSSS score, positive emotion PANAS score and exercise management CDSSM score could negatively predict body image disturbance (standardized regression coefficient=-0.245, -0.134, -0.247, P<0.05). Conclusion The body image disturbance in AS patients is worthy of clinical attention. Nighttime pain, negative emotion and self-paid medical treatment can increase the risk of body image disturbance. Positive emotion, social support and high self-management level of exercise behavior can reduce the formation of body image disturbance, which can provide new ideas for clinical management of AS patients.

Background Per- and poly-fluoroalkyl substances (PFAS) can influence gestational diabetes mellitus (GDM); however, current studies on their association are limited and have yielded inconsistent findings. Objective To investigate the association between maternal exposure to PFAS, as measured by urinary concentrations in early pregnancy, and the risk of developing GDM. Methods Based on the Wuhu Birth Cohort in Anhui Province conducted between 2020 and 2023, this study included 1910 pregnant women. Urine samples were collected during the first trimester, and a 75 g oral glucose tolerance test (OGTT) was completed at 24–28 gestational weeks. The concentrations of six PFAS in urine were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Participants were categorized into low, medium, and high exposure groups according to tertiles of individual PFAS concentrations. Logistic regression analysis was employed to assess the potential impact of early-pregnancy PFAS exposure levels on the risk of GDM, with stratified analyses by maternal age and fetal sex. A generalized weighted quantile sum regression (gWQS) model was applied to evaluate the relative weight distribution of the six PFAS in their mixture effect. Results The logistic regression results revealed that both medium exposure to perfluorohexane sulfonate (PFHxS) (OR=1.475, 95%CI: 1.040, 2.093) and high exposure to perfluorononanoic acid (PFNA) (OR=1.430, 95%CI: 1.013, 2.018) were positively associated with diagnosed GDM. This association was more pronounced among women older than 28 years. Among pregnant women carrying male fetuses, high-level exposures to per fluorohexanoic acid (PFHxA) (OR=1.708, 95%CI: 1.006, 2.899) and PFHxS (OR=2.116, 95%CI: 1.247, 3.584) showed positive associations with diagnosed GDM. In contrast, among those carrying female fetuses, moderate perfluorooctanesulfonic acid (PFOS) exposure was associated with a lower risk of GDM (OR=0.542, 95%CI: 0.311, 0.946). The gWQS results demonstrated that PFOS (0.48) and perfluorobutane sulfonate (PFBS) (0.34) contributed the most to the mixture effect. Conclusion Early-pregnancy exposure to PFAS (particularly PFHxS and PFNA) may be an etiological factor for GDM, with maternal age and fetal sex potentially acting as effect modifiers in this association.

Objective: The cross sectional study aimed to identify predominant co-occurrence patterns among six common mental health issues in college students, so as to provide empirical basis for designing targeted interventions. Methods: From October 2024, a total of 9 837 students from 4 universities in Xiangtan City, Hunan Province, participated in the current study by multistage random cluster sampling method. Participants completed self report measures, including the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7 item Scale (GAD-7), Young s Internet Addiction Diagnostic Questionnaire, the Adolescent Insomnia Symptom Self rating Scale, the Ottawa Self injury Inventory, and the Brief Community Assessment of Psychic Experiences Questionnaire. Demographic and co-occurrence characteristics were first compared using Chi square or trend Chi-square tests, followed by application of the Apriori algorithm to mine association rules for primary co-occurrence patterns. Results: The detection rate of co-occuring the common mental health issues was 46.44%. The detection rate was significantly higher in female than in male students (50.42%, 43.61%; χ 2=44.46) and in students from rural versus urban areas (47.22%, 44.60%; χ 2=5.67) (both P <0.05). Significant differences were observed among freshmen, sophomores, juniors, and seniors (46.63%, 48.35%, 45.05% , 43.66%, respectively; χ 2=9.22, P <0.05), although no statistically significant trend was detected ( χ 2 trend =3.75, P = 0.05 ). Association rule mining identified “anxiety + depression” “anxiety + psychotic experiences + depression” and “anxiety + sleep disorder + depression” as the combinations with the highest support. In addition, “anxiety+depression+Internet addiction+psychotic experiences =>sleep disorder (>= refered to the occurrence of the latter item under the condition that the former item occurs)” and “anxiety + depression+Internet addiction=>sleep disorder” were combinations with relatively high confidence. Conclusions Co-occurrence of these mental health issues among college students is high and exhibits diverse patterns. Strategies to address this burden should prioritize integrated interventions that target these specific combinations of factors.

Ankylosing spondylitis is a chronic immunoinflammatory disease that mainly affects the spine and the sacroiliac joint， the mechanism of which is closely related to signaling pathways， such as osteoprotegerin （OPG）/receptor activator of nuclear factor-κB （RANK）/RANK ligand， mitogen-activated protein kinase （MAPK）， Wnt/β-catenin （β-catenin）， phosphoinositide 3- kinase/protein kinase B/mammalian target of rapamycin （PI3K/Akt/mTOR）. Traditional Chinese medicine has the characteristics of multiple components and targets， and is widely used for the treatment of autoimmune diseases due to its low toxicity， strong specificity， and high efficacy. This review found that monomers and compounds of traditional Chinese medicine can exert anti ankylosing spondylitis effects by intervening in the aforementioned signaling pathways， regulating immune inflammatory responses， and inhibiting biological processes such as bone destruction， ectopic osteogenic differentiation， cell apoptosis， and autophagy.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

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.

Objective: To explore the therapeutic effects of different surgical methods for temporomandibular joint disc reduction and anchoring surgery, providing reference for optimizing this surgical procedure. Method: The study was approved by the hospital ethics committee. 173 patients (195 joints) who underwent temporomandibular joint disc repositioning and anchoring surgery were selected for retrospective analysis. Patients were categorized into groups A (traditional preauricular incision-scalpel/tissue scissors anterior attachment release), 35 patients (40 joints), B (traditional preauricular incision-plasma bipolar radiofrequency electrode anterior attachment release), 42 patients (46 joints), C (revised tragus incision - scalpel/tissue scissors anterior attachment release), 50 patients (58 joints), and D (revised tragus incision-plasma bipolar radiofrequency electrode anterior attachment release), 46 patients (51 joints). After a 6-month postoperative follow-up, the differences in maximum mouth opening (MMO), visual analogue scale (VAS), effective rate of joint disc reduction, incidence of preauricular numbness, obvious scars among patients in each group at 1, 3, and 6 months were compared postoperatively. Results: After surgery, the MMO of all four groups of patients initially shrunk and then gradually increased compared to before surgery. At the 1-month follow-up after surgery, the plasma bipolar radiofrequency release (B+D) group had a smaller impact on the patient’s MMO compared to the surgical knife/tissue scissors release (A+C) group (P < 0.05). Postoperative VAS scores for all four groups showed a gradual decrease from pre-operative levels, with the (B+D) group scoring significantly lower in the first month post-surgery compared to the (A+C) group (P < 0.05). Six months post-surgery, the rate of joint disc reduction of the four groups were higher than 95%, with no significant differences observed between the groups (P > 0.05). Patients in the revised tragus incision (C+D) group experienced a lower rate of preauricular numbness compared to those in the traditional preauricular incision (A+B) group (4.59% vs. 12.79%, P < 0.05), The incidence of obvious scars in the (C+D) group was significantly lower than that in the (A+B) group (3.67% vs. 23.26%, P < 0.05). Conclusion The revised tragus incision is superior to traditional preauricular incision in terms of protecting the auriculotemporal nerve and the scars were more inconspicuous. Further, the plasma bipolar radiofrequency electrode is superior to the scalpel/tissue scissors in terms of mouth opening recovery and pain control. For temporomandibular joint disc reduction and anchoring surgery, a modified tragus incision combined with plasma bipolar radiofrequency electrode to release the anterior attachment of the joint disc can be recommended as a surgical option.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

Major depressive disorder (MDD) is a prevalent psychiatric condition characterized by high rates of prevalence and recurrence. Repetitive transcranial magnetic stimulation (rTMS) has been widely utilized in the acute phase treatment of MDD and has shown potential in maintenance therapy. However, the factors influencing the efficacy of rTMS during the acute phase and the sustainability of its long-term therapeutic effects remain incompletely understood. Additionally, a consensus on the optimal maintenance treatment protocol has yet to be established. This article reviews recent evidence on the application of rTMS in both acute and maintenance treatment of MDD, aiming to summarize current research progress and provide insights for the further promotion and application of rTMS in clinical practice.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.