ObjectiveTo observe the effects of Xiaoyaosan on glucagon-like peptide-1 （GLP-1）/GLP-1 receptor （GLP-1r） and protein kinase A （PKA）/cAMP response element binding protein （CREB）/brain-derived neurotrophic factor （BDNF） signaling pathways in the hippocampal CA1 region of rats under chronic restraint stress （CRS），and to explore the mechanism of this formula to alleviate anxiety and depression-like behaviors. Methods40 specific pathogen-free male Sprague-Dawley （SD） rats were randomly divided into normal，model，Xiaoyaosan，and fluoxetine groups，with 10 rats in each group. CRS was used to induce anxiety and depression-like behaviors. The rats in the Xiaoyaosan group were gavaged with aqueous solution of traditional Chinese medicine formula granules （7.36 g·kg^-1·d^-1），while those in the fluoxetine group were gavaged with aqueous solution of fluoxetine （2 mg·kg^-1·d^-1）. Body weight was measured on days 0，7，14，and 21 of the experiment. On days 0 and 22 of the experiment，the sucrose preference test （SPT），forced swimming test （FST），and open field test （OFT） were performed. The pathological morphology of the hippocampal CA1 region was observed by Nissl staining. The relative mRNA expression of post-synaptic density protein-95 （PSD95） and synapsin （SYP） was detected by reverse transcription quantitative real-time polymerase chain reaction. Immunohistochemistry and Western blot were used to detect expression of proteins in the GLP-1/GLP-1r and PKA/CREB/BDNF pathways in the hippocampal CA1 region. ResultsAfter CRS modeling，compared with the normal group，the rats of the model group had anxiety and depression-like behavioral manifestations，neuronal damage in the hippocampal CA1 region，significantly downregulated expression of synaptic plasticity markers PSD95 and SYP genes （P<0.01），and inhibition of GLP-1/GLP-1r and PKA/CREB/BDNF signaling pathways （P<0.05，P<0.01）. Compared with the model group，the Xiaoyaosan group exhibited alleviated anxiety and depression-like behaviors，reduced neuronal damage in the hippocampal CA1 region， significantly increased expression of PSD95 and SYP genes （P<0.01），and the activation of the GLP-1/GLP-1r and PKA/CREB/BDNF signaling pathways （P<0.05，P<0.01）. ConclusionXiaoyaosan can alleviate anxiety and depression-like behaviors in CRS rats by improving synaptic plasticity in the hippocampal CA1 region. The mechanisms may be related to the activation of the GLP-1/GLP-1r pathway and its mediated PKA/CREB/BDNF signaling pathway by the formula.

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.

It has been demonstrated that long-term space flights have a significantly greater impact on the cardiovascular, skeletal, and nervous systems of astronauts. The structural and functional alterations in the skeletal and muscular systems resulting from exposure to weightlessness can lead to the development of low back pain, significantly impairing the ability of astronauts to perform tasks and respond to emergencies. Both space flight and simulated microgravity have been shown to result in low back pain among astronauts, with the following factors identified as primary contributors to this phenomenon. The occurrence of intervertebral disc (IVD) edema results in the stimulation of type IV mechanoreceptors, which subsequently activate nociceptive afferents. The protrusion of an IVD causes compression of the spinal nerve roots. Furthermore, the elongation of the vertebral column and/or the diminished lumbar curvature of the spine exert traction on the dorsal root nerves. Paravertebral muscle degeneration leads to the inhibition of decreased nociceptive activity of the wide-dynamic range neurons of the spinal dorsal horn. Moreover, endogenous pain descending facilitation triggered by conditioning stimulation can be enhanced via the thalamic mediodorsal nuclei, while endogenous pain descending inhibition triggered by conditioning stimulation can be weakened via the thalamic ventromedial nuclei. Psychological factors may contribute to the development of low back pain. The mechanisms governing the generation, maintenance, and alleviation of low back pain in weightlessness differ from those observed in normal gravitational environments. This presents a significant challenge for space medicine research. Therefore, the elucidation of the occurrence and development mechanism of low back pain in weightlessness is important for the prevention and treatment during space flight. To reduce the incidence of low back pain during long-term missions on the space station, astronauts may choose to wear specialized space clothing that can provide axial physiological loads, designed to stimulate both musculature and skeletal structures, mitigating potential increases in vertebral column length, diminished lumbar curvature, and intervertebral disc edema and/or muscular atrophy. Additionally, assuming a “fetal tuck position” described as the knees to chest position may increase lumbar IVD hydrostatic pressure, subsequently reducing disc volume, rectifying diminished lumbar curvature, and alleviating dorsal root nerve tensions. Moreover, this position may reduce type IV mechanoreceptor facilitation and nerve impulse propagation from the sinuvertebral nerves of the annulus fibrosus. Elongated posterior soft tissues (apophyseal joint capsules and ligaments) with spinal flexion may potentially stimulate type I and II mechanoreceptors. It is also recommended to exercise the paraspinal muscles to prevent and alleviate the decrease in their cross-sectional area and maintain their structure and function. Photobiomodulation has been proved to be an effective means of activating the pain descending inhibition pathway of the central nervous system. In addition, astronauts should be encouraged to participate in mission-related activities and strive to avoid psychological problems caused by the long-term confinement in a small space station. The article presents a concise review of potential causes and targeted treatment strategies for low back pain induced by space flight or simulated microgravity in recent years. Its objective is to further elucidate the mechanisms underlying the occurrence and development of low back pain in weightless environments while providing scientific evidence to inform the development of guidelines for preventing, treating, and rehabilitating low back pain during long-term space flights.

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.

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.

Objective To explore enteral nutrition support and analyze its influencing factors in elderly patients with ischemic stroke. Methods A total of 328 patients with ischemic stroke in General Hospital of Western Theater Command were enrolled for nutritional screening between July 2020 and February 2024. Corresponding nutritional support plans were selected to investigate the compliance of patients with enteral nutrition support. Patients were divided into a standard group (n=140) and a non-standard group (n=97) based on whether their calorie intake met the standard. The effects of different clinical characteristics on enteral nutrition support were explored, and logistic analysis was used to analyze the influencing factors of non-standard enteral nutrition support. Results In the 328 patients with ischemic stroke, proportions of total parenteral nutrition support, total enteral nutrition support, and parenteral/enteral nutrition support were 25.30%, 27.74% and 46.95%, respectively. The proportions of vomiting or regurgitation, gastric residual volume >100 mL, mechanical ventilation and use of antibiotics >2 in the non-standard group were higher than those in the standard group (P<0.05). Logistic analysis showed that the above clinical characteristics were risk factors influencing patients with enteral nutrition support and parenteral/enteral nutrition support. Conclusion Vomiting or regurgitation , gastric residual volume, mechanical ventilation, and amount of antibiotics used are important influencing factors of enteral nutrition support in patients. Clinicians should pay attention to the above clinical characteristics.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

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.