ObjectiveThe study explores the influence of voluntary wheel running on the behavioral abnormalities and the activation state of the hypothalamic-pituitary-adrenal (HPA) axis in autism spectrum disorder (ASD) rats through gut microbiota. MethodsSD female rats were selected and administered either400 mg/kg of valproic acid (VPA) solution or an equivalent volume of saline via intraperitoneal injection on day 12.5 of pregnancy. The resulting offspring were divided into 2 groups: the ASD model group (PASD, n=35) and the normal control group (PCON, n=16). Behavioral assessments, including the three-chamber social test, open field test, and Morris water maze, were conducted on postnatal day 23. After behavioral testing, 8 rats from each group (PCON, PASD) were randomly selected for serum analysis using enzyme-linked immunosorbent assay (ELISA) to measure corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) concentration, to evaluate the functional state of the HPA axis in rats. On postnatal day 28, the remaining 8 rats in the PCON group were designated as the control group (CON, n=8), and the remaining 27 rats in the PASD group were randomly divided into 4 groups: ASD non-intervention group (ASD, n=6), ASD exercise group (ASDE, n=8), ASD fecal microbiota transplantation group (FMT, n=8), and ASD sham fecal microbiota transplantation group (sFMT, n=5). The rats in the ASD group and the CON group were kept under standard conditions, while the rats in the ASDE group performed 6 weeks of voluntary wheel running intervention starting on postnatal day 28. The rats in the FMT group were gavaged daily from postnatal day 42 with 1 ml/100 g fresh fecal suspension from ASDE rats which had undergone exercise for 2 weeks, 5 d per week, continuing for 4 weeks. The sFMT group received an equivalent volume of saline. After the interventions were completed, behavioral assessments and HPA axis markers were measured for all groups. ResultsBefore the intervention, the ASD model group exhibited significantly reduced social ability, social novelty preference, spontaneous activity, and exploratory interest, as well as impaired spatial learning, memory, and navigation abilities compared to the normal control group (P<0.05). Serum concentration of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) in the PASD group were significantly higher than those in the PCON group (P<0.05). Following 6 weeks of voluntary wheel running, the ASDE group showed significant improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, spatial learning, memory, and navigation skills compared to the ASD group (P<0.05), with a significant decrease in serum CORT concentration (P<0.05), and a downward trend in CRH and ACTH concentration. After 4 weeks of fecal microbiota transplantation in the exercise group, the FMT group showed marked improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, as well as spatial learning, memory, and navigation abilities compared to both the ASD and sFMT groups (P<0.05). In addition, serum ACTH and CORT concentration were significantly reduced (P<0.05), and CRH concentration also showed a decreasing trend. ConclusionExercise may improve ASD-related behaviors by suppressing the activation of the HPA axis, with the gut microbiota likely playing a crucial role in this process.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective: To reveal the molecular biological mechanism of a rare Dc-variant individual using PacBio third-generation sequencing technology. Methods: ABO and Rh blood type identification, DAT, unexpected antibody screening and D antigen enhancement test were conducted by serological testing. The absorption-elution test was used to detect the e antigen. RHCE gene typing was performed by PCR-SSP, and the 1-10 exons of RHCE were sequenced by Sanger sequencing. The full-length sequences of RHCE, RHD and RHAG were detected by PacBio third-generation sequencing technology. Results: Serological findings: Blood type O, Dc-phenotype, DAT negative, unexpected antibody screening negative; enhanced D antigen expression; no detection of e antigen in the absorption-elution test. PCR-SSP genotyping indicated the presence of only the RHCE c allele. Sanger sequencing results: Exons 5-9 of RHCE were deleted, exon 1 had a heterozygous mutation at c. 48G/C, and exon 2 had five heterozygous mutations at c. 150C/T, c. 178C/A, c. 201A/G, c. 203A/G and c. 307C/T. Third-generation sequencing results: RHCE genotype was RHCE 02N. 08/RHCE-D(5-9)-CE; RHD genotype was RHD 01/RHD 01; RHAG genotype was RHAG 01/RHAG 01 (c. 808G>A and c. 861G>A). Conclusion: This Dc-individual carries the allele RHCE 02N. 08 and the novel allele RHCE-D(5-9)-CE. The findings of this study provide data support and a theoretical basis for elucidating the molecular mechanisms underlying RhCE deficiency phenotypes.

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.

OBJECTIVE To optimize the simmering technology of Rheum palmatum from Menghe Medical School and compare the difference of chemical components before and after processing. METHODS Using appearance score， the contents of gallic acid， 5-hydroxymethylfurfural （5-HMF）， sennoside A+sennoside B， combined anthraquinone and free anthraquinone as indexes， analytic hierarchy process （AHP）-entropy weight method was used to calculate the comprehensive score of evaluation indicators； the orthogonal experiment was designed to optimize the processing technology of simmering R. palmatum with fire temperature， simmering time， paper layer number and paper wrapping time as factors； validation test was conducted. The changes in the contents of five anthraquinones （aloe-emodin， rhein， emodin， chrysophanol， physcion）， five anthraquinone glycosides （barbaloin， rheinoside， rhubarb glycoside， emodin glycoside， and emodin methyl ether glycoside）， two sennosides （sennoside A， sennoside B）， gallic acid and 5-HMF were compared between simmered R. palmatum prepared by optimized technology and R. palmatum. RESULTS The optimal processing conditions of R. palmatum was as follows: each 80 g R. palmatum was wrapped with a layer of wet paper for 0.5 h， simmered on high heat for 20 min and then simmered at 140 ℃， the total simmering time was 2.5 h. The average comprehensive score of 3 validation tests was 94.10 （RSD＜1.0%）. After simmering， the contents of five anthraquinones and two sennosides were decreased significantly， while those of 5 free anthraquinones and gallic acid were increased to different extents； a new component 5-HMF was formed. CONCLUSIONS This study successfully optimizes the simmering technology of R. palmatum. There is a significant difference in the chemical components before and after processing， which can explain that simmering technology slows down the relase of R. palmatum and beneficiate it.

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.

Objective To explore the potential mechanisms of hypoxic adaptive metabolic changes in the kidneys of plateau pikas at different altitudes using non-targeted metabolomics analysis via ultra-high-performance liquid chromatography coupled with quadrupole electrostatic field orbital trap-mass spectrometry (UHPLC-QE-MS). Methods 10 plateau pikas were captured at an altitude of 4 360 m in Xingxiuhai area, Maduo County, Guoluo Tibetan Autonomous Prefecture, Qinghai Province (MD group), and 10 plateau pikas were captured at an altitude of 2 900 m in Menyuan area, Haibei Tibetan Autonomous Prefecture, Qinghai Province (MY group). After anesthesia, serum samples were collected, and kidney samples were collected after euthanasia. General physiological and biochemical indicators were measured and metabolomics analysis was performed. Part of the serum samples was used for hematology analysis, another part for blood gas analysis, and the remaining part for biochemical indicator detection. Metabolites were extracted from the kidney tissue samples and then analyzed using UHPLC-QE-MS. Differential metabolites were analyzed using metabolomics principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), with screening criteria set as variable importance in projection (VIP)>1.5 and fold change (FC)>1.5, or VIP>1.5 and FC<1/1.5. Correlation analysis heatmaps, significance analysis volcano plots, signaling pathway recognition bubble charts, and rectangular graphs were used for the analysis of differential metabolites and related signaling pathways. Results The red blood cell count, glucose, urea nitrogen, uric acid, and homocysteine levels in the MD group plateau pikas were higher than those in the MY group, while hemoglobin, hematocrit, creatinine, and carbon dioxide combining power were lower than those in the MY group. This indicated a significant difference in the blood oxygen-carrying capacity of plateau pikas at different altitudes. The principal component pattern recognition analyses, and OPLS-DA permutation test showed that the kidney metabolites of the MD and MY groups of plateau pikas had distinct clustering distributions (R²Y=0.930, Q²=0.655). According to the screening criteria and database comparison, 46 differential metabolites were identified in the kidneys of plateau pikas at different altitudes. In the MD group of plateau pikas, the expression levels of bufadienolide, adenosine, adenine, diosgenin, berberine chloride, carnosol, and astaxanthin were significantly increased (VIP>1.5, P<0.05), while the levels of arachidonic acid, histamine, and coumarin were significantly decreased (VIP>1.5, P<0.05). The analysis of related signaling pathways showed that the biosynthetic pathways of valine, leucine, and isoleucine had the largest impact factors (P<0.05), while the biosynthetic pathways of pantothenate and coenzyme A showed the most significant enrichment (P<0.05). Conclusion The differential metabolites of amino acids, pantothenate, and coenzyme A pathways in the kidneys of plateau pikas at different altitudes may be involved in the metabolic mechanisms of plateau pikas' hypoxia adaptation in high-altitude environments.

Tumor immune homeostasis is a dynamic equilibrium state in which the body removes abnormal mutated cells in time to prevent tumor development without damaging other normal cells under the surveillance of the immune system. It is an important concept to understand the process of tumor development. Programmed cell death （PCD） is a kind of regulable cell death including various forms such as apoptosis， autophagy， pyroptosis， necrosis， and ferroptosis. It is regarded as an important way for the body to remove abnormal or mutated cells. In recent years， modern research has found that PCD has a bi-directional regulatory effect on carcinogenesis and tumor development. In the early stage of tumor formation， PCD can control tumor development in time by playing a specific immune clearance role， while in the later tumorigenic stage， PCD can promote the growth and development of tumor cells by forming a tumor-specific microenvironment， resulting in carcinogenic effects. Therefore， PCD is regarded as an important way to maintain tumor immune homeostasis. Based on the idea of ''supporting the vital Qi and cultivating the root'' by professors Yu Guiqing and Piao Bingkui， the team proposed the theory of ''regulating Qi and resolving toxins'' and applied it to clinical tumor prevention and treatment. Based on the theory of ''regulating Qi and resolving toxins''， the research summarized the current progress of modern medical research on mechanisms related to PCD to explore the role of PCD in the regulation of tumor immune homeostasis. The article believed that the harmonious state of Qi movement was the basic condition for normal PCD to maintain tumor immune homeostasis， while the disorder of Qi movement and the evolution of tumor toxicity were the core processes of abnormal PCD and disorder of tumor immunity homeostasis， which led to the escape and development of tumor cells. Therefore， under the guidance of ''regulating Qi and removing toxins''， the idea of full-cycle prevention and treatment of tumors was proposed summarily. In the early stage of tumor formation， the method of ''regulating Qi movement and strengthening vital Qi'' was applied to reestablish tumor immune homeostasis and to promote the elimination of abnormal cells. In the late tumorigenic stage， the method of ''resolving toxins and dispelling evils'' was applied to reverse the specific microenvironment of tumors and inhibit the development of tumor cells， with a view to providing new theoretical support for the prevention and treatment of tumors through traditional Chinese medicine.

Tumor immune homeostasis is a dynamic equilibrium state in which the body removes abnormal mutated cells in time to prevent tumor development without damaging other normal cells under the surveillance of the immune system. It is an important concept to understand the process of tumor development. Programmed cell death （PCD） is a kind of regulable cell death including various forms such as apoptosis， autophagy， pyroptosis， necrosis， and ferroptosis. It is regarded as an important way for the body to remove abnormal or mutated cells. In recent years， modern research has found that PCD has a bi-directional regulatory effect on carcinogenesis and tumor development. In the early stage of tumor formation， PCD can control tumor development in time by playing a specific immune clearance role， while in the later tumorigenic stage， PCD can promote the growth and development of tumor cells by forming a tumor-specific microenvironment， resulting in carcinogenic effects. Therefore， PCD is regarded as an important way to maintain tumor immune homeostasis. Based on the idea of ''supporting the vital Qi and cultivating the root'' by professors Yu Guiqing and Piao Bingkui， the team proposed the theory of ''regulating Qi and resolving toxins'' and applied it to clinical tumor prevention and treatment. Based on the theory of ''regulating Qi and resolving toxins''， the research summarized the current progress of modern medical research on mechanisms related to PCD to explore the role of PCD in the regulation of tumor immune homeostasis. The article believed that the harmonious state of Qi movement was the basic condition for normal PCD to maintain tumor immune homeostasis， while the disorder of Qi movement and the evolution of tumor toxicity were the core processes of abnormal PCD and disorder of tumor immunity homeostasis， which led to the escape and development of tumor cells. Therefore， under the guidance of ''regulating Qi and removing toxins''， the idea of full-cycle prevention and treatment of tumors was proposed summarily. In the early stage of tumor formation， the method of ''regulating Qi movement and strengthening vital Qi'' was applied to reestablish tumor immune homeostasis and to promote the elimination of abnormal cells. In the late tumorigenic stage， the method of ''resolving toxins and dispelling evils'' was applied to reverse the specific microenvironment of tumors and inhibit the development of tumor cells， with a view to providing new theoretical support for the prevention and treatment of tumors through traditional Chinese medicine.

To summarize the clinical experience of Professor LIU Shangyi in treating pruritus vulvae. It is believed that women have the physiological characteristics of liver and kidney as the root， and their pubic area is easily attacked by wind-dampness pathogenic qi， so the core mechanism of pruritus vulvae is proposed as wind-dampness accumulation and deficiency of liver and kidney. The core treatment method is to dispel wind-dampness and nourish the liver and kidneys， and modify the Danggui Decoction （当归饮子） to form a self-prescribed Yinyang Formula （阴痒方） as the basic prescription to treat pruritus vulvaen.