ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveTo investigate the mechanism by which Wumeiwan suppresses the development and progression of colorectal cancer（CRC） through the regulation of fatty acid metabolic reprogramming， thereby providing new experimental evidence for the prevention and treatment of CRC. MethodsA total of 120 C57BL/6 mice were randomly divided into the blank group， model group， Wumeiwan high-， medium-， and low-dose groups（54， 27， 13.5 g·kg^-1）， and the mesalazine group（0.01 g·kg^-1）， with 20 mice in each group. Except for the blank group， all mice were subjected to azoxymethane（AOM）/dextran sulfate sodium（DSS） treatment to establish an inflammation-associated CRC model. One week after AOM injection， mice in the treatment groups received intragastric administration of the designated drugs， while the blank and model groups received an equal volume of purified water， continuing until 20 d after the intervention endpoint. Hematoxylin-eosin（HE） staining was used to observe colonic histopathological alterations， and immunohistochemistry for vascular endothelial growth factor（VEGF） was performed to evaluate neovascularization and tumor invasion. Metabolomics combined with Kyoto Encyclopedia of Genes and Genomes（KEGG） and metabolite set enrichment analysis（MSEA） was applied to identify key CRC-related metabolic pathways， which were further validated by transcriptomic Gene Ontology（GO） enrichment and gene heatmap analysis. Subsequently， Western blot was performed to determine the expression levels of core proteins in these pathways， and immunofluorescence was used to analyze their localization and co-expression patterns in tissues， thereby elucidating the mechanism of Wumeiwan from multiple biological dimensions. ResultsCompared with the blank group， mice in the model group exhibited a significant decrease in body weight and a significant increase in the disease activity index（DAI） score（P<0.05）， with pronounced colonic mucosal damage accompanied by aggravated tumor invasion. Compared with the model group， Wumeiwan intervention markedly improved body weight loss and reduced DAI score， attenuated mucosal injury， and significantly decreased VEGF expression level（P<0.05）. Multi-omics analysis revealed that differential metabolites and genes across groups were commonly enriched in fatty acid metabolism， fatty acid biosynthesis， and other lipid-related pathways. Relative to the blank group， the model group showed significant upregulation levels of fatty acid synthesis-related genes， including sterol regulatory element-binding protein 1（SREBP1）， fatty acid synthase（FASN）， stearoyl-CoA desaturase 1（SCD1）， as well as saturated fatty acids（P<0.05）. Compared with the model group， treatment with Wumeiwan significantly reduced the expression of key genes involved in fatty acid metabolic pathways， including SREBP1， FASN， and SCD1（P<0.05）. Western blot results further confirmed that proteins in this pathway were significantly elevated in the model group， whereas they were markedly downregulated following Wumeiwan treatment（P<0.05）. Immunofluorescence analysis demonstrated enhanced co-localization of SREBP1 with the cancer-associated fibroblast（CAF） marker α-smooth muscle actin（SMA） in the model group， whereas this co-localization signal was attenuated after Wumeiwan intervention（P<0.05）. ConclusionWumeiwan can improve survival outcomes and alleviate colonic pathological damage in CRC mice， its therapeutic mechanism may be closely associated with the regulation of fatty acid metabolic reprogramming mediated by the SREBP1/FASN/SCD1 signaling pathway.

ObjectiveCell adhesion is a critical process that regulates cellular physiological functions. Quantitative characterization of adhesion dynamics is essential for elucidating the intrinsic mechanical mechanisms underlying cellular activities. Although atomic force microscopy-based single-cell force spectroscopy is widely used for single-cell adhesion measurements, it requires complex chemical modifications for preparation of live-cell probes, leading to limitations such as cumbersome operation, low throughput, and potential impacts on cell viability. Fluidic force microscopy, which combines atomic force microscopy with microfluidic probes, is a technique allowing the operation of force-controlled nanopipettes in aqueous environments. By applying negative or positive pressure via a pressure controller, a single living cell can be captured onto or released from the cantilever under physiological conditions. This procedure offers a simple workflow and high assay throughput for single-cell adhesion measurements without the need for chemical functionalization. In this study, fluidic force microscopy-based single-cell force spectroscopy was adopted to achieve long-term quantitative characterization of single-cell adhesion dynamics in a simpler and more efficient manner, comparing the dynamic differences in adhesion establishment between two cell lines with different differentiation levels. MethodsHEK 293T and hTERT RPE-1 cells were non-invasively captured on the cantilever of a fluidic force microscope via its integrated microfluidic system during 40 h of adhesion culture. Cell-substrate detachment assays were performed, and force-distance curves were recorded to extract key mechanical adhesion parameters, including adhesion force, adhesion energy, and maximum detachment distance. These measurements were combined with real-time monitoring of cell spreading area to systematically characterize the dynamic evolution of single-cell adhesion. ResultshTERT RPE-1 cells rapidly entered a stable adhesion phase within 1 h after seeding, with both area-normalized adhesion force and area-normalized adhesion energy reaching peak values. In contrast, HEK 293T cells required 4 h to achieve stable adhesion. Subsequently, the adhesion force, adhesion energy and maximum detachment distance of hTERT RPE-1 and HEK 293T cells stabilized at approximately 240 nN vs. 30 nN, 2.2 pJ vs. 0.12 pJ and 6 μm vs. 4 μm, respectively. hTERT RPE-1 cells reached the peak of area-normalized adhesion parameters earlier than HEK 293T cells, with their peak area-normalized adhesion force and area-normalized adhesion energy being substantially elevated relative to HEK 293T cells. HEK 293T cells presented stronger linear correlations among adhesion energy, maximum detachment distance and adhesion force compared with hTERT RPE-1 cells. For both cell lines, cell spreading area exhibited a weak correlation with adhesion force. Whereas the area-normalized adhesion parameters of HEK 293T cells remained relatively constant throughout the adhesion process, hTERT RPE-1 cells exhibited elevated values in the early phase, followed by a gradual decline. These results indicated distinct dynamic adhesion patterns between the two cell types, with hTERT RPE-1 cells exhibiting stronger adhesion strength and higher adhesion efficiency. ConclusionIn this study, fluidic force microscopy-based single-cell force spectroscopy was successfully applied to perform long-term in situ quantitative measurement of the adhesion dynamics in single adherent cells. The approach revealed divergent adhesion patterns between HEK 293T and hTERT RPE-1 cells, suggesting a close association between cell differentiation and adhesion behaviors. These findings provide quantitative mechanical evidence for further understanding the underlying mechanisms of cell adhesion.

Objective: To identify the latent classes of acceptance and striving and their associated factors among nursing students, and to examine the mediating role of positive acceptance and striving in the association between self transcendence and subjective well being, so as to provide evidence for targeted interventions to improve subjective well being among nursing students. Methods: From July to October 2025, a convenience sample of 861 nursing students was recruited from five schools in Sichuan and Chongqing, China. Participants were surveyed using the Self transcendence Scale, the Positive Acceptance and Striving Scale, and the General Well being Scale. Latent profile analysis was conducted to identify profiles of positive acceptance and striving. Logistic regression analysis was used to examine associated factors, and relative mediation analysis was performed to assess differences in the mediating effect across latent profiles. Results: Three latent classes of acceptance and striving were identified: the low acceptance-low striving profile (46.34%, n =399), the moderate acceptance-moderate striving profile (40.88%, n =352), and the high acceptance-high striving profile (12.78%, n =110). Multivariable Logistic regression analysis showed that, with the low acceptance-low striving profile as the reference, nursing students with higher self transcendence scores ( OR =151.85) and those who studied nursing because of personal preference ( OR =14.72) or parental expectations ( OR =11.16) were more likely to be classified into the high acceptance-high striving profile (all P <0.05). Male students ( OR =0.19), those perceiving nurses social status as average ( OR =0.35), and those receiving moderate teacher support ( OR =0.23) were less likely to be classified into the high acceptance-high striving profile (all P <0.05). Positive acceptance and striving partially mediated the association between self transcendence and subjective well being, with a total effect of β =0.97 and a mediating effect of β =0.48; the mediating effect accounted for 49.34% of the total effect. Relative mediation analysis showed that the mediating effect was statistically significant only in the low acceptance-low striving profile ( β =0.21), whereas the direct effects were stronger in the moderate acceptance-moderate striving and high acceptance-high striving profiles ( β =0.39, 1.06)(all P <0.05). Conclusions Positive acceptance and striving among nursing students exhibit substantial heterogeneity. Its predictive effect on subjective well being and the underlying mediating mechanism vary across latent profiles. Profile specific interventions should therefore be implemented to improve subjective well being among nursing students.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To analyze the prevalence of the Dabie bandavirus among rats in Taipusi Banner, Xilingol League, Inner Mongolia Autonomous Region.Methods:The cytochrome b gene for the identification of rat species was amplified. Real-Time RT-PCR and RT-PCR were used to amplify and sequence DBV positive rat tissues and detected by XilinGol League Center for Disease Control and Prevention, and the gene evolution was analyzed.Results:The rat was identified as Spermophilus dauricus. Through molecular detection on 20 samples, 15 samples showed positive result in Real Time RT-PCR nucleic acid test, and 7 samples showed positive result in RT-PCR nucleic acid test. Seven base sequences were obtained through sequencing, and they were compared with those in the National Center for Biotechnology Information (NCBI) of the United States by BLAST. It was determined that they were the S gene sequences of DBV. It has been uploaded to GenBank with serial numbers PV231886, PV231887 and PV231888.Conclusions:DBV was detected for the first time in the mice of Inner Mongolia Autonomous Region. In future work, monitoring of DBV carried by rodents and ectoparasites such as ticks and fleas in this area should be strengthened. Publicity and education on severe fever with thrombocytopenia syndrome should be carried out in this area to improve the local medical system′s ability to identify such cases, ensuring early detection, early diagnosis, and early treatment, and reducing the occurrence of human cases and clustered outbreaks.

Objective To investigate the effects of peripheral blood neutrophil infiltration on the polarization regulation of cerebral resident microglia under a permanent ischemic stroke model.Methods Fifty-eight C57BL/6 mice were divided into two groups.One group was sham group,and the other group of mice was subjected to permanent middle cerebral artery occlusion surgery.Mice were euthanized 48 hours,7 days,14 days,and 30 days after surgery for tissue collection.Western blotting was used to detect expression levels of M1 microglia markers CD 16,M2 microglia marker arginase 1(Arg1),inflammatory cytokine interleukin-1 β(IL-1β),and neutrophil marker myeloperoxidase(MPO)in brain tissue.Immunofluorescence histochemical staining was used to assess neutrophil infiltration and M2 microglial distribution around the infarct area in brain sections.In vitro,purified neutrophils were co-cultured with BV2 microglial cells.After lipopolysaccharide stimulation,the phagocytosis of neutrophils by BV2 cells was observed,and the expression levels of CD16 and Arg1 proteins in BV2 cells were detected.Results Western blotting showed that the levels of CD16(P＜0.05),IL-1β(P＜0.001),and MPO(P＜0.05)in brain tissue increased significantly 48 hours and 7 days after surgery,then decreased,with MPO expression returning to normal levels 30 days after surgery.Immunofluorescence showed a significant increase of MPO-positive cells around the infarct area of the mouse cerebral cortex 48 hours after surgery(P＜0.001),followed by a decrease(P＜0.05).The number of ionized calcium binding adapter molecule 1(Iba1)and MPO double-positive cells gradually increased after surgery,and reached their peak at 14 days(P＜0.05).Iba1 and Arg1 double-positive cells also increased significantly 7 days(P＜0.05)and 14 days(P＜0.01)after surgery.In vitro,co-culture experiments showed that after BV2 phagocytosing neutrophils,CD 16(P＜0.05)significantly decreased and Arg1 significantly upregulated(P＜0.05).Conclusion In a permanent ischemic stroke model,microglia transition from M1 to M2 type after phagocytosing neutrophils,and the injured brain area changes from pro-inflammatory state to anti-inflammatory state.

Single-cell assay for transposase-accessible chromatin sequencing(scATAC-seq)is a power-ful technique for studying cellular heterogeneity and gene regulatory networks,widely applied in epigenet-ic research.However,the complexity of data analysis workflows and high programming requirements have limited its broader adoption among non-programmer researchers.To address this issue,we developed Sig-nac.UIO,a modular and visual scATAC-seq analysis platform based on the R Shiny framework,integra-ting mainstream tools such as Signac and Seurat.The platform includes ten key modules covering quality control,cell filtering,dimensionality reduction,clustering,differential analysis,cell annotation,path-way enrichment,motif analysis,and transcription factor footprinting.Through a graphical user interface,users can perform full analyses and obtain interactive visualization results.The platform's stability and u-tility have been validated using a public PBMC dataset and it is currently deployed online(https://xula-bgdpu.org.cn/Signac.UIO),providing an efficient and user-friendly tool for single-cell epigenomics re-search.

Developmental dysplasia of hip(DDH)usually occurs in children,and delayed diagnosis of DDH might lead to serious complications and influence long-term prognosis.The application of artificial intelligence(AI)in medical images helps to quantitatively individualize image data,reduce bias generated by manual analysis and achieve early and accurate diagnosis of children DDH.The research progresses of AI in imaging diagnosis of children DDH were reviewed in this article.