ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Hepatic alveolar echinococcosis is a highly invasive zoonotic parasitic disease with poor prognosis. Surgical intervention serves as the pivotal approach to achieve radical cure and improve the prognosis of hepatic alveolar echinococcosis patients. In recent years, with the popularization of the concept of precision surgery and the development of the multidisciplinary diagnosis and treatment model, the surgical treatment strategies for hepatic alveolar echinococcosis have been continuously enriched, and the selection of surgical procedures has become increasingly diversified. Although key surgical techniques such as radical hepatectomy, autologous liver transplantation and allogeneic liver transplantation have achieved remarkable progress in clinical application, many insurmountable challenges still remain. Therefore, by sorting out the latest evidence-based advances in the field of surgical treatment for hepatic alveolar echinococcosis, this article focuses on discussing the application status and bottlenecks of radical hepatectomy, autologous liver transplantation and allogeneic liver transplantation in hepatic alveolar echinococcosis, aiming to provide a reference for the clinical treatment of hepatic alveolar echinococcosis.

Objective To investigate the effect of health failure mode and effect analysis(HFMEA)in optimizing the management process of postoperative diabetes insipidus in children undergoing neurosurgery.Methods Based on HFMEA,a management flowchart for postoperative diabetes insipidus in children undergoing neurosurgery was created.Brainstorming was adopted to identify failure modes in the workflow,analyze risk factors,and develop improvement measures,thereby refining the management flowchart.The amelioration and prognosis of diabetes insipidus in these children before(October 2022 to November 2023)and after(January 2024 to February 2025)implementation of the management flowchart were compared.Results The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery alleviated the symptoms of diabetes insipidus regarding the number of diabetes insipidus in the pediatric intensive care unit(P=0.006),the average daily urine output in the pediatric intensive care unit(P=0.001),the proportion of electrolyte abnormalities at discharge/transfer(P=0.037),the duration of mechanical ventilation(P=0.007),and the length of stay in the intensive care unit(P=0.001).Conclusion The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery is beneficial to the optimization of the management process,the alleviation of postoperative diabetes insipidus,and the improvement of prognosis in these children.
Humans ; Diabetes Insipidus/etiology* ; Neurosurgical Procedures/adverse effects* ; Child ; Postoperative Complications/therapy* ; Healthcare Failure Mode and Effect Analysis ; Intensive Care Units, Pediatric ; Risk Factors

Integrated metabolomic and lipidomic profiling,utilizing liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS),has emerged as a pivotal strategy for biomarker discovery.However,the inherent polarity disparity between metabolites and lipids complicates simultaneous analysis.To address this,a dual-stationary phase polarity-extended liquid chromatography(PELC)system was developed,which surpassed conventional one-dimensional LC(1D-LC)by enabling comprehensive coverage of both polar and non-polar compounds within a single injection.This system enhanced chromatographic resolution,peak capacity,and throughput while minimizing analytical variability.Extracellular vesicles(EVs),lipid bilayer-enclosed nanoparticles ubiquitously present in biofluids,had gained prominence as reservoirs of cancer biomarkers due to their cargo stability and pathophysiological relevance.Herein,the application of PELC-HRMS for concurrent metabolome-lipidome profiling in EVs was pioneered.A total of 193 metabolites were identified using this technique coupled with MS-DIAL software and Human Metabolome Database.Subsequently,this technique was employed to explore potential biomarkers for prostate cancer(PCa).Multivariate analysis identified 17 differentially abundant metabolites in PCa,implicating dysregulated pathways including purine metabolism,starch and sucrose metabolism,galactose metabolism,cysteine and methionine metabolism,and biosynthesis of unsaturated fatty acids.Notably,creatine(AUC=0.92)and DG 42:5(AUC=0.80)demonstrated robust diagnostic efficacy,attributable to their broad polarity ranges and EV-specific enrichment.This study established PELC as a high-fidelity platform for multi-omics integration in complex biospecimens,advancing mechanistic insights into metabolic rewiring and disease pathophysiology.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective:To analyze the relationship between 24-hour urinary calcium (24 h UCa) level and the risk of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and all-cause mortality.Methods:In the Chinese Cohort Study of Chronic Kidney Disease, we examined 3 375 patients aged 18-74 years with CKD stages 1-4. Kaplan-Meier survival and Cox proportional hazard regression models were used to test a time-to-event association between levels of 24 h UCa and incidence of ESKD, CVD, and all-cause mortality.Results:During a follow-up of 4.17 (3.37, 5.20) years, 179, 145, 104 and 38 ESKD events occurred in <0.60, 0.60-, 1.20-, ≥2.32 mmol 24 h UCa groups. Higher levels of 24 h UCa (1.20-,≥2.32 mmol) were independently associated with a lower incidence of ESKD events in patients with CKD, with HR (95% CI) of 0.71 (0.54-0.93) and 0.43 (0.29-0.64), respectively. No significant associations with CVD and all-cause mortality endpoints were detected. Conclusion:Among patients with CKD, levels of 24 h UCa displayed an association with the risk of ESKD among patients with CKD stages 1-4.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Psoriasis is an incurable chronic inflammatory disease that requires new interventions. Here, we found that fibroblasts exacerbate psoriasis progression by promoting macrophage recruitment via CCL2 secretion by single-cell multi-omics analysis. The natural small molecule celastrol was screened to interfere with the secretion of CCL2 by fibroblasts and improve the psoriasis-like symptoms in both murine and cynomolgus monkey models. Mechanistically, celastrol directly bound to the low-density lipoprotein receptor-related protein 1 (LRP1) β-chain and abolished its binding to the transcription factor c-Jun in the nucleus, which in turn inhibited CCL2 production by skin fibroblasts, blocked fibroblast-macrophage crosstalk, and ameliorated psoriasis progression. Notably, fibroblast-specific LRP1 knockout mice exhibited a significant reduction in psoriasis like inflammation. Taken together, from clinical samples and combined with various mouse models, we revealed the pathogenesis of psoriasis from the perspective of fibroblast-macrophage crosstalk, and provided a foundation for LRP1 as a novel potential target for psoriasis treatment.

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis