ObjectiveTo systematically characterize the in vitro chemical constituents of Entada phaseoloides and its absorbed prototype compounds and metabolites in a neuropathic pain （NP） model， thereby elucidating the potential pharmacological material basis underlying its analgesic activity. MethodsUltra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry （UHPLC-Q-Orbitrap HRMS） was used to analyze the constituents of the 80% methanol extract of E. phaseoloides. Thirty-six mice were divided into a blank group， low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， an aspirin group （400 mg·kg^-1）， and a morphine group （10 mg·kg^-1）， with six mice in each group. The tail-flick threshold was measured. Twenty-four mice were randomly divided into a blank group and low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， with six mice in each group， and motor coordination was evaluated. A spinal nerve ligation （SNL） model was established in mice. Twenty-four mice were randomly divided into a sham-operated （Sham） group， an SNL group， an SNL + E. phaseoloides extract group （750 mg·kg^-1）， and an SNL + pregabalin group （20 mg·kg^-1）， with 6 mice in each group. Mechanical allodynia and thermal hyperalgesia were evaluated. Behavioral assessments were combined to determine the optimal onset time of the analgesic effect， and plasma samples were collected at this time point. Absorbed components and metabolites were systematically identified based on accurate mass， MS/MS fragmentation patterns， retention time， and comparisons with literature data and reference standards. Meanwhile， network pharmacology， target similarity analysis， and molecular docking validation were integrated to evaluate the potential effects of absorbed components on disease intervention. ResultsCompared with the blank group， the high-dose E. phaseoloides extract group and morphine group showed significantly increased pain response thresholds （P<0.01）， without affecting motor coordination in normal mice. Compared with the Sham group， the SNL group exhibited significantly decreased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. Compared with the SNL group， the SNL + E. phaseoloides extract group and SNL + pregabalin group showed significantly increased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. The analgesic effect of E. phaseoloides was most significant at 2 h after administration. A total of 70 constituents were identified in vitro， and 22 prototype compounds and 29 metabolites were detected in plasma. The targets of the absorbed components were mainly enriched in biological processes associated with pain signal perception and transmission and showed a high degree of overlap with disease genes related to neuropathic pain. In addition， multiple components exhibited strong binding energies with core pain-related targets. Based on integrated analysis of in vitro content， plasma exposure levels， and target associations， ketanthamide A-β-D-pyranoside and ketengzide were identified as the most representative pharmacologically active substances responsible for the analgesic effect. ConclusionThis study systematically elucidates the material basis of E. phaseoloides intervention in neuropathic pain from three aspects， including chemical constituent characteristics， in vivo behavioral characteristics， and potential targets， providing important theoretical foundations and experimental support for further studies on its pharmacological mechanism and analgesic drug development.

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

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

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

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

Tumors exhibit abnormal glucose metabolism, consuming excessive glucose and excreting lactate, which constructs a tumor microenvironment that facilitates cancer progression and disrupts immunotherapeutic efficacy. Currently, tumor glucose metabolic dysregulation to reshape the immunosuppressive microenvironment and enhance immunotherapy efficacy is emerging as an innovative therapeutic strategy. However, glucose metabolism modulators lack specificity and still face significant challenges in overcoming tumor delivery barriers, microenvironmental complexity, and metabolic heterogeneity, resulting in poor clinical benefit. Nanomedicines, with their ability to selectively target tumors or immune cells, respond to the tumor microenvironment, co-deliver multiple drugs, and facilitate combinatorial therapies, hold significant promise for enhancing immunotherapy through tumor glucose metabolic reprogramming. This review explores the complex interactions between tumor glucose metabolism-specifically metabolite transport, glycolysis processes, and lactate-and the immune microenvironment. We summarize how nanomedicine-mediated reprogramming of tumor glucose metabolism can enhance immunotherapy efficacy and outline the prospects and challenges in this field.

OBJECTIVE: To explore the mechanism of colon dialysis with Yishen Decoction (YS) in improving the autophagy disorder of intestinal epithelial cells in chronic renal failure (CRF) in vivo and in vitro. METHODS: Thirty male SD rats were randomly divided into normal, CRF, and colonic dialysis with YS groups by a random number table method (n=10). The CRF model was established by orally gavage of adenine 200 mg/(kg•d) for 4 weeks. CRF rats in the YS group were treated with colonic dialysis using YS 20 g/(kg•d) for 14 consecutive days. The serum creatinine (SCr) and urea nitrogen (BUN) levels were detected by enzyme-linked immunosorbent assay. Pathological changes of kidney and colon tissues were observed by hematoxylin and eosin staining. Autophagosome changes in colonic epithelial cells was observed with electron microscopy. In vitro experiments, human colon cancer epithelial cells (T84) were cultured and divided into normal, urea model (74U), YS colon dialysis, autophagy activator rapamycin (Ra), autophagy inhibitor 3-methyladenine (3-MA), and SIRT1 activator resveratrol (Re) groups. RT-PCR and Western blot were used to detect the mRNA and protein expressions of zonula occludens-1 (ZO-1), Claudin-1, silent information regulator sirtuin 1 (SIRT1), LC3, and Beclin-1 both in vitro and in vivo. RESULTS: Colonic dialysis with YS decreased SCr and BUN levels in CRF rats (P<0.05), and alleviated the pathological changes of renal and colon tissues. Expressions of SIRT1, ZO-1, Claudin-1, Beclin-1, and LC3II/I were increased in the YS group compared with the CRF group in vivo (P<0.05). In in vitro study, compared with normal group, the expressions of SIRT1, ZO-1, and Claudin-1 were decreased, and expressions of Beclin-1, and LC3II/I were increased in the 74U group (P<0.05). Compared with the 74U group, expressions of SIRT1, ZO-1, and Claudin-1 were increased, whereas Beclin-1, and LC3II/I were decreased in the YS group (P<0.05). The treatment of 3-MA and rapamycin regulated autophagy and the expression of SIRT1. SIRT1 activator intervention up-regulated autophagy as well as the expressions of ZO-1 and Claudin-1 compared with the 74U group (P<0.05). CONCLUSION Colonic dialysis with YS could improve autophagy disorder and repair CRF intestinal mucosal barrier injury by regulating SIRT1 expression in intestinal epithelial cells.
Animals ; Sirtuin 1/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Autophagy/drug effects* ; Male ; Intestinal Mucosa/drug effects* ; Rats, Sprague-Dawley ; Epithelial Cells/metabolism* ; Colon/drug effects* ; Humans ; Kidney Failure, Chronic/drug therapy* ; Signal Transduction/drug effects* ; Renal Dialysis ; Rats ; Kidney/drug effects*

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) can result in fatigue and post-exertional malaise; however, whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exacerbates lower urinary tract symptoms (LUTS) is unclear. This study investigated the association between prenatal SARS-CoV-2 infection and postpartum LUTS. METHODS: A multicenter, retrospective cohort study was conducted at two tertiary hospitals in China from November 1, 2022, to November 1, 2023. Participants were classified into infected and uninfected groups based on SARS-CoV-2 antigen results. LUTS prevalence and severity were assessed using self-reported symptoms and the Incontinence Impact Questionnaire-Short Form (IIQ-7). Pelvic floor muscle activity was measured using electromyography following the Glazer protocol. Group comparisons were performed to evaluate the association of SARS-CoV-2 infection with LUTS and electromyography parameters, with stratified analyses conducted using SPSS version 26.0. RESULTS: Among 3,652 participants (681 infected, 2,971 uninfected), no significant differences in LUTS prevalence or IIQ-7 scores were observed. However, SARS-CoV-2 infection was an independent factor influencing the electromyographic activity of the pelvic floor muscles (mean tonic contraction amplitudes), regardless of delivery mode ( P = 0.001). CONCLUSION Prenatal SARS-CoV-2 infection was not significantly associated with an increased risk of postpartum LUTS but independently altered pelvic floor muscle electromyographic activity, suggesting potential neuromuscular effects.
Humans ; Female ; COVID-19/epidemiology* ; Retrospective Studies ; Adult ; Pregnancy ; Lower Urinary Tract Symptoms/virology* ; Postpartum Period ; Pregnancy Complications, Infectious/virology* ; China/epidemiology* ; Electromyography ; SARS-CoV-2/physiology* ; Pelvic Floor/physiopathology* ; Prevalence

Objective:To investigate the molecular characteristics of low-risk acute myeloid leukemia(AML)at recurrence,and analyze the factors affecting retreatment efficacy and prognosis.Methods:A retrospective analysis was conducted on the clinical and laboratory data of 31 patients with newly diagnosed low-risk AML who relapsed during consolidation treatment or follow-up after treatment in our hospital from April 2017 to January 2023.Gene mutations before and after relapse were compared,retreatment efficacy following relapse was evaluated,and univariate and multivariate analyses were performed to identify factors influencing treatment efficacy and prognosis.Results:Gene sequencing results after relapse showed that the most common newly acquired mutation was FLT3-ITD,while RAS mutation detected at initial diagnosis were predisposed to loss of expression during relapse.The median overall survival(OS)after relapse for the entire cohort was 349(170-528)days,with non-hematopoietic stem cell transplantation(HSCT)group and HSCT group demonstrating median survival times of 210(106-314)days and not reached,respectively(P=0.001).Multivariate analysis revealed that age ≥60 years was a significant risk factor for achieving remission after retreatment in initially diagnosed low-risk AML patients who experienced relapse(OR=18.222,95％CI:1.188-279.597,P=0.037).Additionally,DNMT3A mutation was identified as an independent risk factor for OS(HR=13.165,95％CI:2.018-85.877,P=0.007),while HSCT post-relapse demonstrated significant survival benefits(HR=0.133,95％CI:0.025-0.698,P=0.017)and served as an independent protective factor for OS.Conclusion:Relapsed low-risk AML is often associated with loss of RAS and novel mutations in FLT3-ITD.Age ≥ 60 years and DNMT3A mutations were identified as independent adverse factors for achieving subsequent remission and post-relapse survival,respectively,while HSCT significantly improved patient outcomes.