Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Elemene is widely recognized as an effective anti-cancer compound and is routinely administered in Chinese clinical settings for the management of several solid tumors,including non-small cell lung cancer(NSCLC).However,its detailed molecular mechanism has not been adequately demonstrated.In this research,it was demonstrated that elemene effectively curtailed NSCLC growth in the patient-derived xenograft(PDX)model.Mechanistically,employing high-throughput screening techniques and subsequent biochemical validations such as microscale thermophoresis(MST),microRNA-145-5p(miR-145-5p)was pinpointed as a critical target through which elemene exerts its anti-tumor effects.Inter-estingly,elemene serves as a binding stabilizer for miR-145-5p,demonstrating a strong binding affinity(dissociation constant(KD)=0.39±0.17 μg/mL)and preventing its degradation both in vitro and in vivo,while not interfering with the synthesis of the primary microRNA transcripts(pri-miRNAs)and precursor miRNAs(pre-miRNAs).The stabilization of miR-145-5p by elemene resulted in an increased level of this miRNA,subsequently suppressing NSCLC progression through the miR-145-5p/mitogen-activated pro-tein kinase kinase kinase 3(MAP3K3)/nuclear factor kappaB(NF-κB)pathway.Our findings provide a new perspective on revealing the interaction patterns between clinical anti-tumor drugs and miRNAs.

OBJECTIVE: We aimed to investigate the patterns of fasting blood glucose (FBG) trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers. METHODS: The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort (TGOC) between 2017 and 2022. A group-based trajectory model was used to identify the FBG trajectories. Environmental risk scores (ERS) were constructed using regression coefficients from the occupational hazard model as weights. Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories. RESULTS: FBG trajectories were categorized into three groups. An association was observed between high temperature, noise exposure, and FBG trajectory ( P < 0.05). Using the first quartile group of ERS1 as a reference, the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90 and 2.21 times, respectively (odds ratio [ OR] = 1.90, 95% confidence interval [ CI]: 1.17-3.10; OR = 2.21, 95% CI: 1.09-4.45). CONCLUSION An association was observed between occupational hazards based on ERS and FBG trajectories. The risk of FBG trajectory levels increase with an increase in ERS.
Humans ; Male ; Adult ; Blood Glucose/analysis* ; China ; Prospective Studies ; Occupational Exposure/adverse effects* ; Risk Factors ; Middle Aged ; Steel ; Fasting/blood* ; Metal Workers ; East Asian People

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 observe the early efficacy of intense pulsed light(IPL)combined with non-ablative fractional laser(NAFL)in preventing postoperative pathological scar formation and intervention of inflammatory factors.Methods:93 patients with postoperative pathological scar formation who were admitted to our hospital from March 2022 to September 2024 were selected,they were divided into control group A(silicone gel treatment,n=31),control group B(NAFL on the basis of control group A,n=31)and study group(IPL on the basis of control group B,n=31)using the random number table method.The clinical efficacy,simple quality of life scale(SF-36),vancouver scar scale(VSS),inflammatory factors[interleukin-6(IL-6),tumor necrosis factor-α(TNF-α),C-reactive protein(CRP)],and adverse reactions among three groups were compared.Results:The clinical total effective rate in the study group were higher than those in the control group A and control group B(P＜0.05).SF-36 increased sequentially and VSS decreased sequentially in control group A,control group B,and study group after treatment(P＜0.05).CRP,IL-6,and TNF-α decreased sequentially in control group A,control group B,and study group after treatment(P＜0.05).There was no significant difference in the incidence of adverse reactions among the three groups(P＞0.05).Conclusion:IPL combined with NAFL in preventing postoperative pathological scar formation,can effectively reduce scar formation,reduce inflammatory factors levels,improve patients' quality of life,and be safe and reliable.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:To develop a simplified confocal laser endomicroscopy (CLE)-based healing scoring system for assistant diagnosis of deep remission in ulcerative colitis (UC).Methods:CLE images from consecutive UC patients in clinical remission or mild activity and healthy controls undergoing CLE at Peking University First Hospital from January 2017 to December 2019 were retrospectively analyzed. According to the diagnosis of inflammation in intestinal segments in the medical records of UC patients, CLE images were divided into two groups, the involved group (inflamed UC segment) and the control group (segments from healthy individuals and non-inflamed UC segments). CLE features differentiating the groups were identified, and univariable regression analysis was used to obtain indicators related to unhealed histological inflammation (Geboes score>2.0), forming a simplified CLE healing score using the significant indicators, and receiver operator characteristic (ROC) curve was drawn.Results:The study included 53 UC patients and 14 healthy controls, yielding 201 CLE segments (42 healthy, 69 non-inflamed, 90 inflamed). Eight CLE features differed significantly between the involved and the control groups ( P<0.001), including crypt distortion, crypt lumen irregularity, crypt proximity, crypt sparsity, crypt lumen fluorescein leakage, vascular fluorescein leakage, increased vessel diameter, and cellular infiltration. Univariable regression analysis indicated there were 4 indicators related to histological inflammation, including crypt distortion ( P=0.025, OR=3.613, 95% CI:1.174-11.114), crypt lumen irregularity ( P=0.021, OR=4.081, 95% CI: 1.233-13.511), crypt fluorescein leakage ( P=0.011, OR=5.486, 95% CI: 1.468-20.494) and increased vessel diameter ( P=0.002, OR=7.724, 95% CI: 2.062-28.938). These 4 indicators were combined to form a simplified CLE healing score and a ROC curve was plotted with AUC of 0.769 (95% CI:0.654-0.833). The optimal threshold for histological healing was the absence of all four features (score=0), with sensitivity and specificity of 83.1% (59/71) and 42.1% (8/19), respectively. Conclusion:The simplified CLE score demonstrates high sensitivity and correlates positively with histological healing, supporting its utility as an adjunct tool for assessing deep remission in UC.

Objective To explore the mechanism of honey-processed Hedysari Radix in the regulation of intestinal immunity in rats with spleen qi deficiency,which was based on G protein-coupled receptor 41(GPR41)/GPR43-mediated mitogen-activated protein kinase(MAPK)signaling pathway.Methods The three-factor composite modeling method of eating disorder,diarrhea and fatigue was used to establish a model of spleen qi deficiency,and the rats were randomly divided into model,honey-processed Hedysari Radix,probiotics and blank groups with 15 rats per group.The honey-processed Hedysari Radix group was given by gavage 12.6 g·kg-1 aqueous extract of honey-processed Hedysari Radix.The probiotics group was given 0.625 g·kg-1 bifidobacterium triple viable solution by gavage.The blank and model groups were given the same dose of distilled water by gavage.Four groups were treated for 15 d with once a day.The expression levels of GPR41,GPR43,P38 MAPK,c-Jun N-terminal kinase(JNK)and extracellular regulatory protein kinase 1/2(ERK1/2)in colon tissues were detected by Western blotting.Results The relative expression levels of GPR41 in the blank,model,honey-processed Hedysari Radix and probiotics groups were 0.95±0.07,0.45±0.03,0.84±0.19 and 0.86±0.20;the relative expression levels of GPR43 were 1.17±0.11,0.41±0.06,0.66±0.03 and 0.57±0.01;the phosphorylated ERK1/2/ERK1/2 ratios were 0.16±0.01,0.43±0.01,0.39±0.01 and 0.36±0.02;the phosphorylated JNK/JNK ratios were 0.58±0.05,1.47±0.10,0.90±0.11 and 0.90±0.11;the phosphorylated P38 MAPK/P38 MAPK ratios were 1.77±0.33,3.19±0.03,2.01±0.17 and 2.23±0.59,respectively.Compared with the model group,the differences of above indexes were statistically significant in the honey-processed Hedysari Radix and probiotics groups(P＜0.05,P＜0.01).Conclusion The mechanism of honey-processed Hedysari Radix regulating intestinal immunity in rats with spleen qi deficiency is related to the regulation of GPR41/GPR43 mediated MAPK signaling pathway.

Objective To summarize the magnetic resonance imaging(MRI)features of spinal adnexal tuberculosis in order to improve its early diagnosis.Methods Totally 21 spinal adnexal tuberculosis patients confirmed at some hospital from January 2019 to October 2023 had their clinical data and MRI images analyzed retrospectively to determine the basic clinical characteristics and MRI features.Results Of the 21 patients,8 ones had sudden lower extremity weakness and pyramidal tract signs,and the remaining 13 ones had no significant symptoms of neurologic deficit.The lesion involvement ranged from C4 to L5 vertebrae,with the involvement of lumbar segments in 13 cases,thoracic segments in 6 cases and cervical segments in 2 cases.There were 15 cases that had tuberculosis involving in only a single spinal adnexa and peripheral soft tissue,4 cases in 2 vertebrae and 2 cases in 3 vertebrae.There were 9 cases involving in pedicles,8 cases in vertebral plates and some cases involving in sphenoid process,transverse process or facet joints.The MRI features of spinal adnexal tuberculosis included the tuberculosis-infected bone showing slightly low signals on T1WI while slightly high signals on T2WI,edge enhancement by enhanced scan and edema and abscess of paravertebral soft tissue and intra-and extradural tuberculous abscess displayed clearly by the fat suppression and diffusion weighted imaging sequences of T2WI.Conclusion MRI effectively detects the abnormal signs of bone and soft tissue of spinal adnexal tuberculosis.The MRI findings of spinal adnexal tuberculosis are of characteristics,and MRI can be used as the first choice for imaging examination and differential diagnosis to realize early detection of spinal adnexal tuberculosis.[Chinese Medical Equipment Journal,2025,46(1):55-59]

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.