Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

Objective:To analyze the clinical features associated with pelvic lymph node metastasis(PLNM)in prostate cancer and to construct a preoperative prediction model for PLNM,thereby reducing unnecessary extended pelvic lymph node dissection(ePLND).Methods:Based on predefined inclusion and exclusion criteria,344 patients who underwent radical prostatectomy and ePLND at the First Affilia-ted Hospital of Zhengzhou University between 2014 and 2024 were retrospectively enrolled,among whom,77 patients(22.4％)were pathologically confirmed to have lymph node-positive disease.The clinical characteristics,MRI reports,and pathological results were collected.The data were then randomly divi-ded into a training cohort(241 cases,70％)and a validation cohort(103 cases,30％).Univariate and multivariate Logistic regression analysis were employed to construct a preoperative prediction model for PLNM.Results:Univariate Logistic regression analysis revealed that total prostate specific antigen(tPSA)(P=0.021),free prostate specific antigen(fPSA)(P=0.002),fPSA to tPSA ratio(fPSA/tPSA)(P=0.011),percentage of positive biopsy cores(P＜0.001),prostate imaging reporting and data system(PI-RADS)score(P=0.004),biopsy Gleason score ≥8(P=0.005),clinical T stage(P＜0.001),and MRI-indicated lymph node involvement(MRI-LNI)(P＜0.001)were significant predictors of PLNM.Multivariate Logistic regression analysis demonstrated that the percentage of positive biopsy cores(OR=91.24,95％CI:13.34-968.68),PI-RADS score(OR=7.64,95％ CI:1.78-138.06),and MRI-LNI(OR=4.67,95％ CI:1.74-13.24)were independent risk factors for PLNM.And a novel nomogram for predicting PLNM was developed by integrating all these three variables.Com-pared with the individual predictors:percentage of positive biopsy cores[area under curve(AUC)=0.806],PI-RADS score(AUC=0.679),and MRI-LNI(AUC=0.768),the multivariate model incor-porating all three variables demonstrated significantly superior predictive performance(AUC=0.883).Consistently,calibration curves and decision curve analyses confirmed that the multivariable model had high predictive accuracy and provided significant net clinical benefit relative to single-variable models.And using a cutoff of 6％,the multiparameter model missed only approximately 5.2％of PLNM cases(4/77),while reducing approximately 53％of ePLND procedures(139/267),demonstrating favorable predictive efficacy.Conclusion:Percentage of positive biopsy cores,PI-RADS score and MRI-LNI are independent risk factors for PLNM.The constructed multivariate model significantly improves predictive efficacy,offering a valuable tool to guide clinical decisions on ePLND.

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.
Biological Products/chemistry* ; Mass Spectrometry/methods* ; Databases, Factual ; Drug Discovery/methods* ; Humans

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

Objective: To compare peritoneal indexes between transperitoneal approach and retroperitioneal approach of robot-assisted partial nephrectomy (RAPN) for dorsal renal tumors via transperitoneal and retroperitoneal approaches，thereby providing reference for clinical decision-making in managing such neoplasms. Methods: The clinical data of renal cancer patients undergoing RAPN performed by the same surgeon at our hospital during 2017 and 2021 were retrospectively analyzed.A total of 80 patients with complete data of dorsal renal tumors were screened and divided into two groups based on the surgical approaches：50 cases in the transperitoneal group and 30 in the retroperitoneal group.The general information，intraoperative data，positive rate of pathological margins，recovery time of gastrointestinal functions，and incidence of complications were compared between the two groups. Results: All operations were successfully completed, and the surgical margins were negative.There were no statistically significant differences in warm ischemia time [17 (15，18) min vs.16 (14，19) min，P=0.772]，operation time [120 (105，149) min vs.124 (108，152) min，P=0.584]，intraoperative blood loss [100 (50，100) mL vs.100 (50，100) mL，P=0.814]，and incidence of postoperative complications (17% vs.24%，P=0.504) between the two groups (P>0.05).The postoperative recovery time of gastrointestinal functions in the retroperitoneal group was significantly shorter than that in the transperitoneal group [2.0 (2.0，3.0) d vs.3.5 (3.0，4.0) d，P<0.001]. Conclusion: The perioperative outcomes of patients undergoing RAPN via the retroperitoneal approach are similar to those via the transperitoneal approach.However，the retroperitoneal approach has an advantage of faster recovery of gastrointestinal functions.

Objective:To analyze the clinical features associated with pelvic lymph node metastasis(PLNM)in prostate cancer and to construct a preoperative prediction model for PLNM,thereby reducing unnecessary extended pelvic lymph node dissection(ePLND).Methods:Based on predefined inclusion and exclusion criteria,344 patients who underwent radical prostatectomy and ePLND at the First Affilia-ted Hospital of Zhengzhou University between 2014 and 2024 were retrospectively enrolled,among whom,77 patients(22.4％)were pathologically confirmed to have lymph node-positive disease.The clinical characteristics,MRI reports,and pathological results were collected.The data were then randomly divi-ded into a training cohort(241 cases,70％)and a validation cohort(103 cases,30％).Univariate and multivariate Logistic regression analysis were employed to construct a preoperative prediction model for PLNM.Results:Univariate Logistic regression analysis revealed that total prostate specific antigen(tPSA)(P=0.021),free prostate specific antigen(fPSA)(P=0.002),fPSA to tPSA ratio(fPSA/tPSA)(P=0.011),percentage of positive biopsy cores(P＜0.001),prostate imaging reporting and data system(PI-RADS)score(P=0.004),biopsy Gleason score ≥8(P=0.005),clinical T stage(P＜0.001),and MRI-indicated lymph node involvement(MRI-LNI)(P＜0.001)were significant predictors of PLNM.Multivariate Logistic regression analysis demonstrated that the percentage of positive biopsy cores(OR=91.24,95％CI:13.34-968.68),PI-RADS score(OR=7.64,95％ CI:1.78-138.06),and MRI-LNI(OR=4.67,95％ CI:1.74-13.24)were independent risk factors for PLNM.And a novel nomogram for predicting PLNM was developed by integrating all these three variables.Com-pared with the individual predictors:percentage of positive biopsy cores[area under curve(AUC)=0.806],PI-RADS score(AUC=0.679),and MRI-LNI(AUC=0.768),the multivariate model incor-porating all three variables demonstrated significantly superior predictive performance(AUC=0.883).Consistently,calibration curves and decision curve analyses confirmed that the multivariable model had high predictive accuracy and provided significant net clinical benefit relative to single-variable models.And using a cutoff of 6％,the multiparameter model missed only approximately 5.2％of PLNM cases(4/77),while reducing approximately 53％of ePLND procedures(139/267),demonstrating favorable predictive efficacy.Conclusion:Percentage of positive biopsy cores,PI-RADS score and MRI-LNI are independent risk factors for PLNM.The constructed multivariate model significantly improves predictive efficacy,offering a valuable tool to guide clinical decisions on ePLND.

Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

ObjectiveTo observe the effect of Youguiwan on the leptin/Janus kinase 2 （JAK2）/signal transducer and activator of transcription 3 （STAT3） signaling pathway in the lung tissue of the rat model of chronic obstructive pulmonary disease （COPD） due to kidney-Yang deficiency. MethodForty rats were modeled for COPD with the syndrome of kidney-Yang deficiency by intratracheal instillation of lipopolysaccharide on day 1 and day 14 and continuous fumigation for 6 weeks， during which hydrocortisone was injected intramuscularly at an interval of 3 days. The modeled rats were randomized into model， high- （11.7 g·kg^-1）， medium- （5.85 g·kg^-1）， and low-dose （2.93 g·kg^-1） Youguiwan， and aminophylline （0.054 g·kg^-1） group. In addition， 8 SD rats were set as the blank group. After the completion of modeling， the rats in each group were administrated with the corresponding drug by gavage for 28 consecutive days. After the last administration， samples were collected. A lung function analyzer was used to evaluate the lung function of rats. Enzyme-linked immunosorbent assay was employed to measure the levels of interleukin-17A （IL-17A）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） in the bronchoalveolar lavage fluid （BALF）. Hematoxylin-eosin staining was employed to observe the pathological changes in the lung tissue， and Masson staining was employed to observe the deposition of blue collagen fibers around bronchi in the lung tissue and calculate the inflammation score. The immunofluorescence assay was employed to measure the protein content of collagen type Ⅰ （ColⅠ） and α-smooth muscle actin （α-SMA） in the bronchi. The protein and mRNA levels of leptin， IL-17A， JAK2， and STAT3 in the lung tissue were determined by Western blot and real-time fluorescence quantitative polymerase chain reaction， respectively. ResultCompared with the blank group， the model group showed decreased lung function （P<0.01）， elevated levels of IL-6， IL-17A， and TNF-α in the BALF （P<0.01）， and increased lung inflammation score， deposition of subcutaneous collagen fibers in the airway， and ColⅠ and α-SMA proteins （P<0.01）. Furthermore， the modeling up-regulated the proteins and mRNA levels of leptin， IL-17A， JAK2， and STAT3 in the lung tissue （P<0.01） and enhanced the phosphorylation of JAK2 and STAT3 （P<0.01）. Compared with the model group， high- and medium-dose Youguiwan improved the lung function， decreased the inflammation score， reduced collagen fiber deposition and ColⅠ and α-SMA proteins， lowered the levels of IL-6， IL-17A， and TNF-α in the BALF， down-regulated the mRNA and protein levels of leptin， JAK2， STAT3， and IL-17A， and weakened the phosphorylation of JAK2 and STAT3 （P<0.05， P<0.01）. The aminophylline group had higher IL-17A and TNF-α levels than the high-dose Youguiwan group， lower IL-17A level than the medium and low-dose Youguiwan groups， and lower TNF-α level than the low-dose Youguiwan group. Compared with the aminophylline group， the high- and medium-dose Youguiwan groups showed reduced deposition of collagen fibers and protein levels of ColⅠ and α-SMA around the bronchi in the lung tissue （P<0.05， P<0.01）， decreased inflammation score， and down-regulated protein and mRNA levels of leptin， JAK2， STAT3， and IL-17A in the lung tissue. ConclusionYouguiwan can prevent airway remodeling by inhibiting IL-17A to reduce inflammation and collagen deposition in COPD rats， which may be related to the inhibition of the leptin/JAK2/STAT3 signaling pathway.

Dracaena marginata is a widely cultivated horticultural plant in the world, which has high ornamental and medicinal value. In this study, the whole genome of leaves from D. marginata was sequenced by Illumina HiSeq 4000 platform. The chloroplast genome were assembled for functional annotation, sequence characteristics and phylogenetic analysis. The results showed that the chloroplast genome of D. marginata composed of four regions with a size of 154 926 bp, which was the smallest chloroplast genome reported for Dracaena species to date. A total of 132 genes were identified, including 86 coding genes, 38 tRNA genes and 8 rRNA genes. Codon bias analysis found that the codon usage bias was weak and there was a bias for using A/U base endings. 46 simple sequence repeat and 54 repeats loci were detected in the chloroplast genome, with the maximum detection rate in the large single copy region and inverted repeat region, respectively. The inverted repeats boundaries of D. marginata and Dracaena were highly conserved, whereas gene location differences occurred. Phylogenetic analysis revealed that D. serrulata and D. cinnabari form a monophyletic clade, which was the closest relationship and conformed to the morphological classification characteristics. The analysis of the chloroplast genome of D. marginata provides important data basis for species identification, genetic diversity and chloroplast genome engineering of Dracaena.
Phylogeny ; Dracaena ; Genome, Chloroplast/genetics* ; Base Sequence ; Genes, Plant

Objective:To investigate the clinical effect of low molecular weight heparin sodium combined with antivenin in the treatment of severe and critical bite by Trimeresurus stejnegeri.Methods:The clinical data of 48 patients with severe or critical bite by Trimeresurus stejnegeri admitted to emergency department of Southeast Hospital Affiliated to Xiamen University from March 2017 to May 2019 were retrospectively analyzed. On the basis of early treatment of antivenom serum, internal administration and external application of Jidesheng snake tablet, and wound incision and detoxification, the patients were divided into heparin treatment group and non-heparin treatment group according to whether the low molecular heparin sodium was used or not. The patients in the two groups were compared in terms of gender, age, clinical classification, swelling degree of injured limbs, change of coagulation function index, bleeding of skin, mucous membrane or digestive tract, blood transfusion, local symptoms of bite, length of hospital stay and prognosis.Results:There was no significant difference in terms of gender, age, clinical classification or swelling degree of injured limbs between the two groups. On the 3rd day of treatment, the platelet count (PLT) in the heparin treatment group was significantly higher than that in the non-heparin treatment group [×10 9/L: 210.0 (160.0, 252.0) vs. 136.0 (104.0, 198.5), P < 0.05]. However, there was no significant difference in the four coagulation test results between the two groups. On the 6th day of treatment, the plasma thrombin time (TT) in the heparin treatment group was significantly shorter than that on the 3rd day of treatment [s: 30.3 (20.4, 37.0) vs. 34.7 (24.0, 73.4), P < 0.05], and the fibrinogen (FIB) in the heparin treatment group was significantly higher than that in the non-heparin treatment group [g/L: 0.60 (0.31, 1.07) vs. 0.20 (0.14, 0.60), P < 0.01]. The incidence of bleeding in the heparin treatment group was significantly lower than that in the non-heparin treatment group [21.7% (5/23) vs. 64.0% (16/25), P < 0.01]; 11 patients in the heparin treatment group and 18 patients in the non-heparin treatment group received blood transfusion and prothrombin complex supplement respectively. There was no significant difference in the length of hospital stay between the heparin group and non-heparin treatment group (days: 6.91±1.92 vs. 7.48±2.27, P > 0.05). The patients in both groups were followed up for 1 week to 1 month after treatment, and no death or local necrosis of skin and soft tissue was found. Conclusions:For the patients with severe and critical bite by Trimeresurus stejnegeri, on the basis of injection of antivenom serum, internal administration and external application of Jidesheng snake tablet, and wound incision and detoxification, early application of low molecular weight heparin sodium anticoagulation and other comprehensive treatment is helpful to improve limb swelling and inflammation, reduce blood transfusion, promote the recovery of coagulation function, and shorten the length of hospitalization.