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 exploring the drug rules for treating children's night cough based on the theory of"Shaoyang as the pivot".Methods 189 cases of children with night cough were included,and 224 prescriptions.Used the Traditional Chinese Medicine Inheritance Assistance Platform to analyze the time distribution,syndrome types,and four nature,five flavors,and channel tropism of diseases.Used frequency statistics,association rule analysis,and cluster analysis to extract drug patterns for pediatric night cough.Results Coughing occured most frequently during the Yin period.The syndrome type was mainly Shaoyang syndrome.The high-frequency core drugs were Chaihu,Huangqin,and Banxia,etc..The prescription characteristics were Xiaochaihu decoction without Renshen,Shengjiang,and Dazao,and added Wuweizi,Danggui and Xingren.The drugs were used flexibly according to the syndromes:Maxing Er San decoction was added to the phlegm and drink syndrome,Qumai Er Chen decoction was added to the food stagnation syndrome,Sijunzi decoction was added to the qi deficiency syndrome,Maiwei Dihuang decoction was added to the yin deficiency syndrome,and Weijing decoction was added to the phlegm and heat syndrome.Conclusion Based on the basic principle of harmonizing Shaoyang,and according to the disease mechanism,the classical prescription is flexibly used,forming a night cough treatment system based on the"Shaoyang as the pivot"theory,with distinct clinical characteristics.

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.

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

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 exploring the drug rules for treating children's night cough based on the theory of"Shaoyang as the pivot".Methods 189 cases of children with night cough were included,and 224 prescriptions.Used the Traditional Chinese Medicine Inheritance Assistance Platform to analyze the time distribution,syndrome types,and four nature,five flavors,and channel tropism of diseases.Used frequency statistics,association rule analysis,and cluster analysis to extract drug patterns for pediatric night cough.Results Coughing occured most frequently during the Yin period.The syndrome type was mainly Shaoyang syndrome.The high-frequency core drugs were Chaihu,Huangqin,and Banxia,etc..The prescription characteristics were Xiaochaihu decoction without Renshen,Shengjiang,and Dazao,and added Wuweizi,Danggui and Xingren.The drugs were used flexibly according to the syndromes:Maxing Er San decoction was added to the phlegm and drink syndrome,Qumai Er Chen decoction was added to the food stagnation syndrome,Sijunzi decoction was added to the qi deficiency syndrome,Maiwei Dihuang decoction was added to the yin deficiency syndrome,and Weijing decoction was added to the phlegm and heat syndrome.Conclusion Based on the basic principle of harmonizing Shaoyang,and according to the disease mechanism,the classical prescription is flexibly used,forming a night cough treatment system based on the"Shaoyang as the pivot"theory,with distinct clinical characteristics.

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 investigate the predictive value of inflammatory cells and clinical features in the prognosis of immune checkpoint inhibitors (ICIs) treating non-small cell lung cancer (NSCLC).Methods The data of 163 cases of stage Ⅲ and Ⅳ NSCLC patients treated with the ICIs in this hospital from January 1,2017 to December 31,2022 were collected.The CT examination was conducted after 6-8 weeks treatment.The pa-tients were divided into the objective remission group[complete remission (CR)+partial remission (PR)]and non-objective remission group[stable disease (SD)+progressed disease (PD)],disease control group (CR+PR+SD) and non-disease control group (PD),persistent clinical benefit group (DCB) and non-DCB group.The differences in clinical features and inflammatory cells indicators were compared among the differ-ent groups.The receiver operating characteristic (ROC) curve was adopted to evaluate the predictive efficiency of the inflammatory cells indicators for DCB.The influencing factors analysis of progression free survival (PFS) time and overall survival (OS) time adopted the Cox regression analysis.Results The lymphocyte count (ALC) in the disease control group was higher than that in the non-disease control group.The neutro-phil to lymphocyte ratio (NLR),platelet-lymphocyte ratio (PLR) and mononuclear lymphocyte ratio (MLR) were lower than those in the non-disease control group.The proportions of squamous cell carcinoma,stage Ⅲ,ECOG score 0-1 point,adverse reactions in the DCB group were higher than those in the non-DCB group (P<0.05),the PLT count,NLR,PLR and MLR were lower than those in the non-DCB group (P<0.05). The ROC curve analysis results showed that PLT,NLR,PLR and MLR could serve as the indicators for pre-dicting DCB,the area under of ROC curve (AUC) was 0.633,0.602,0.635 and 0.604 respectively,the opti-mal cut off values were 187×109/L (P=0.004),5.0 (P=0.026),235 (P=0.003) and 0.35 (P=0.024) re-spectively.The multivariate Cox regression analysis showed that non-squamous carcinoma including adenocar-cinoma (HR=1.565,95％CI:1.057-2.316) and other pathologic types (HR=2.285,95％CI:1.326-3.936),ECOG score 2-3 points (HR=2.375,95％CI:1.652-3.415),AMC≥0.65×109/L (HR=1.847,95％CI:1.160-2.938) and PLR≥235 (HR=1.557,95％CI:1.016-2.386) were the independent risk factors for short PFS.The ECOG score 2-3 points (HR=4.615,95％CI:2.882-7.391),AMC≥0.65×109/L (HR=5.161,95％CI:2.984-8.925) and PLR ≥235 (HR=1.732,95％CI:1.059-2.833) were the independent risk fac-tors for short OS (P<0.05),and having adverse reactions (HR=0.472,95％CI:0.294-0.757) was the independ-ent protective factor for short OS (P<0.05).Conclusion Lower PLT,AMC,NLR,MLR and PLR,higher ALC,squamous cell carcinoma,TNM stage Ⅲ,ECOG score 0-1 point and immunotherapy related adverse reactions could prompt that the prognosis is good in ICIs treating advanced NSCLC.PLT,NLR,PLR and MLR could serve as the indicators for predicting DCB.

BACKGROUND: Classical guided bone regeneration (GBR) treatments can achieve favorable clinical results for ridge defects. However, extensive bone augmentation in the non-esthetic area in the posterior region for minor ridge defects is unnecessary. Therefore, this study used a collagen and Platelet-rich fibrin (PRF) mixture for bone augmentation on minor posterior ridge defects and evaluated the effects. METHODS: 22 Seibert Class I ridge defects were treated with BC and covered with a PRF membrane (simplified guided bone regeneration, simplified GBR) and other 22 were treated with Bio-Oss and covered with Bio-Gide (classical GBR). Cone-beam computed tomography imaging was conducted 6 months post-surgery to compare the ridge’s horizontal width (HW) and buccal ridge’s horizontal width to assess the osteogenic effect. In addition, the buccal ridge contour morphology was studied and classified. RESULTS: The buccal ridge contour of simplified GBR was Type A in 14 cases, Type B in 7 cases, and Type C in 1 case and it of classical GBR was Type A in 11 cases, Type B in 8 cases, and Type C in 3 cases. The mean HW significantly increased by 1.50 mm of simplified GBR treatment, while it increased by 1.83 mm in classical GBR treatment. CONCLUSION The combined use of BC and PRF had a significant effect on bone augmentation and this treatment exhibited promising clinical results for correcting posterior Seibert Class I ridge defects. The morphological classification of the reconstructive effect in this study can be utilized in future clinical work.

Gastric cancer is one of the most common malignant tumors in China, and its pathogenesis is complex. At present, there is no good method for the diagnosis and treatment of gastric cancer. However, studies have shown that microRNA (miRNA) has abnormal expression in gastric cancer, which participates in the regulation of gastric cancer related genes, and has an impact on the occurrence, development, diagnosis and treatment of gastric cancer. This paper aims to review the relationship between miRNA and the diagnosis and treatment of gastric cancer, as well as the drug resistance in the treatment, so as to lay the foundation for the follow-up research and clinical diagnosis and treatment.