To assess the current status of early surgical site infections following prosthetic implantation in plastic surgery across four provincial-level administrative regions in China, and to explore the effectiveness of standardized training for infection prevention and control.

Our previous research demonstrated that the Wenxia Changfu Formula (WCF), as a neoadjuvant therapy, inhibits M2 macrophage infiltration in the tumor microenvironment and prevents lung cancer metastasis. Given tumor-associated macrophages (TAMs) in epithelial-mesenchymal transition (EMT), this study investigated whether WCF impedes lung cancer metastasis by attenuating TAM-induced EMT in non-small cell lung cancer (NSCLC) cells. Utilizing a co-culture model treated with or without WCF, we observed that WCF downregulated cluster of differentiation 163 (CD163) expression in macrophages, reduced CCL18 levels in the conditioned medium, and inhibited the growth, invasion, and EMT of NSCLC cells induced by macrophage co-culture. Manipulation of CCL18 levels and Src overexpression in NSCLC cells revealed that WCF's effects are mediated through CCL18 and Src signaling. In vivo, WCF inhibited recombinant CCL18 (rCCL18)-induced tumor metastasis in nude mice by blocking Src signaling. These findings indicate that WCF inhibits NSCLC metastasis by impeding TAM-induced EMT via antagonistic modulation of CCL18, providing evidence for its potential development and clinical application in NSCLC patients.
Epithelial-Mesenchymal Transition/drug effects* ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Humans ; Animals ; Lung Neoplasms/metabolism* ; Chemokines, CC/antagonists & inhibitors* ; Mice ; Mice, Nude ; Drugs, Chinese Herbal/administration & dosage* ; Cell Line, Tumor ; Neoplasm Metastasis ; Tumor-Associated Macrophages/drug effects* ; Mice, Inbred BALB C ; Signal Transduction/drug effects*

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective To investigate the effects of Qizhi Zhoufei Granules on endoplasmic reticulum stress in rats with chronic obstructive pulmonary disease(COPD);To explore its mechanism.Methods COPD rat model was induced by lipopolysaccharide tracheal instillation and smoking.Totally 60 Wistar rats were divided into control group,model group,Bufei Huoxue Capsules group and Qizhi Zhoufei Granules low-,medium-and high-dosage groups using random number table method,with 10 rats in each group.Drug gavage intervention was carried out for the treatment group since the 29th day of modeling,and normal saline was given to the control group and model group for 28 d.Lung function tests were performed,HE staining was used to detect morphology of lung tissue,TUNEL staining was used to detect the degree of apoptosis in lung tissue,RT-qPCR and Western blot were used to detect the endoplasmic reticulum stress and apoptosis related molecular mRNA and protein expression in lung tissue.Results Compared with the control group,the lung function indexes of peak inspiratory flow(PIF),peak expiratory flow(PEF)and minute volume(MV)significantly decreased,and frequency of breathing(F)significantly increased in the model group(P＜0.05);the structural damage of the lung tissue was obvious,the lung injury score and apoptosis rate significantly increased(P＜0.05),the expressions of glucose regulated protein 78(GRP78),protein kinase R-like ER kinase(PERK),C/EBP-homologous protein(CHOP),Caspase-3 and Caspase-9 mRNA were increased(P＜0.05),the protein expressions of GRP78,p-PERK,activating transcription factor 4(ATF4),CHOP,Caspase-3 and Caspase-9 were significantly increased(P＜0.05).Compared with the model group,PIF,PEF and MV significantly increased in Qizhi Zhoufei Granules medium-and high-dosage groups and Bufei Huoxue Capsules group,and F significantly decreased(P＜0.05);the damage in lung tissue was improved,and the lung injury score and cell apoptosis rate significantly decreased(P＜0.05),the mRNA expressions of GRP78,PERK,CHOP,Caspase-3 and Caspase-9 in lung tissue decreased(P＜0.05),and the protein expressions of GRP78,p-PERK,ATF4,CHOP,Caspase-3 and Caspase-9 decreased(P＜0.05).Conclusion Qizhi Zhoufei Granules can prevent cell apoptosis and excessive damage by inhibiting the expression of endoplasmic reticulum stress related factors in COPD rats,thereby promoting unfolded protein response and improving endoplasmic reticulum folding ability,constraining endoplasmic reticulum stress state,and assisting in its regulation.

Objective:Using Mendelian randomization analysis to investigate the unidirectional causal effects of gut microbiota on gout and serum uric acid levels.Methods:The Mendelian randomization analysis was conducted using summary statistics from genome-wide association studies (GWAS). The gut microbiota was used as the exposure factor, with gout and serum uric acid levels as the outcomes, utilizing the MiBioGen Consortium, FinnGen GWAS, and CKDGen Consortium meta-analysis databases. The analysis was performed using inverse variance weighted (IVW) method, MR-Egger, and weighted median (WM) approach. Additionally, sensitivity analysis was conducted by excluding heterogeneity and horizontal pleiotropy. This study used RStudio 4.3.1 software for analysis.Results:The IVW results confirmed that 17 microbiota taxa were associated with gout, including class Verrucomicrobiaceae [ OR(95% CI)=1.162(1.004, 1.344), P=0.044], family Verrucomicrobiaceae [ OR(95% CI)=1.161(1.004, 1.344), P=0.044], genus Akkermansia [ OR(95% CI)=1.162(1.004, 1.344), P=0.044], genus Collinsella [ OR(95% CI)=1.257(1.043, 1.516), P=0.016], genus Eubacterium hallii group [ OR(95% CI)=1.226(1.022, 1.471), P=0.027], genus Howardella [ OR(95% CI)=1.094(1.001, 1.195), P=0.046], genus Ruminococcaceae UCG010 [ OR(95% CI)=1.317(1.089, 1.593), P=0.004], order Clostridiales [ OR(95% CI)=1.182(1.007,1.387), P=0.041], order Verrucomicrobiales [ OR(95% CI)=1.162(1.004, 1.344), P=0.044], class Melainabacteria [ OR(95% CI)=0.894(0.804, 0.994), P=0.038], family Streptococcaceae [ OR(95% CI)=0.851(0.727, 0.996), P=0.044], unknown family [ OR(95% CI)=0.890(0.800, 0.989), P=0.030], genus Streptococcus [ OR(95% CI)=0.836(0.710, 0.983), P=0.030], unknown genus [ OR(95% CI)=0.890(0.800, 0.989), P=0.030], genus Victivallis [ OR(95% CI)=0.857(0.736, 0.998), P=0.046], order Gastranaerophilales [ OR(95% CI)=0.890(0.800,0.989), P=0.030], and phylum Bacteroidetes [ OR(95% CI)=0.827(0.692, 0.989), P=0.037]. Additionally, 5 microbiota taxa were associated with serum uric acid levels: phylum Actinobacteria [ OR(95% CI)=0.963(0.925, 0.992), P=0.027], family ⅩⅢ [ OR(95% CI)=0.965(0.932, 1.008), P=0.035], genus Escherichia Shigella [ OR(95% CI)=1.047(1.005,1.089), P=0.034], genus Lachnospiraceae FCS020 group [ OR(95% CI)=0.974(0.941, 1.003), P=0.028], and genus Lachnospiraceae NC2004 group [ OR(95% CI)=0.966(0.943, 0.995), P=0.018]. No abnormalities in SNPs were found in the sensitivity analysis. Conclusion:An increase in the levels of class Verrucomicrobiae, family Verrucomicrobiaceae, genus Akkermansia, and genus Escherichia Shigella is associated with an increased risk of gout or serum uric acid levels, while an increase in the levels of class Melainabacteria, family Streptococcaceae, unknown family, phylum Actinobacteria, and family ⅩⅢ is associated with a decreased risk of gout or serum uric acid levels.

The term"heat stroke"originates from the integration of modern traditional Chinese and Western medicine.In clinical practice,the complementary advantages of the two medical systems can significantly enhance the clinical diagnosis and treatment level of heat stroke.Through comprehensively analyzes the traditional Chinese medicine(TCM)nomenclature for heat stroke,proposing that heat stroke is a type of sunstroke characterized by intense and pure yang nature,specifically referring to symptoms caused directly or indirectly by hot weather.It can be referenced under the categories of Zhongye,Shuwen,Yinshu/Yangshu,Shujue,and Shufeng for treatment.The article reviews the TCM diagnostic and therapeutic thinking for heat stroke,summarizing its etiology and pathogenesis,including summerheat directly entering the Yangming,heat entering the heart and nutrient-blood aspects,evil combined with water(post-emergency),dual injury of qi and fluid(post-mild recovery),and phlegm-stasis obstructing collaterals(post-severe recovery).Based on years of clinical experience and combining the different clinical manifestations of heat stroke with TCM's four diagnostic methods,the article proposes a treatment plan that integrates Chinese and Western medicine,combining disease differentiation with syndrome differentiation.The main syndromes summarized include high fever with spasms(Yangming heat excess syndrome),diarrhea(Yangming fu syndrome-intestinal sweating),high fever with coma(heat entering the heart-nutrient syndrome),high fever with convulsions(extreme heat generating wind syndrome),heat stroke-induced coagulopathy(heat entering the blood aspect syndrome),edema after fluid resuscitation(Taiyang water retention syndrome),recovery phase(dual injury of qi and fluid syndrome),and sequelae(phlegm-stasis obstructing collaterals syndrome).For treatment,Baihu Jia Renshen decotion combined with Zengye Chengqi decotion is used for nourishing yin and increasing fluids,relaxing tendons,and stopping spasms for Yangming heat excess syndrome;Baihu decotion combined with Zengye decotion for clearing summerheat and nourishing yin for Yangming fu syndrome-intestinal sweating;Qingying decotion for clearing the nutrient aspect and cooling blood,penetrating heat,and nourishing yin for heat entering the heart-nutrient syndrome;Lingjiao Gouteng decotion for clearing heat and cooling the liver,extinguishing wind,and calming spasms for extreme heat generating wind syndrome;Wuling powder for draining and eliminating water retention for Taiyang water retention syndrome;Wang's Qing Shu Yiqi decotion for clearing summerheat and reducing fever,benefiting qi,and generating fluids for dual injury of qi and fluid syndrome;and Sanjia powder for clearing residual heat,resolving phlegm,and removing stasis from collaterals for phlegm-stasis obstructing collaterals syndrome.Starting from TCM theory and linking it with practice,the article combines Western disease differentiation with TCM syndrome differentiation,aiming to provide new ideas for the clinical treatment of heat stroke.