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.

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: 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: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.

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.

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·To explore the biological functions and underlying mechanisms of anaphase-promoting complex subunit 10(ANAPC10)in the development and progression of liver hepatocellular carcinoma(LIHC,often abbreviated as HCC).Methods·By integrating data from The Cancer Genome Atlas(TCGA)_LIHC,the hepatitis B virus-related subgroup(HBV)of the China Hepatocellular Carcinoma Genome Project(CHCC),and the Gene Expression Omnibus(GEO),the expression pattern of ANAPC10 in HCC was analyzed.Western blotting and quantitative real-time PCR(q-PCR)were used to verify the findings in HCC cell lines.shRNA-mediated knockdown of ANAPC10 was performed in MHCC-97H and SNU-398 cell lines to investigate the effect of ANAPC10 depletion on the in vitro proliferation of HCC cells.An orthotopic liver cancer model with Anapc10 knockout was constructed using the hydrodynamic tail-vein injection technique in mice to further confirm the impact of ANAPC10 deficiency in the liver on the development and progression of HCC.Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Ontology(GO)enrichment analyses were performed on the RNA-sequencing data from TCGA_LIHC and CHCC_HBV.Results·ANAPC10 was highly expressed in tumor tissues,and its expression level was closely related to patient survival.Downregulation of ANAPC10 in vitro and in vivo effectively inhibited HCC progression.ANAPC10 mainly reprogrammed the metabolism of tumors by affecting the PI3K-AKT-mTOR pathway.In the tumor tissues of the orthotopic liver cancer mouse model in the Anapc10 knockout group,the phosphorylation levels of Akt and S6k were decreased,and changes in the key downstream lipid metabolism proteins Fasn and Scd1 were verified.Conclusion·ANAPC10 is highly expressed in HCC and is positively correlated with poor prognosis.It promotes HCC occurrence and progression by activating the PI3K-AKT-mTOR signaling pathway and enhancing lipid metabolism reprogramming,thereby promoting tumor cell proliferation.These findings expand the understanding of ANAPC10 in tumor progression and suggest potential therapeutic targets for HCC.

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.