Gouty arthritis （GA） is caused by monosodium urate（MSU） deposition due to purine metabolism disorders. In traditional Chinese medicine （TCM）， it falls under the category of "dampness-heat Bi syndrome"， with core pathogenesis involving dampness-heat accumulation and dysfunction of the spleen and kidney. The dampness-heat syndrome is the most common and the primary syndrome type during acute attacks. In Western medicine， GA is associated with purine metabolism imbalance and inflammation triggered by MSU crystals， involving pathways such as NOD-like receptor protein 3 （NLRP3） inflammasome activation and Toll-like receptor 2/4 （TLR2/4） signaling. Clinically， colchicine and similar drugs are commonly used to treat GA， although long-term use carries potential side effects. Ermiao Formula analogs originate from ancient prescriptions， including Ermiao， Sanmiao， and Simiao compound formulas. All contain Atractylodis Rhizoma and Phellodendri Chinensis Cortex. Ermiaowan follow a 1∶1 formulation ratio. Sanmiaowan add Cyathulae Radix. Simiaowan further incorporate Coicis Semen. These formulas are rich in active ingredients， including alkaloids， terpenoids， flavonoids， and sterols， and treat GA through multi-component， multi-pathway， and multi-target mechanisms. Ermiaosan primarily exerts anti-inflammatory effects by inhibiting pathways such as TLR4/nuclear factor kappa-B （NF-κB） or regulating immune responses to reduce the release of inflammatory mediators， while also suppressing xanthine dehydrogenase （XDH） and xanthine oxidase （XO） activity to decrease uric acid production. Sanmiaowan enhance uric acid-lowering and anti-inflammatory effects through the guiding herb Cyathulae Radix， while also protecting cartilage from damage. Simiaowan utilizes Coicis Semen to regulate intestinal flora， alleviate dampness-heat symptoms， and exert multi-pathway anti-inflammatory and uric acid-lowering effects. The active ingredients contribute differently to uric acid metabolism regulation， anti-inflammation， antioxidant activity， and bone repair， resulting in varying therapeutic effects due to differences in formula composition. In summary， formulas derived from Ermiaosan demonstrate significant efficacy in treating dampness-heat type GA. This review summarizes their research progress and mechanisms， providing a reference for clinical application， new drug development， and further studies.

OBJECTIVE To investigate the clinical characteristics and risk factors for batroxobin-related severe hypofibrinogenemia （HFIB） and construct a risk prediction model. METHODS A retrospective analysis was conducted on inpatients treated with batroxobin in the First Medical Center of a tertiary hospital from January 1， 2020， to December 31， 2024. Patients were categorized into non-severe HFIB group and severe HFIB group based on the severity of HFIB. Univariate and multivariate Logistic regression analyses were performed to identify the independent influencing factors for batroxobin-related severe HFIB. A nomogram was developed using the “rms” package in R 4.5 software. The predictive performance of the model was evaluated using the receiver operating characteristic curve. Calibration was assessed via the Bootstrap resampling method， and goodness-of-fit was evaluated with the Hosmer-Lemeshow test. RESULTS A total of 1 472 patients were included in this study. Of these， 1 445 developed HFIB， yi elding an incidence of 98.17%. Furthermore， 895 were classified as severe HFIB， accounting for 60.80% of the cohort. Multivariate Logistic regression analysis showed that increased age， high initial dose per 10 kg body weight， use of maintenance dose， and concomitant glucocorticoid use were independent risk factors for batroxobin-related severe HFIB， while high baseline fibrinogen （FIB） level was identified as a protective factor. The model demonstrated an area under the curve of 0.760 （95% CI： 0.735-0.785）. The mean absolute error of the calibration curve was 0.006. The P value of the Hosmer-Lemeshow test was 0.609. CONCLUSIONS Batroxobin can rapidly and significantly reduce FIB levels and carries a risk of inducing severe HFIB. Patients with advanced age， high initial dose per 10 kg body weight， use of maintenance dose and concomitant glucocorticoid use had a higher risk of batroxobin-related severe HFIB， while high baseline FIB level had a lower risk of batroxobin-related severe HFIB. The risk prediction model developed based on these factors can be used to predict the likelihood of batroxobin-related severe HFIB.

OBJECTIVE To investigate the mechanism by which Naozhenning granules （NZN） improve learning and memory impairment in a rat model of multiple cerebral concussion （MCC）. METHODS The MCC rat model was established using the closed controlled cortical impact method. The experiment was set up with a blank group （normal saline）， a model group （normal saline）， a piracetam group （positive control group， 0.324 g/kg）， and high-， medium-， and low-dose NZN groups （5.4， 2.7， 1.35 g/kg）， with 11 rats in each group. Drugs or normal saline were administered by gavage once daily for 28 consecutive days. General condition and body weight were monitored throughout the experiment. The sucrose preference rate and novel object recognition index were measured； Evans blue （EB） extravasation in the cerebral cortex was detected； pathological changes of cortical neurons were observed； the levels of B-cell lymphoma-2 （Bcl-2）， Bcl-2-associated X protein （Bax）， interleukin-6 （IL-6）， IL-10， and tumor necrosis factor-α （TNF-α） in the cerebral cortex were determined； and the phosphorylation levels of AMP-activated protein kinase （AMPK）， glycogen synthase kinase 3β （GSK3β）， and Tau protein were detected. RESULTS Compared with the blank group， the model group showed poor mental state， sluggish response to external stimuli， reduced food and water intake， decreased limb flexibility， and disheveled fur. Body weight， sucrose preference rate， and novel object recognition index were significantly decreased （ P ＜0.05）； EB extravasation in the cerebral cortex was significantly increased （ P ＜0.05）， with severe neuronal damage. The positive area ratio of Bax protein， IL-6 and TNF-α levels， and Tau protein phosphorylation level were all significantly increased （ P ＜0.05）， whereas the positive area ratio of Bcl-2 protein， IL-10 level， and AMPK and GSK3β protein phosphorylation levels were significantly decreased （ P ＜0.05）. Compared with the model group， all NZN dose groups showed improvements in general condition and pathological damage， with quantitative indices partially restored， and the differences in quantitative indices in high-dose NZN group were statistically significant （ P ＜0.05）. CONCLUSIONS NZN can effectively improve learning and memory impairment in MCC model rats. The mechanism may be related to activating the AMPK/GSK3β pathway， inhibiting inflammatory response， reducing Tau protein phosphorylation level， and then repairing the neuronal injury.

Objective To explore the value of radiomics and deep transfer learning(DTL)based on gadoxetic acid disodium-enhanced magnetic resonance imaging(MRI)for preoperative prediction of microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was conducted using the MRI and clinicopathological data of 369 HCC patients who underwent surgery and had pathologically confirmed MVI at the Affiliated Hospital of Qingdao University from January 2019 to September 2024.According to the negative and positive manifestations of MVI,these patients were divided into MVI-group(n=219)and MVI+group(n=150);and they were then randomly assigned into the training set(n=258)and the test set(n=111)in a ratio of 7:3.Based on the hepatobiliary phase images,the optimal features were extracted and screened from radiomics features,DTL features,and the fusion features of the two.Nine machine learning models were constructed using 3 algorithms(random forest,multi-layer perceptron,and support vector machine,separately)and trained on radiomics features,DTL features,and the fusion features of the two.The diagnostic efficacy of each model was evaluated using receiver operating characteristic curve,and the optimal model was identified as the output model.Results Among all the constructed models,those based on fused features outperformed models using individual features.The random forest classifier model in the training set had the best performance,with an AUC of 0.998(95%CI:0.996-1.000),and was therefore selected as the output model in this study.Conclusion Radiomics and DTL models based on gadoxetic acid disodium-enhanced MRI can effectively predict the MVI in HCC.Among these,the random forest classifier model utilizing fused features in the training set exhibits the best performance.

Objective To analyze the reverse mechano-electric effect of the layered structure of articular cartilage and its influencing factors.Methods The cartilage samples were classified according to their physiological thickness(approximately 0.4 mm for the upper layer,1 mm for the middle layer,and 0.6 mm for the lower layer).Through a non-contact external electric field testing method,how different influencing factors affected the reverse mechano-electric effect of articular cartilage was analyzed.Results When the electric field spacing decreased,water content increased,and in vitro time decreased,the displacement of normal layered cartilage in a non-contact electric field increased by 18,10,15 μm,respectively.In the case of simulated arthritis defects,as the defect depth and radius increased,the overall deviation deflection of articular cartilage gradually decreased by about 7 μm.Conclusions The three-layer cartilage differed in their reverse mechano-electricity effects,showing the greatest deflection in the middle layer at 90％water content,under 7 mm electric field spacing,and after 12 hours ex vivo.

Intra-aortic balloon pump(IABP),as the most commonly used percutaneous mechanical circulatory assist device,is routinely implanted through the femoral artery pathway.However,when implantation through the femoral artery pathway is difficult or contraindicated,other pathways including the brachial artery pathway,axillary artery pathway,etc.can be considered.IABP assisted interventional treatment for complex high-risk and indicated patients(CHIP)can reduce the risk of intraoperative complications,stabilize hemodynamics,increase the complete revascularization rate of CHIP,and improve long-term prognosis.The reverse guidewire technique can improve the success rate of percutaneous coronary intervention(PCI)for chronic total occlusion(CTO)of coronary arteries.In clinical practice,the reverse pathway is often chosen in addition to the forward pathway,such as the contralateral radial and brachial artery pathway,femoral artery pathway,etc.This article reports a case of bilateral femoral artery occlusion,in which IABP was implanted through the left brachial artery pathway,and then with the assistance of IABP,the right coronary artery CTO lesion was successfully opened through the right radial artery in the forward direction and the right brachial artery in the reverse direction,with the aim of providing reference for clinical PCI treatment for such special cases.

Objective To explore the value of radiomics and deep transfer learning(DTL)based on gadoxetic acid disodium-enhanced magnetic resonance imaging(MRI)for preoperative prediction of microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was conducted using the MRI and clinicopathological data of 369 HCC patients who underwent surgery and had pathologically confirmed MVI at the Affiliated Hospital of Qingdao University from January 2019 to September 2024.According to the negative and positive manifestations of MVI,these patients were divided into MVI-group(n=219)and MVI+group(n=150);and they were then randomly assigned into the training set(n=258)and the test set(n=111)in a ratio of 7:3.Based on the hepatobiliary phase images,the optimal features were extracted and screened from radiomics features,DTL features,and the fusion features of the two.Nine machine learning models were constructed using 3 algorithms(random forest,multi-layer perceptron,and support vector machine,separately)and trained on radiomics features,DTL features,and the fusion features of the two.The diagnostic efficacy of each model was evaluated using receiver operating characteristic curve,and the optimal model was identified as the output model.Results Among all the constructed models,those based on fused features outperformed models using individual features.The random forest classifier model in the training set had the best performance,with an AUC of 0.998(95%CI:0.996-1.000),and was therefore selected as the output model in this study.Conclusion Radiomics and DTL models based on gadoxetic acid disodium-enhanced MRI can effectively predict the MVI in HCC.Among these,the random forest classifier model utilizing fused features in the training set exhibits the best performance.

Objective To analyze the reverse mechano-electric effect of the layered structure of articular cartilage and its influencing factors.Methods The cartilage samples were classified according to their physiological thickness(approximately 0.4 mm for the upper layer,1 mm for the middle layer,and 0.6 mm for the lower layer).Through a non-contact external electric field testing method,how different influencing factors affected the reverse mechano-electric effect of articular cartilage was analyzed.Results When the electric field spacing decreased,water content increased,and in vitro time decreased,the displacement of normal layered cartilage in a non-contact electric field increased by 18,10,15 μm,respectively.In the case of simulated arthritis defects,as the defect depth and radius increased,the overall deviation deflection of articular cartilage gradually decreased by about 7 μm.Conclusions The three-layer cartilage differed in their reverse mechano-electricity effects,showing the greatest deflection in the middle layer at 90％water content,under 7 mm electric field spacing,and after 12 hours ex vivo.

Intra-aortic balloon pump(IABP),as the most commonly used percutaneous mechanical circulatory assist device,is routinely implanted through the femoral artery pathway.However,when implantation through the femoral artery pathway is difficult or contraindicated,other pathways including the brachial artery pathway,axillary artery pathway,etc.can be considered.IABP assisted interventional treatment for complex high-risk and indicated patients(CHIP)can reduce the risk of intraoperative complications,stabilize hemodynamics,increase the complete revascularization rate of CHIP,and improve long-term prognosis.The reverse guidewire technique can improve the success rate of percutaneous coronary intervention(PCI)for chronic total occlusion(CTO)of coronary arteries.In clinical practice,the reverse pathway is often chosen in addition to the forward pathway,such as the contralateral radial and brachial artery pathway,femoral artery pathway,etc.This article reports a case of bilateral femoral artery occlusion,in which IABP was implanted through the left brachial artery pathway,and then with the assistance of IABP,the right coronary artery CTO lesion was successfully opened through the right radial artery in the forward direction and the right brachial artery in the reverse direction,with the aim of providing reference for clinical PCI treatment for such special cases.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.