Bupivacaine is a long-acting local anesthetic widely used clinically,but its 6-8 hours duration is insufficient for postoperative analgesia.Designing drug dosage forms to prolong the action time of local anesthetics is the research content of local anesthetics.In 2011,the FDA approved a bupivacaine liposome preparation based on DepoFoam TM technology:Exparel,which can be used for more than 72 hours.This article reviewed the clinical safety research of Exparel and the research progress of its application in clinical postoperative analgesia.

Objective:To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms.Methods:The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and P value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms—random forest, decision tree, and XGBoost—were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. Results:A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95% CI 0.813-0.995), 0.956 (95% CI 0.796-0.992), and 0.973 (95% CI 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95% CI 0.557-0.964), 0.706 (95% CI 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. Conclusion:Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.

Objective To study the effect and mechanism of paeonol(PAE)on renal interstitial fibrosis in rats.Methods The rats were randomly divided into sham operation(Sham)group,unilateral ureteral obstruction(UUO)group,PAE low dose(PAE-L)group,PAE medium dose(PAE-M)group,PAE high dose(PAE-H)group and irbesartan(IRB)group.Except for the Sham group,the UUO model was established in other groups.Each group was given a corresponding intervention for two weeks.Serum creatinine(Scr),blood urea nitrogen(BUN),serum 8-hydroxydeoxyguanosine(8-OHdG)levels,renal tissue superoxide dismutase(SOD),glutathione peroxidase(GPX)activities,α-smooth muscle actin(α-SMA),type Ⅰ collagen(Col-Ⅰ),fibronectin(FN),silent information regulator 1(SIRT1),nuclear factor E2-related factor 2(Nrf2)protein expression were detected;observe pathological changes of kidney tissue and calculate collagen volume fraction(CVF).Results Compared with the UUO group,the serum levels of Scr,BUN,and 8-OHdG in each dose group of PAE were decreased,the activities of SOD and GPX in kidney tissue were increased,the positive expressions of α-SMA,Col-Ⅰ and FN in kidney tissue were decreased,and the protein expressions of SIRT1 and Nrf2 were increased.Masson staining showed a decrease of CVF in renal tissue(all P<0.05),and HE staining showed a different degree of improvement in pathological changes such as inflammatory cell infiltration and tubular dilatation in renal tissue;PAE improves renal interstitial fibrosis in rats in a dose-dependent manner(P<0.05),and the effect of large dose PAE on renal interstitial fibrosis in rats was similar to that of IRB.Conclusion PAE can alleviate UUO-induced rat renal interstitial fibrosis and oxidative stress,and improve rat renal function.And this mechanism may be related to the activation of the SIRT1/Nrf2 signaling pathway.

During the pain treatment process,to reduce the use of opioids,alternative pain relievers have been used.Increasing data indicated that N-methyl-D-aspartic acid(NMDA)receptor antagonists may enhance the analgesic effect of opioids.Furthermore,numerous studies have shown that the auxiliary analgesic effect of magnesium sulfate has a good application prospect in pain treatment and perioperative anesthesia.The primary pharmacological mechanism of magnesium sulfate in pain treatment is that it acts as a physiological voltage-dependent blocker of NMDA coupling channels,and its anti-nociceptive effect is related to magnesium blocking calcium inflow,thereby inhibiting central sensitization and reducing preexisting hyperalgesia.This paper reviewed the current application of magnesium sulfate in perioperative period,focusing on its auxiliary analgesic effect through intravenous,intrathecal,and local administration,and the reduction of opioid consumption.As well as its potential role and application prospects in opioid-free anesthesia program.

Objective To compare the radiation dose of in vitro pre-fenestration and in situ fenestration thoracic endovascular aortic repair(TEVAR)in treatment of aortic disease.Methods Data of 51 patients with aortic diseases who received in vitro pre-fenestration(group A)and 21 cases who underwent in situ fenestration(group B)TEVAR were retrospectively analyzed.The fluoroscopy duration,total reference air kerma(AK),total dose area product(DAP)and TEVAR time were compared between groups.Results TEVAR was successfully completed in all 72 patients.Fluoroscopy duration([21.42±8.04]min vs.[34.57±9.07]min)and total DAP(44315.0[31157.0,56307.5]μGy·m2 vs.72153.0[45460.0,82354.0]μGy·m2)in group A were both significantly lower than those in group B(both P＜0.05),while total AK(2423[1638,3533]mGy vs.3600[1898,3921]mGy)and TEVAR time([83.41±22.89]min vs.[81.00±22.13]min)in group A were not significant different from those in group B(both P＞0.05).Conclusion Compared with in situ fenestration TEVAR,both the fluoroscopy time and total DAP of in vitro pre-fenestration TEVAR significantly reduced for treating aortic diseases.

Objective:To investigate the value of radiomics features derived from cardiac MR (CMR) cine images for predicting late gadolinium enhancement (LGE) in patients with hypertrophic cardiomyopathy (HCM).Methods:Firstly, a total of 300 HCM patients with definite diagnosis who underwent CMR examination in Beijing Anzhen Hospital from May 2017 to August 2021 were retrospectively included, and were divided into a training set and a test set with a proportion of 7∶3 using random stratified sampling method. Then, a total of 89 HCM patients with definite diagnosis who underwent CMR examination in Beijing Anzhen Hospital from January 2022 to May 2023 were included for external validation. The CVI42 software was used to obtain the cardiac function parameters. Intraclass correlation coefficient (ICC), Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Finally, LASSO regression and three machine learning algorithms (including support vector machine, linear discriminant analysis and naive Bayes) were used to build prediction models. The area under the receiver operating characteristic curve (AUC) was used to evaluate the prediction value of the model.Results:Totally 1 409 features were extracted from each patient, and 19 features were retained to build radiomics signature after dimension reduction. Although no significant differences among the four methods, the prediction performance and stability of LASSO regression were relatively good. The AUC was 0.795 (95%CI 0.735-0.855) in the training set, 0.765 (95%CI 0.668-0.862) in the test set and 0.721(95%CI 0.598-0.845) in the external validation set.Conclusions:The features extracted from CMR cine images can be used to predict LGE in HCM patients. LASSO regression is recommended for model construction.

Background The contradiction between science and operability has always existed in the model of classified occupational health supervision and management. Comprehensive risk assessment method for occupational disease hazards of employers provides risk grading and classification for occupational health management, and it's a new attempt to explore classification supervision and law enforcement. Objective To apply a comprehensive risk assessment method for occupational disease hazards of employers to estimate health risk level of wood furniture enterprises, discuss its advantages and disadvantages, and provide a basis for improving the classified management of occupational health. Methods Seven typical factories were selected in counties with highly concentrated wooden furniture manufacturing. Occupational health field investigation and testing were carried out to estimate occupational disease hazard risk level (Ⅰ, Ⅱ, and Ⅲ). A self-rated checklist was applied to score occupational health management status by interviewing employers, one by one, and to determine occupational health management status grade (A, B, and C) by the score. Thus, a comprehensive risk level (Class A, Class B, and Class C) of a specific factory was obtained from a matrix of occupational disease hazard risk level and management status grade. Risk verification was carried out based on any abnormality reported by regular occupational physical examination in past 3 years. Results Defects in occupational health management were identified in all 7 factories, among which 6 were grade C with key nonconformities (poor), and 1 was grade B (medium). Disqualified occupational disease hazards were found in 6 of 7 factories, and the workstation-specific disqualified rates were 26.09% (12/46) for noise, 14.71% (5/34) for wood dust (hard), and 12.50% (1/8) for xylene. Level Ⅱ (medium) of occupational disease hazard risk was estimated in 5 of 7 factories, while level Ⅲ (high) in 2 factories. All 7 factories were class C (high risk) accessed by the comprehensive risk assessment method for occupational disease hazards. The occupational health surveillance documents showed 636 batches of regular occupational physical examination were ordered by the 7 employers, and a total of 37 workers from 5 factories reported abnormalities in physical examination, among which 3 workers reported dust exposure and 34 workers reported noise exposure. Conclusion The comprehensive risk assessment method for occupational disease hazards of employers is not able to perform with satisfaction in occupational health classification of wooden furniture manufacturing factories yet. It is necessary to expand the pilot to improve this assessment method and develop an efficient supervision mechanism to ensure the authenticity of the data before it is popularized and applied in classified occupational health management.

Objective:To investigate the levels of serum surface active protein D (SP-D) and clara cell protein (CCl6) in workers exposed to black silica dust, and analyze its influencing factors.Methods:From July to September 2021, 174 workers in 37 positions exposed to silica dust in 5 ferrous metal foundry were investigated by cross-sectional research method. The exposure concentration of silica dust workers was obtained through occupational health field investigation and detection, and the general situation of the study subjects was obtained through questionnaire survey and peripheral blood was collected. Double antigen sandwich enzyme-linked immunosorbent assay (ELISA) was used to detect the concentrations of SP-D and CC16 in serum of workers. The mean values were compared by one-way ANOVA, and the influencing factors of SP-D and CC16 concentrations in serum were analyzed by ordered multiple logistic regression.Results:The time-weighted average concentration (C-TWA) of 174 workers exposed to silica dust (respirable dust) ranged from 0.09 mg/m 3~3.58 mg/m 3, and the C-TWA overstandard rate of dust exposed workers was 32.18% (56/174) , with differences among workers in different positions (χ 2=28.85, P<0.001) . The highest concentration of silica dust was (0.82±0.11) mg/m 3. Using C-TWA<50% OEL occupational exposure limit (OEL) as reference, serum SP-D concentration in workers with ≥50% OEL was increased ( OR=4.95, 95% CI: 1.86~13.17, P=0.001) , while CC16 concentration was decreased ( OR=0.15, 95% CI: 0.05~0.40, P<0.001) ; Serum CC16 concentration decreased in workers exposed to silica dust C-TWA≥OEL ( OR=0.46, 95% CI: 0.28~0.98, P=0.043) . Compared with those with low occupational health literacy, the serum SP-D concentration of workers with high occupational health literacy decreased ( OR=0.48, 95% CI: 0.25~0.92, P=0.027) and CC16 concentration increased ( OR=2.09, 95% CI: 1.10-3.97, P=0.024) . Conclusion:When no abnormality was found in the physical examination of workers, the serum SP-D and CC16 concentration levels changed, and the change was related to the concentration of workers exposed to silica dust.

Objective:To establish a method for the rapid determination of the three metabolites of xylene, 2-methylmarmaluronic acid, 3-methylmarmaluronic acid and 4-methylmarmaluronic acid, in urine of occupationally exposed workers by ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) .Methods:In July 2022, urine samples were diluted and extracted with pH=6.86 phosphate cache solution, cleaned up by a MAX solid-phase extraction (SPE) column and separated by an Accucore Ph/Hexyl column (100 mm×2.1 mm, 2.6 μm) with a gradient of 5 mmol/L ammonium formate-0.1% formic acid aqueous solution and methanol as mobile phases. The analysis was carried out in electrospray ionization mode and full mass-data dependent secondary mass spectrometry mode, and quantified by external standard method. The characteristics of each index of this method were analyzed.Results:A good linearity was obtained in the concentration range of 1.0-200.0 μg/L for 2-methylmuramic acid, 3-methylmuramic acid and 4-methylmuramic acid with the correlation coefficients of 0.9979-0.9993. The limits of detection of the method were 0.18-0.24 μg/L. While the spiked recoveries at the three concentrations (1.0 μg/L, 100.0 μg/L, and 180.0 μg/L) were in the range of 83.0%-93.7%, with the relative standard deviations of 2.2%-7.9%.Conclusion:The UPLC-HRMS method is simple, rapid, sensitive and accurate, and is suitable for the simultaneous determination of the three metabolites of xylene in the urine of occupationally exposed workers.

Objective:To investigate the levels of serum surface active protein D (SP-D) and clara cell protein (CCl6) in workers exposed to black silica dust, and analyze its influencing factors.Methods:From July to September 2021, 174 workers in 37 positions exposed to silica dust in 5 ferrous metal foundry were investigated by cross-sectional research method. The exposure concentration of silica dust workers was obtained through occupational health field investigation and detection, and the general situation of the study subjects was obtained through questionnaire survey and peripheral blood was collected. Double antigen sandwich enzyme-linked immunosorbent assay (ELISA) was used to detect the concentrations of SP-D and CC16 in serum of workers. The mean values were compared by one-way ANOVA, and the influencing factors of SP-D and CC16 concentrations in serum were analyzed by ordered multiple logistic regression.Results:The time-weighted average concentration (C-TWA) of 174 workers exposed to silica dust (respirable dust) ranged from 0.09 mg/m 3~3.58 mg/m 3, and the C-TWA overstandard rate of dust exposed workers was 32.18% (56/174) , with differences among workers in different positions (χ 2=28.85, P<0.001) . The highest concentration of silica dust was (0.82±0.11) mg/m 3. Using C-TWA<50% OEL occupational exposure limit (OEL) as reference, serum SP-D concentration in workers with ≥50% OEL was increased ( OR=4.95, 95% CI: 1.86~13.17, P=0.001) , while CC16 concentration was decreased ( OR=0.15, 95% CI: 0.05~0.40, P<0.001) ; Serum CC16 concentration decreased in workers exposed to silica dust C-TWA≥OEL ( OR=0.46, 95% CI: 0.28~0.98, P=0.043) . Compared with those with low occupational health literacy, the serum SP-D concentration of workers with high occupational health literacy decreased ( OR=0.48, 95% CI: 0.25~0.92, P=0.027) and CC16 concentration increased ( OR=2.09, 95% CI: 1.10-3.97, P=0.024) . Conclusion:When no abnormality was found in the physical examination of workers, the serum SP-D and CC16 concentration levels changed, and the change was related to the concentration of workers exposed to silica dust.