Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

OBJECTIVE To elucidate the mechanisms responsible for the inhibitory effects of limonene on the proliferation of non-small cell lung cancer(NSCLC) by non-targeted metabolomics and additional approaches. METHODS The CCK-8 assay was utilized to evaluate the inhibitory effects of limonene on NSCLC A549 cell viability and to ascertain the IC50. In vitro experiments, encompassing colony formation, flow cytometry, iron content assessment, and mitochondrial staining, were conducted to assess the anti-lung cancer and iron-induced cell death effects of limonene. Metabolomic analysis was employed to identify potential pathways influenced by limonene, and Western blotting was carried out to validate pivotal proteins within these pathways. RESULTS In comparison to the control group, the limonene-treated group demonstrated a significant, dose-dependent reduction in A549 cell proliferation and colony formation. Optical microscopy revealed cellular detachment and pronounced changes in cellular morphology following exposure to limonene. Limonene induced apoptosis in A549 cells and arrested them in the G0-G1 phase of the cell cycle. Confocal microscopy unveiled diminished mitochondrial fluorescence and an augmented intracellular iron content, indicative of the classical phenomenon of ferroptosis. Metabolomic investigations unveiled divergent metabolic pathways, including glutathione(GSH) metabolism, arginine biosynthesis, D-glutamine and D-glutamate metabolism, as well as cysteine and methionine metabolism, with many of them intricately linked to intracellular GSH synthesis. Western blotting experiments underscored a marked reduction in the levels of SLC40A1, SLC7A11(xCT), and GPX4 proteins within the cells post-limonene treatment. CONCLUSION Limonene may induce ferroptosis in lung cancer cells by reducing GSH synthesis and increasing Fe2+ levels.

Objective To compare the anesthetic effect of sufentanil combined with propofol and remifentanil combined with propofol in hysteroscopic surgery, and to provide a scientific basis for the selection of clinical anesthesia methods. Methods From November 2016 to March 2017, 94 patients in Lishui central hospital underwent hysteroscopic surgery were divided into the observation group and the control group according the anesthesia way, 47 cases in each group. The control group were given remifentanil(1 μg/kg) combined with propofol(2 mg/kg) by intravenous injection, the observation group were given sufentanil(0.2 μg/kg) combined with propofol(2 mg/kg). The mean arterial pressure(MAP), respiration (RR), pulse oxygen saturation (SpO2), heart rate (HR), the onset time of anesthesia, the postoperative recovery time, the recovery time of orientation and Ramsay sedation score in the two groups were recorded and compared before anesthesia, 2 min after anesthesia, 10 min after operation, 10min after operation finished. Results Compared with before anesthesia, MAP, RR, SpO2, HR index decreased significantly after anesthesia, the differences have statistical significance (P<0.05), the control group compared to the index value, the observation group and the control group of convergence and the decline of difference, group showed no statistically significant difference the observation group was more stable; comparison between the 10min group and MAP HR index after surgery, there was significant difference between two groups (P<0.05). The onset time of anesthesia, postoperative recovery time, orientation recovery time, Ramsay score difference between the groups was not statistically significant sedation score difference was statistically significant between group VAS, observation group than in the control group (P<0.05). Conclusion Sufentanil combined with propofol anesthesia were used in hysteroscopic surgery, which can better maintain the vital signs of patients, effectively reduce postoperative pain.

Objective To analyze and investigate dopamine combined with milrinone treatment effect on elderly patients with intractable heart failure and N-terminal B-type natriuretic peptide level and cardiac function.Methods 80 cases of elderly patients with heart failure according to the number table method randomly divided into two groups: control group and experimental group, and control group was given conventional drugs.The patients in experimental group were received dopamine +Milrinone on the basis of control group.Clinical efficacy, N-terminal pro-B-type natriuretic peptide levels ( NT proBNP ) and heart function condition between two groups are compared and analyzed.Results The total effective rate of experimental group (95.00%) was higher than that of control group (70.00%) (P<0.05).NT-proBNP(2013.31 ±295.84)ng/L、LVEDD(61.48 ± 10.11)mm、LVEF(59.69 ±8.44)% in the experimental group was significantly better than the control group(P <0.05).Conclusion Dopamine combined with milrinone in the treatment of elderly patients with intractable heart failure is remarkable, can relieve the level of NT proBNP, and promote the recovery of cardiac function.