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 mine adverse drug event(ADE)signals of doxycycline using the U.S.Food and Drug Administration Adverse Event Reporting System(FAERS)database,and provide scientific evidence for clinical medication safety.Methods The data from the FAERS database between the first quarter of 2004 and the first quarter of 2024 were extracted.After data cleaning and standardization,ADE reports with doxycycline as the main suspected drug were screened.The system organ class(SOC)of ADE was performed using MedDRA,and the reporting odds ratio method and Medicines and Healthcare products Regulatory Agency method were used to mine ADE signals.The information component method was also used to evaluate signal strength.Results A total of 43 126 ADE reports with doxycycline as the primary suspected drug were collected,involving 14 642 patients,with a higher proportion of female patients(57.32％).There were 555 related ADE signals involving 26 SOC,with the top 5 SOC being gastrointestinal disorders,skin and subcutaneous tissue disorders,injuries,poisonings,and procedural complications,psychiatric disorders,and infections and infestations.The top 5 ADE signals with the highest signal intensity were Hatch reaction,sclerosing cholangitis,esophageal ulcer,gastrointestinal mucosal necrosis,and gastrointestinal injury.Among the ADE signals with the strongest signal strength not listed in the package insert,the top five were sclerosing cholangitis,nephrogenic diabetes insipidus,minimal change glomerular nephritis,diabetes insipidus and Sixth cranial nerve paralysis.Conclusion In clinical practice,particular attention should be paid to the frequent ADEs caused by doxycycline,as well as those not yet documented in the package insert,which involve multiple SOC such as renal and urinary disorders,hepatobiliary diseases,blood and lymphatic system disorders,and endocrine disorders.Therefore,clinical pharmacists should play a key role in assisting clinicians to develop and implement prevention plans for ADEs,thereby improving the safety of doxycycline in clinical use.

Objective To establish and optimize a loop-mediated isothermal amplification (LAMP) method for the rapid detection of Escherichia coli and its microbial toxin.Methods The LAMP reaction system and reaction conditions were determined by optimizing LAMP reaction,and the optimized LAMP system was used for the detection.Results Primers targeting shiga toxin (stx) gene and O157 antigen gene rfbe were designed.The established and optimized LAMP amplification system contained 1.2 mmol/L dNTPs,10 mmol/L MgSO4,0.4 mol/L betaine,1 μl 10 × Bst DNA polymerase Buffer,8 U Bst DNA polymerase fragment,2 μl DNA template,and the ratio of inner-primer (FIP and BIP) and outerprimer (F3 and B3) were 8∶ 1.Time and temperature for LAMP was 60 min,60 ℃.The sensitivity was 103 times higher than polymerase chain reaction (PCR),reached 5 × 101 CFU/ml.When LAMP was applied to 19 reference strains,102 EHEC strains,the specification was 100％ while identification rate of rfbe,stx1 and stx2 gene reached 100％,95.2％,92.9％.Conclusions The LAMP method showed a promising prospect for the rapid detection of common nosocomial pathogens microbial toxin.

Objective To explore the clinical effect of self-made electric stoma irrigation pot.Methods A total of 60 patients with intestinal stoma in our hospital were randomly divided into control group and experimental group.The control group was given stoma cleaning bottle for enterostoma washing,and the experimental group used an electric stoma irrigation pot for stoma washing.Mastery condition of self-care,rinse time,usage life of ostomy bag and incidence of fecal dermatitis around the stoma were compared between the two groups.Results The experimental group had higher self-care mastery in were higher than those in the control group (P ＜ 0.05).Rinse time was shorter in experimental group (P ＜ 0.05).Life length of enterostoma bag was longer in experimental group (P ＜ 0.05).The incidence of fecal dermatitis around the stoma was lower in experimental group than that in the control group (P ＜ 0.05).Conclusion The electric stoma irrigation pot for patients with intestinal stoma is featured by easy mastery and operation for family members and patients to flush the stool,so it is worthy of popularization in clinic.

Objective To explore the clinical effect of self-made electric stoma irrigation pot.Methods A total of 60 patients with intestinal stoma in our hospital were randomly divided into control group and experimental group.The control group was given stoma cleaning bottle for enterostoma washing,and the experimental group used an electric stoma irrigation pot for stoma washing.Mastery condition of self-care,rinse time,usage life of ostomy bag and incidence of fecal dermatitis around the stoma were compared between the two groups.Results The experimental group had higher self-care mastery in were higher than those in the control group (P ＜ 0.05).Rinse time was shorter in experimental group (P ＜ 0.05).Life length of enterostoma bag was longer in experimental group (P ＜ 0.05).The incidence of fecal dermatitis around the stoma was lower in experimental group than that in the control group (P ＜ 0.05).Conclusion The electric stoma irrigation pot for patients with intestinal stoma is featured by easy mastery and operation for family members and patients to flush the stool,so it is worthy of popularization in clinic.

Objective To explore the values of diffusion-weighted imaging(DWI)and apparent diffusion coefficient(ADC)measurements in various lesions of multiple sclerosis(MS).Methods Sixty patients with clinically diagnosed remitting-relapsing MS(RRMS)were included to undergo conventional brain MRI and DWI scans.the lesions were included when the diameter was more than 5 mm.mean ADC values were measured for various lesions of MS.The statistical analyses were performed to determine the differences of mean ADC values among various lesions of MS.and to compare the correlation between ADC values of lesions and Expanded Disability Status Scale(EDSS)scores.Results (1)The ADC value of hypointense lesions was significantly higher than that of isointense lesions(F=55.90,P<0.05),the ADC values were(127.5 ±9.3)×10-5mm2/s and(95.7 ±6.3)×10-5mm/s respectively.The nodular enhancing lesions had a significantly lower ADC value than the ring-enhancing lesions(F=64.18,P<0.01).the ADC values were(114.7 ±12.3)×10-5mm2/s and(140.7 ±11.0)×10-5mm2/s respectively.The ADC value of confluent lesions was substantially higher that of discrete lesions(t=9.04,P<0.01).the ADC values were(141.4±6.5)×10-5mm2/s and(105.4±13.9)×10-5mm2/s respectively.(2)No correlation between ADC of lesions and EDSS scores was found(r=0.35,P>0.05).Conclusion DWI and quantitative ADC are useful to explain the pathological changes in different lesions and to monitor the disease duration of MS.