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 investigate the application value of nasolabial flaps in addressing tissue defects after resection of benign and malignant nasal vestibular lesions. Methods:The clinical data of patients with benign and malignant nasal vestibular lesions were analyzed retrospectively. There were 4 cases of squamous cell carcinoma, 2 cases of black hairy nevus and 1 case of chronic proliferative inflammatory lesions, all of which were repaired by adjacent nasolabial flap. Results:After 6 months of follow-up, none of the patients developed nasal vestibular contracture or nostril stenosis, and postoperative nasal ventilation function was good. Conclusion:The preoperative design of individual nasolabial flaps is very important for maintaining maxillofacial aesthetics, protectingthe nasolabial framework, and preserving postoperative nasal ventilation function.
Humans ; Retrospective Studies ; Middle Aged ; Nose Neoplasms/surgery* ; Surgical Flaps ; Male ; Female ; Adult ; Nose/surgery* ; Plastic Surgery Procedures/methods* ; Carcinoma, Squamous Cell/surgery* ; Aged ; Skin Transplantation

OBJECTIVE: To observe the effects of point-moxibustion with Zhuang medicinal thread on differentially expressed genes (DEGs) in the dorsal root ganglion (DRG), tissue morphology, and the expression of Fc epsilon RI (FcεRI) pathway proteins spleen tyrosine kinase (Syk) and membrane spanning 4-domain A2 (MS4A2) in rat model of postherpetic neuralgia (PHN), and to explore the potential mechanism by which this therapy alleviates pain sensitization. METHODS: Thirty-nine male Sprague-Dawley (SD) rats were randomly divided into a control group, a model group, and a moxibustion group, with 13 rats in each group. The PHN model was established in the model and moxibustion groups by intraperitoneal injection of resiniferatoxin. In the moxibustion group, bilateral L₄-L₆ "Jiaji" (EX-B2) points were treated with point-moxibustion with Zhuang medicinal thread from day 7 post-modeling, with two cones per acupoint per session, every other day for a total of 10 sessions. Mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) were measured at 1 day before modeling and on days 1, 4, 7, 13, 19, and 25 after modeling. After intervention, HE staining was used to observe DRG morphology. RNA sequencing was performed to analyze DEGs in DRG and conduct bioinformatics analysis. The expression of Syk and MS4A2 mRNA and proteins in the FcεRI pathway in DRG was detected by quantitative PCR and Western blot. RESULTS: Compared with the control group, the model group exhibited decreased MWT (P<0.05) and increased TWL (P<0.05); histopathological analysis revealed neuronal atrophy, nuclear displacement, and intracellular vacuoles, with a slightly loose arrangement; the RNA-Seq identified 3,207 DEGs (1,997 upregulated and 1,210 downregulated); the mRNA and protein expression levels of Syk and MS4A2 were significantly increased (P<0.01). Compared with the model group, the moxibustion group showed increased MWT (P<0.05) and decreased TWL (P<0.05), with relatively normal neuronal morphology; the RNA-Seq identified 426 DEGs (250 upregulated and 176 downregulated); the mRNA and protein expression levels of Syk and MS4A2 were significantly decreased (P<0.05). Venn diagram analysis identified 156 DEGs that showed a reversal in expression trends after treatment, including Syk and MS4A2, which are associated with pain sensitization. KEGG pathway analysis indicated that these DEGs were primarily enriched in the FcεRI pathway. CONCLUSION Point-moxibustion with Zhuang medicinal thread could alleviate pain sensitization in PHN rats, possibly by inhibiting the FcεRI signaling pathway and downregulating the expression of Syk and MS4A2.
Animals ; Rats, Sprague-Dawley ; Male ; Rats ; Moxibustion ; Neuralgia, Postherpetic/physiopathology* ; Syk Kinase/metabolism* ; Acupuncture Points ; Humans ; Ganglia, Spinal/metabolism* ; Signal Transduction

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.