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.

Bronchial asthma(hereinafter referred to as asthma)is a common chronic respiratory disease characterized by airway inflammation,airway hyperresponsiveness,and airway remodeling.Its pathogenesis is highly complex and heterogeneous,involving multiple factors such as genetics,immunity,and environmental exposure.Currently,therapeutic options for asthma remain relatively limited,making it an urgent priority to explore its underlying mechanisms,identify effective treatment strategies,and develop new drugs.In this context,the establishment of animal models for asthma plays an irreplaceable and crucial role.However,to date,no single ideal animal model has been able to fully and accurately replicate all the features of the onset and progression of human asthma.This study systematically reviews the research progress over the past five years in the establishment methods of asthma animal models.It provides a detailed overview of commonly used experimental animals(such as mice,rats,and guinea pigs),frequently used sensitizing agents(including ovalbumin,house dust mite,lipopolysaccharide,and toluene diisocyanate),and the methods for establishing asthma models using these animals and sensitizers.This study also presents an objective evaluation of the advantages,limitations,and applicability of each model.Evaluation criteria for asthma models are summarized across multiple dimensions,including behavioral assessments,pulmonary function,histopathology,immunological indicators,and pharmacodynamics.Although methods for establishing refractory asthma models remain underdeveloped,several strategies for modeling refractory asthma have been summarized through a review of relevant literature,aiming to provide useful references for related research.Based on current scientific and technological advancements,it is anticipated that future research on asthma animal models will focus more on clinical relevance,technological innovation,and multidisciplinary integration.Specifically,future models are expected to adopt multi-sensitizer induction protocols,apply cutting-edge tools such as gene editing,enhance clinical relevance and promote diversification and personalization of models.Furthermore,advanced technologies such as bioimaging and biosensing are anticipated to enable dynamic monitoring of airway inflammation and remodeling.Organ-on-a-chip platforms may also be explored as potential alternatives to traditional animal models.The ultimate goal is to develop multifactorial,composite models that better simulate the complexity and heterogeneity of human asthma.

Bronchial asthma(hereinafter referred to as asthma)is a common chronic respiratory disease characterized by airway inflammation,airway hyperresponsiveness,and airway remodeling.Its pathogenesis is highly complex and heterogeneous,involving multiple factors such as genetics,immunity,and environmental exposure.Currently,therapeutic options for asthma remain relatively limited,making it an urgent priority to explore its underlying mechanisms,identify effective treatment strategies,and develop new drugs.In this context,the establishment of animal models for asthma plays an irreplaceable and crucial role.However,to date,no single ideal animal model has been able to fully and accurately replicate all the features of the onset and progression of human asthma.This study systematically reviews the research progress over the past five years in the establishment methods of asthma animal models.It provides a detailed overview of commonly used experimental animals(such as mice,rats,and guinea pigs),frequently used sensitizing agents(including ovalbumin,house dust mite,lipopolysaccharide,and toluene diisocyanate),and the methods for establishing asthma models using these animals and sensitizers.This study also presents an objective evaluation of the advantages,limitations,and applicability of each model.Evaluation criteria for asthma models are summarized across multiple dimensions,including behavioral assessments,pulmonary function,histopathology,immunological indicators,and pharmacodynamics.Although methods for establishing refractory asthma models remain underdeveloped,several strategies for modeling refractory asthma have been summarized through a review of relevant literature,aiming to provide useful references for related research.Based on current scientific and technological advancements,it is anticipated that future research on asthma animal models will focus more on clinical relevance,technological innovation,and multidisciplinary integration.Specifically,future models are expected to adopt multi-sensitizer induction protocols,apply cutting-edge tools such as gene editing,enhance clinical relevance and promote diversification and personalization of models.Furthermore,advanced technologies such as bioimaging and biosensing are anticipated to enable dynamic monitoring of airway inflammation and remodeling.Organ-on-a-chip platforms may also be explored as potential alternatives to traditional animal models.The ultimate goal is to develop multifactorial,composite models that better simulate the complexity and heterogeneity of human asthma.

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.

In order to establish a specific,rapid and convenient method for the detection of Campy-lobacter jejuni(C.jejuni).A set of specific primers and a probe that do not cause false positives were designed with the hipO gene of C.jejuni as the target,and the 5'ends of the downstream primers and probes were labeled with biotin and fluorescein,respectively.C.jejuni-RPA-LFD had no cross-reactivity with Klebsiella pneumoniae,Escherichia coli,Pseudomonas,Bacillus cereus,Pasteurella,Proteus mirabilis,and Salmonella typhimurium,and the optimal reaction system was 37 ℃,25 min,and its sensitivity could reach 3.93×100 copies/μL,and 1 × 102 CFU/mL of C.jejuni contaminated stool samples could be detected in the simulated detection.The C.jejuni-RPA-LFD established in this study has the advantages of good specificity,simplicity,rapidity and high sensitivity,which provides an effective way for the rapid diagnosis of C.jejuni and the con-trol of the spread of C.jejuni at the grassroots level of livestock and poultry farming.

In order to establish a specific,rapid and convenient method for the detection of Campy-lobacter jejuni(C.jejuni).A set of specific primers and a probe that do not cause false positives were designed with the hipO gene of C.jejuni as the target,and the 5'ends of the downstream primers and probes were labeled with biotin and fluorescein,respectively.C.jejuni-RPA-LFD had no cross-reactivity with Klebsiella pneumoniae,Escherichia coli,Pseudomonas,Bacillus cereus,Pasteurella,Proteus mirabilis,and Salmonella typhimurium,and the optimal reaction system was 37 ℃,25 min,and its sensitivity could reach 3.93×100 copies/μL,and 1 × 102 CFU/mL of C.jejuni contaminated stool samples could be detected in the simulated detection.The C.jejuni-RPA-LFD established in this study has the advantages of good specificity,simplicity,rapidity and high sensitivity,which provides an effective way for the rapid diagnosis of C.jejuni and the con-trol of the spread of C.jejuni at the grassroots level of livestock and poultry farming.

Objective To analyze the spatial and temporal characteristics of hand, foot and mouth disease (HFMD) in Hunan Province from 2016 to 2020. Methods The data of HFMD in Hunan Province from 2016 to 2020 were collected from China's Disease Prevention and Control Information System. HFMD spatial autocorrelation analysis was conducted by ArcGIS 10.2 software at county level, and spatial-temporal scan statistical analysis was performed by SaTScan 9.7 software. Results A total of 714 157 cases was reported in Hunan Province during 2016-2020, with an average annual incidence rate of 208.36/100 000. Global spatial autocorrelation showed that HFMD had a positive spatial correlation on the county scale in Hunan Province during this period. Local spatial autocorrelation indicated that the hot spots were mainly concentrated in the north of central Hunan, the east of central Hunan and the west of Hunan. Spatial-temporal scanning analysis revealed the first class clusters (RR = 6.65, P< 0.001) covering 34 counties in northern and central Hunan, mainly distributed in Yueyang City, Changsha City, Zhuzhou City, Yiyang City and Xiangtan City from May 2018 to June, and the second class clusters (RR = 3.02, P < 0.001) covering 40 counties in western Hunan and central and southwest Hunan from April 2016 to June 2016. Conclusion HFMD incidence exhibits seasonal and regional characteristics in Hunan Province. The prevention and control of HFMD should be guided by combining the characteristics of spatial-temporal clustering.

Starting from the characteristics of nervous system,breaking the traditional subject-centered teaching model,combining the nervous system-related basic and clinical courses,Chongqing Medical University has established a nervous-module organ-system integration curriculum since 2010.This new teaching model is introduced and practiced in 5-year outstanding medical class of Chongqing Medical University.The new integrated curriculum guides students to learn knowledge from points to surface,helps them to combine knowledge vertically and horizontally,and simplifies the duplicate teaching content,so as to promote students to develop coherent and innovative thinking mode.Integrated curriculum pattern is an inevitable trend in medical education reform.

Objective To improve the diagnostic and therapeutic ability of Brucellosis by analyzing the epidemic and clinical characteristics.Methods A retrospective analysis was done on the data of Brucellosis patients treated in our hospital from 2007 to 2016.Results Since the first case was diagnosed in 2012,27 patients [19 male and 8 female,mean age (44.4 ± 16.9) years] were confirmed by clinical manifestations and positive bacterial cultures results.The annual number of cases from 2012 to 2016 was 1,1,6,4 and 15.Among them,10 cases (37.0％) had a history of close contact with goat,7 cases (25.9％)with raw mutton,1 case (3.7％) with raw beef and 1 case (3.7％) with suspicious laboratory contamination while 8 cases (29.6％) had no evident risk factors for Brucellosis.The common clinical manifestation included fever (81.5％),lumbago/joint pain (55.6％),fatigue (33.3％) and hyperhidrosis (22.2％).The white blood cell count was normal among 20 cases (74.1％) while 6 cases (22.2％) with leukopenia.Mild to moderate anemia in 20 cases (74.1％) and decreased platelet number in 4 case (14.8％).The percentage of elevated alanine aminotransferase,aspartate aminotransferase and lactate dehydrogenase was 32.0％,48.0％ and 100％,respectively.Decreased albumin level was found in 23 cases (92.0％).The percentage of elevated erythrocyte sedimentation rate (ESR),C-reactive protein and serum ferritin 75.0％,82.3％ and 77.8％,respectively,while 12 cases (85.7％) with procalcitonin level below 0.5 ng/L.According to follow-up for at least half year,all the cases were cured by active medical management.Conclusions The number of Brucellosis cases is rapidly increasing in our hospital.It's of great significance to know the epidemic and clinical characteristics of Brucellosis.

Objective To investigate the benefits of replanning after induction chemotherapy(IC) by analyzing the dosimetric impact of IC on intensity-modulated radiotherapy(IMRT)for locally advanced nasopharyngeal carcinoma(NPC)and the dosimetric characteristics of replanning after IC, and to provide data for the rational design of clinical radiotherapy plans. Methods 16 NPC patients underwent contrast-enhanced CT scan once before and after IC.Target volumes were delineated and the chemotherapy plans were created,defined as Plan-1 and Plan-2,respectively. Then the target structure after IC was copied to Plan-1, generating the third plan, defined as Plan-1-2. The paired t-test was used to compare the dosimetric parameters between Plan-1 and Plan-1-2 and between Plan-2 and Plan-1-2. Results Plan-1 vs. Plan-1-2:Plan-1-2 showed significantly reduced D meanof target volume compared with Plan-1(P<0.05). Plan-1-2 significantly increased D meanand D maxof the spinal cord(P<0.05),although significantly reduced D mean of the brain stem and D maxof the temporal lobes compared with Plan-1. Plan-1-2 also had significantly reduced conformity index(CI)and significantly increased homogeneity index(HI)for the target volume compared with Plan-1(P<0.05). Plan-2 vs. Plan-1-2:Compared with Plan-1-2, Plan-2 significantly increased D meanand D minof gross tumor volume(GTV)and primary GTV(P<0.05)and significantly reduced D meanof the temporal lobes and D maxand D meanof the spinal cord(P<0.05), with D max decreased to 430.48 cGy;Plan-2 had significantly increased CI and significantly reduced HI for the target volume compared with Plan-1-2(all P<0.05). Conclusions IMRT plan-1 after IC has worse dosimetric distribution,while replanning after IC has more dosimetric benefits.