BACKGROUND:Delayed healing and nonunion of fractures are common clinical issues.Clinical observations have shown that patients with limb fractures combined with traumatic brain injury experience significantly faster fracture healing compared with those without brain injury.The potential mechanisms behind this phenomenon have become a crucial focus of current research.Recent studies indicate that traumatic brain injury significantly accelerates callus formation and fracture healing processes by regulating cytokines,hormones,neural signals,and stem cell mechanisms.OBJECTIVE:To summarize the latest research progress in the mechanisms by which traumatic brain injury promotes callus formation and fracture healing,thereby providing a theoretical basis for clinical applications.METHODS:The first author conducted a search of CNKI,WanFang,VIP,PubMed,Embase,Web of Science,and Cochrane Library databases for literature published from January 2013 to October 2024,with some references traced back up to 20 years.The search terms used were"traumatic brain injury,callus,fracture healing,inflammatory response,cytokines,hormones,neuropeptides,genes,stem cells"in Chinese and English.A total of 83 articles meeting the inclusion criteria were ultimately selected.RESULTS AND CONCLUSION:The mechanism by which traumatic brain injury promotes callus formation and fracture healing is highly complex,involving multiple regulatory pathways such as cytokines,hormones,the nervous system,and stem cells.However,the precise mechanisms are still not fully understood and require further investigation.Current research suggests that traumatic brain injury accelerates bone callus formation and bone tissue regeneration by promoting the release of cytokines(e.g.,insulin-like growth factor-1)and hormones(e.g.,growth hormone and leptin),regulating the nervous system,and promoting stem cell proliferation and differentiation.Additionally,traumatic brain injury triggers a series of immune responses,including the release of inflammatory factors and activation of immune cells,which modulate fracture healing.These responses improve local blood flow,cell migration,and fibroblast activation,supporting various stages of bone healing.Stem cell activation induced by traumatic brain injury is also crucial,as activated stem cells differentiate into osteoblasts,chondrocytes,and adipocytes,facilitating bone tissue regeneration and repair.Therefore,traumatic brain injury-induced immune responses and stem cell activation work together to accelerate fracture healing,providing essential support for the process.These mechanisms significantly shorten the healing time and improve patient outcomes.In conclusion,traumatic brain injury promotes callus formation and fracture healing through multiple mechanisms,highlighting its importance in bone repair.Future research should focus on the signaling pathways and regulatory factors influenced by traumatic brain injury to further understand its mechanisms.These findings will provide a foundation for developing targeted therapies,stem cell treatments,and neural regulation therapies,with potential clinical value in shortening healing time,optimizing recovery protocols,and improving prognosis.Exploring traumatic brain injury-induced biological effects will open new avenues for fracture treatment.

Objective:To screen the risk factors of acute kidney injury (AKI) in patients with multiple trauma, construct a prediction model accordingly, and evaluate its predictive value.Methods:A retrospective cohort study was performed to analyze the clinical data of 560 multiple trauma patients who were admitted to while Affiliated Hospital of Jiangsu University from January 2017 to June 2023, including 424 males and 136 females, aged 18-91 years [(55.5±15.0)years]. The patients were randomly divided into a training set ( n=392) and validation set ( n=168) with a ratio of 7∶3. Of all, 77 patients were combined with AKI in the training set, while 33 patients combined with AKI in the validation set. The AKI group and non-AKI group in the training set were compared in terms of gender, age, hypertension, diabetes, cause of injury, abbreviated injury scale (AIS) score of head and neck injury, AIS score of maxillofacial injury, AIS score of chest injury, AIS score of abdominal injury, AIS score of extremities and pelvic injury, AIS score of body surface injury, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, Glasgow coma score (GCS) on admission, revised trauma score (RTS) on admission, acute physiology and chronic health assessment II (APACHE II) on admission, injury severity score (ISS) on admission, and laboratory test results on admission including white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin (PT), fibrinogen (FIB), thrombin time (TT), international normalized ratio (INR), D-dimer, blood lactate, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, globulin, urea nitrogen, serum creatinine, blood glucose, potassium, sodium and chloronium. In the training set, univariate analysis and Lasso regression analysis were used to screen the risk factors of AKI in patients with multiple trauma, which were then included into multivariate logistic regression analysis to identify the independent risk factors. A nomogram prediction model was constructed using the R software based on the above independent risk factors. Hosmer-Lemeshow (H-L) goodness-of-fit test was performed to evaluate the fitting degree of the prediction model in the training set and the validation set, and the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve (DCA) were plotted in the training set and the validation set to evaluate the predictive performance of the prediction model. Results:There were statistically significant differences in AIS score of abdominal injury, heart rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, GCS on admission, RTS on admission, APACHE II on admission, ISS on admission as well as hemoglobin, platelet count, APTT, PT, FIB, TT, INR, blood lactate, base excess, AST, albumin, globulin, urea nitrogen, serum creatinine, blood glucose and sodium on admission between the AKI group and the non-AKI group ( P<0.05 or 0.01). The characteristic variables screened by Lasso regression analysis included AIS score of abdominal injury, red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drugs therapy, blood lactate on admission, blood creatinine on admission, AST on admission, and blood sodium on admission. Multivariate logistic regression analysis showed that red blood cell transfusion volume within 24 hour following admission ( OR=1.09, 95% CI 1.01, 1.18), mechanical ventilation ( OR=2.49, 95% CI 1.06, 5.85), vasoactive drug therapy ( OR=2.04, 95% CI 1.03, 4.03), blood lactate on admission ( OR=1.10, 95% CI 1.01, 1.21) and serum creatinine on admission ( OR=1.02, 95% CI 1.01, 1.03) were independent risk factors for AKI in patients with multiple trauma ( P<0.05). The regression equation was constructed: Logit[ P/(1- P)]=0.086 2×"red blood cell transfusion volume within 24 hour following admission"+0.912 7×"mechanical ventilation"+0.713 2×"vasoactive drug therapy"+0.098 9×"blood lactate on admission"+0.019 2×"serum creatinine on admission" -4.822 3. H-L goodness-of-fit test showed χ2 value of 9.50 in the training set ( P>0.05) and 6.43 in the validation set ( P>0.05). The results of the ROC curve indicated that the area under the curve (AUC) was 0.84 (95% CI 0.78, 0.89) in the training set and 0.80 (95% CI 0.72, 0.88) in the validation set. The calibration curves showed good agreement with the actual curves, with the predicted probability consistent with the actual probability in both training set and validation set. DCA analysis showed that the threshold probability ranged from 2% to 70% with the net benefit rate of the prediction model greater than 0 in the training set, while the threshold probability ranged from 3% to 69% with the net benefit rate of the prediction model greater than 0 in the validation set. Conclusions:Red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drug therapy, lactate and serum creatinine on admission are independent risk factors for AKI in patients with multiple trauma. The nomogram prediction model based on the above 5 predictive variables of AKI in patients with multiple trauma shows good predictive efficacy and clinical application value.

BACKGROUND:Delayed healing and nonunion of fractures are common clinical issues.Clinical observations have shown that patients with limb fractures combined with traumatic brain injury experience significantly faster fracture healing compared with those without brain injury.The potential mechanisms behind this phenomenon have become a crucial focus of current research.Recent studies indicate that traumatic brain injury significantly accelerates callus formation and fracture healing processes by regulating cytokines,hormones,neural signals,and stem cell mechanisms.OBJECTIVE:To summarize the latest research progress in the mechanisms by which traumatic brain injury promotes callus formation and fracture healing,thereby providing a theoretical basis for clinical applications.METHODS:The first author conducted a search of CNKI,WanFang,VIP,PubMed,Embase,Web of Science,and Cochrane Library databases for literature published from January 2013 to October 2024,with some references traced back up to 20 years.The search terms used were"traumatic brain injury,callus,fracture healing,inflammatory response,cytokines,hormones,neuropeptides,genes,stem cells"in Chinese and English.A total of 83 articles meeting the inclusion criteria were ultimately selected.RESULTS AND CONCLUSION:The mechanism by which traumatic brain injury promotes callus formation and fracture healing is highly complex,involving multiple regulatory pathways such as cytokines,hormones,the nervous system,and stem cells.However,the precise mechanisms are still not fully understood and require further investigation.Current research suggests that traumatic brain injury accelerates bone callus formation and bone tissue regeneration by promoting the release of cytokines(e.g.,insulin-like growth factor-1)and hormones(e.g.,growth hormone and leptin),regulating the nervous system,and promoting stem cell proliferation and differentiation.Additionally,traumatic brain injury triggers a series of immune responses,including the release of inflammatory factors and activation of immune cells,which modulate fracture healing.These responses improve local blood flow,cell migration,and fibroblast activation,supporting various stages of bone healing.Stem cell activation induced by traumatic brain injury is also crucial,as activated stem cells differentiate into osteoblasts,chondrocytes,and adipocytes,facilitating bone tissue regeneration and repair.Therefore,traumatic brain injury-induced immune responses and stem cell activation work together to accelerate fracture healing,providing essential support for the process.These mechanisms significantly shorten the healing time and improve patient outcomes.In conclusion,traumatic brain injury promotes callus formation and fracture healing through multiple mechanisms,highlighting its importance in bone repair.Future research should focus on the signaling pathways and regulatory factors influenced by traumatic brain injury to further understand its mechanisms.These findings will provide a foundation for developing targeted therapies,stem cell treatments,and neural regulation therapies,with potential clinical value in shortening healing time,optimizing recovery protocols,and improving prognosis.Exploring traumatic brain injury-induced biological effects will open new avenues for fracture treatment.

Objective:To explore the latent trajectory class of perioperative pain catastrophizing in patients undergoing lumbar internal fixation surgery and analyze the influencing factors of these latent classes.Methods:A total of 180 patients who underwent lumbar internal fixation surgery at Xinxiang Central Hospital from January to December 2023 were selected using convenience sampling. The Pain Catastrophizing Scale (PCS) was used to assess pain catastrophizing at 1 day preoperatively and on postoperative days 3, 7, and 14. Latent Class Growth Modeling (LCGM) was employed to identify trajectory classes of pain catastrophizing, and Logistic regression analysis was used to examine their influencing factors.Results:A total of 177 patients completed the follow-up, with a follow-up rate of 98.33% (177/180) . The overall perioperative PCS score was (30.39±10.86) . PCS scores at 1 day preoperatively and on postoperative days 3, 7, and 14 were (37.63±6.23) , (33.27±6.00) , (28.55±9.02) , and (23.81±9.33) , respectively. The proportions of patients with PCS≥38 at the four times were 42.94% (76/177) , 23.16% (41/177) , 22.60% (40/177) , and 12.43% (22/177) , respectively. LCGM identified three latent trajectory classes of perioperative pain catastrophizing: "high-level declining group" (55.93%, 99/177) , "high-level fluctuating group" (28.81%, 51/177) , and "persistent high-level group" (15.25%, 27/177) . Logistic regression analysis showed that payment method, surgical duration, and preoperative PCS score were significant influencing factors of pain catastrophizing trajectory classes ( P<0.05) . Conclusions:Pain catastrophizing levels in patients undergoing lumbar internal fixation surgery peaked preoperatively. While most patients showed a declining trend postoperatively, a subset exhibited fluctuating or persistently high levels. Payment method, surgical duration, and preoperative pain catastrophizing levels significantly influenced the trajectory of pain catastrophizing, warranting attention from clinical nursing staff.

To establish a rapid visual detection method for Akabane virus(AKAV)on site,specific primers and probes based on the S fragment of AKAV were designed in this experiment.Corre-sponding groups were added to the primers or probes to fulfil the requirement of the combination of recombinase polymerase amplification(RPA)with lateral flow dipstick(LFD).The reaction temperature and time,concentrations of the primer and probe were optimized to establish the RPA-LFD method for detecting AKAV.After that,the specificity,sensitivity and clinical reliability of the method were evaluated.The results showed that after 20 minutes of reaction at 37 ℃,the test results could be read on LFD paper.There was no cross reaction against blue tongue virus,Pasteurella multocida,bovine infectious rhinotracheitis virus and bovine Mycoplasma bovis,and the detection limit was 2.5 × 100 copies/μL of standard plasmid.Detection of clinical samples showed a consistent results with that by RT-PCR method.These findings indicated that the RPA-LFD method established had the advantages of good specificity,high sensitivity,simple operation and visualization,and could be applied to clinical detection,which provides new technical support for the rapid diagnosis and prevention and control of AKAV.

To establish a rapid visual detection method for Akabane virus(AKAV)on site,specific primers and probes based on the S fragment of AKAV were designed in this experiment.Corre-sponding groups were added to the primers or probes to fulfil the requirement of the combination of recombinase polymerase amplification(RPA)with lateral flow dipstick(LFD).The reaction temperature and time,concentrations of the primer and probe were optimized to establish the RPA-LFD method for detecting AKAV.After that,the specificity,sensitivity and clinical reliability of the method were evaluated.The results showed that after 20 minutes of reaction at 37 ℃,the test results could be read on LFD paper.There was no cross reaction against blue tongue virus,Pasteurella multocida,bovine infectious rhinotracheitis virus and bovine Mycoplasma bovis,and the detection limit was 2.5 × 100 copies/μL of standard plasmid.Detection of clinical samples showed a consistent results with that by RT-PCR method.These findings indicated that the RPA-LFD method established had the advantages of good specificity,high sensitivity,simple operation and visualization,and could be applied to clinical detection,which provides new technical support for the rapid diagnosis and prevention and control of AKAV.

Objective:To explore the latent trajectory class of perioperative pain catastrophizing in patients undergoing lumbar internal fixation surgery and analyze the influencing factors of these latent classes.Methods:A total of 180 patients who underwent lumbar internal fixation surgery at Xinxiang Central Hospital from January to December 2023 were selected using convenience sampling. The Pain Catastrophizing Scale (PCS) was used to assess pain catastrophizing at 1 day preoperatively and on postoperative days 3, 7, and 14. Latent Class Growth Modeling (LCGM) was employed to identify trajectory classes of pain catastrophizing, and Logistic regression analysis was used to examine their influencing factors.Results:A total of 177 patients completed the follow-up, with a follow-up rate of 98.33% (177/180) . The overall perioperative PCS score was (30.39±10.86) . PCS scores at 1 day preoperatively and on postoperative days 3, 7, and 14 were (37.63±6.23) , (33.27±6.00) , (28.55±9.02) , and (23.81±9.33) , respectively. The proportions of patients with PCS≥38 at the four times were 42.94% (76/177) , 23.16% (41/177) , 22.60% (40/177) , and 12.43% (22/177) , respectively. LCGM identified three latent trajectory classes of perioperative pain catastrophizing: "high-level declining group" (55.93%, 99/177) , "high-level fluctuating group" (28.81%, 51/177) , and "persistent high-level group" (15.25%, 27/177) . Logistic regression analysis showed that payment method, surgical duration, and preoperative PCS score were significant influencing factors of pain catastrophizing trajectory classes ( P<0.05) . Conclusions:Pain catastrophizing levels in patients undergoing lumbar internal fixation surgery peaked preoperatively. While most patients showed a declining trend postoperatively, a subset exhibited fluctuating or persistently high levels. Payment method, surgical duration, and preoperative pain catastrophizing levels significantly influenced the trajectory of pain catastrophizing, warranting attention from clinical nursing staff.

Objective:To screen the risk factors of acute kidney injury (AKI) in patients with multiple trauma, construct a prediction model accordingly, and evaluate its predictive value.Methods:A retrospective cohort study was performed to analyze the clinical data of 560 multiple trauma patients who were admitted to while Affiliated Hospital of Jiangsu University from January 2017 to June 2023, including 424 males and 136 females, aged 18-91 years [(55.5±15.0)years]. The patients were randomly divided into a training set ( n=392) and validation set ( n=168) with a ratio of 7∶3. Of all, 77 patients were combined with AKI in the training set, while 33 patients combined with AKI in the validation set. The AKI group and non-AKI group in the training set were compared in terms of gender, age, hypertension, diabetes, cause of injury, abbreviated injury scale (AIS) score of head and neck injury, AIS score of maxillofacial injury, AIS score of chest injury, AIS score of abdominal injury, AIS score of extremities and pelvic injury, AIS score of body surface injury, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, Glasgow coma score (GCS) on admission, revised trauma score (RTS) on admission, acute physiology and chronic health assessment II (APACHE II) on admission, injury severity score (ISS) on admission, and laboratory test results on admission including white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin (PT), fibrinogen (FIB), thrombin time (TT), international normalized ratio (INR), D-dimer, blood lactate, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, globulin, urea nitrogen, serum creatinine, blood glucose, potassium, sodium and chloronium. In the training set, univariate analysis and Lasso regression analysis were used to screen the risk factors of AKI in patients with multiple trauma, which were then included into multivariate logistic regression analysis to identify the independent risk factors. A nomogram prediction model was constructed using the R software based on the above independent risk factors. Hosmer-Lemeshow (H-L) goodness-of-fit test was performed to evaluate the fitting degree of the prediction model in the training set and the validation set, and the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve (DCA) were plotted in the training set and the validation set to evaluate the predictive performance of the prediction model. Results:There were statistically significant differences in AIS score of abdominal injury, heart rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, GCS on admission, RTS on admission, APACHE II on admission, ISS on admission as well as hemoglobin, platelet count, APTT, PT, FIB, TT, INR, blood lactate, base excess, AST, albumin, globulin, urea nitrogen, serum creatinine, blood glucose and sodium on admission between the AKI group and the non-AKI group ( P<0.05 or 0.01). The characteristic variables screened by Lasso regression analysis included AIS score of abdominal injury, red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drugs therapy, blood lactate on admission, blood creatinine on admission, AST on admission, and blood sodium on admission. Multivariate logistic regression analysis showed that red blood cell transfusion volume within 24 hour following admission ( OR=1.09, 95% CI 1.01, 1.18), mechanical ventilation ( OR=2.49, 95% CI 1.06, 5.85), vasoactive drug therapy ( OR=2.04, 95% CI 1.03, 4.03), blood lactate on admission ( OR=1.10, 95% CI 1.01, 1.21) and serum creatinine on admission ( OR=1.02, 95% CI 1.01, 1.03) were independent risk factors for AKI in patients with multiple trauma ( P<0.05). The regression equation was constructed: Logit[ P/(1- P)]=0.086 2×"red blood cell transfusion volume within 24 hour following admission"+0.912 7×"mechanical ventilation"+0.713 2×"vasoactive drug therapy"+0.098 9×"blood lactate on admission"+0.019 2×"serum creatinine on admission" -4.822 3. H-L goodness-of-fit test showed χ2 value of 9.50 in the training set ( P>0.05) and 6.43 in the validation set ( P>0.05). The results of the ROC curve indicated that the area under the curve (AUC) was 0.84 (95% CI 0.78, 0.89) in the training set and 0.80 (95% CI 0.72, 0.88) in the validation set. The calibration curves showed good agreement with the actual curves, with the predicted probability consistent with the actual probability in both training set and validation set. DCA analysis showed that the threshold probability ranged from 2% to 70% with the net benefit rate of the prediction model greater than 0 in the training set, while the threshold probability ranged from 3% to 69% with the net benefit rate of the prediction model greater than 0 in the validation set. Conclusions:Red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drug therapy, lactate and serum creatinine on admission are independent risk factors for AKI in patients with multiple trauma. The nomogram prediction model based on the above 5 predictive variables of AKI in patients with multiple trauma shows good predictive efficacy and clinical application value.

Objective:To reveal the molecular characteristics of mononuclear phagocytes (MPs) in rat model of peripheral nerve injury (PNI) using single-cell RNA sequencing (scRNA-seq) technology that would provide the developmental changes and major biological process involved in the function of MPs after PNI.Methods:Twenty-seven male SD rats (200-300 g in weight) were selected from the Department of Hand and Foot Microscopy and Wound Repair Surgery, Henan Provincial People's Hospital (People's Hospital of Zhengzhou University) and the Department of Orthopaedics of First Affiliated Hospital of Zhengzhou University from July 2023 to December 2023. The rats were divided into a Sham operation group (Sham group), a 3 days post crush group (3 dpc group) and a 7 days post crush group (7 dpc group), following the randomised table method with 9 rats per group. After 7 days of environmental acclimatisation, the 3 dpc group and 7 dpc group were subjected to have the right sciatic nerve crushed in order to create a model of crush injury. And as a control group, the Sham group was subjected to Sham surgery only. Nine right sciatic nerves of rats were collected from each group at the corresponding time pints. Single-cell isolation was performed on the 10X Genomics platform. ScRNA-seq libraries were constructed using the Gel Bead Kit V3 and the libraries were sequenced using an Illumina Novaseq 6000 sequencer. Dimensionality reduction was performed using Principal Component Analysis and T-Distributed Stochastic Neighbor Embedding to visualise and explore the cellular heterogeneity within the dataset. Nine distinct cell clusters and their corresponding marker genes were identified based on the dimensionality-reduced data. Differential gene expression analysis was then performed to identify differentially expressed genes (DEGs) in MPs between different groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to uncover the biological processes and pathways based on the DEGs. Monocle program for pseudo-time analysis was used to infer the developmental trajectory of MPs after injury.Results:A total of 19 054 cells were obtained by sequencing, and the results showed that the proportion of MPs in peripheral nerves was significantly up-regulated after PNI, and MPs were classified into 9 cellular subgroups based on the clustering analysis of the scRNA-seq data, which were Cluster 1 (3 398 cells), Cluster 2 (3 388 cells), Cluster 3 (3 262 cells), Cluster 4 (2 825 cells), Cluster 5 (2 753 cells), Cluster 6 (1 894 cells), Cluster 7 (648 cells), Cluster 8 (492 cells) and Cluster 9 (394 cells), respectively. Based on the expression of different cell subpopulation markers, MPs in the Sham group, 3 dpc group and 7 dpc group of sciatic nerves were classified into 9 cell clusters and the distributions of different MPs clusters in the 9 sciatic nerve samples were identified, among which, the Sham group had the lowest number of MPs cells in the sciatic nerve samples (a total of 2 719 cells) and the clusters were mainly dominated by clusters 5 (1 119 cells) and clusters 6 (1 240 cells). The 3 dpc group had the highest number of MPs cells (9 760 cells in total) and the clusters were mainly dominated by cluster 2 (1 760 cells), cluster 3 (3 130 cells) and cluster 4 (2 300 cells). The MPs (6 575 cells in total) in the 7 dpc group were mainly dominated by cluster 1 (2 406 cells) and cluster 2 (1 628 cells). Compared with the Sham group, the GO and KEGG annotations of the DEGs were significantly upregulated in the 3 dpc group, indicating that MPs in the rat sciatic nerves would have the ability to bind to extracellular molecules and remove debris from the injury site at 3 days post-injury, and the 7 dpc group would have the ability to activate the signalling pathways related to nerve repair. The proposed time-series analysis revealed that, in the uninjured condition, the MPs were mainly in the cluster 5 (Ccl17 +Cd80 +) and cluster 6 (Fcmr +Slc9a9 +). At 3 days post-injury, MPs developed into cell types dominated by cluster 2 (Cd8b +Meis3 +), cluster 3 (Il10 +Cd163 +) and cluster 4 (Ccl24 +Prg4 +). At 7 days post-injury, the effector state of cluster 2 among the main cell types of MPs was still maintained but the other parts had developed into cluster 1 (Hspa1b +Apobec1 +) related phenotypes. Conclusion:The molecular characteristics of MPs in the peripheral nerve revealed through scRNA-seq data provide valuable insights into the role of MPs in mediating inflammation and neural regeneration after PNI.

Objective:To explore the independent risk factor for in-hospital mortality of patients with multiple trauma, and to construct a prediction model of risk of death and validate its efficacy.Methods:A retrospective cohort study was performed to analyze the clinical data of 1 028 patients with multiple trauma admitted to Affiliated Hospital of Jiangsu University from January 2011 to December 2021. There were 765 males and 263 females, aged 18-91 years[(53.8±12.4)years]. The injury severity score (ISS) was 16-57 points [(26.3±7.6)points]. There were 153 deaths and 875 survivals. A total of 777 patients were enrolled as the training set from January 2011 to December 2018 for building the prediction model, while another 251 patients were enrolled as validation set from January 2019 to December 2021. According to the outcomes, the training set was divided into the non-survival group (115 patients) and survival group (662 patients). The two groups were compared in terms of the gender, age, underlying disease, injury mechanism, head and neck injury, maxillofacial injury, chest injury, abdominal injury, extremity and pelvis injury, body surface injury, damage control surgery, pre-hospital time, number of injury sites, Glasgow coma score (GCS), ISS, shock index, and laboratory test results within 6 hours on admission, including blood lactate acid, white blood cell counts, neutrophil to lymphocyte ratio (NLR), platelet counts, hemoglobin, activated partial thromboplastin time (APTT), fibrinogen, D-dimer and blood glucose. Univariate analysis and multivariate Logistic regression analysis were performed to determine the independent risk factors for in-hospital mortality in patients with multiple trauma. The R software was used to establish a nomogram prediction model based on the above risk factors. Area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and clinical decision curve analysis (DCA) were plotted in the training set and the validation set, and Hosmer-Lemeshow goodness-of-fit test was performed.Results:Univariate analysis showed that abdominal injury, extremity and pelvis injury, damage control surgery, GCS, ISS, shock index, blood lactic acid, white blood cell counts, NLR, platelet counts, hemoglobin, APTT, fibrinogen, D-dimer and blood glucose were correlated with in-hospital mortality in patients with multiple trauma ( P<0.05 or 0.01). Logistic regression analysis showed that GCS≤8 points ( OR=1.99, 95% CI 1.12,3.53), ISS>25 points ( OR=7.39, 95% CI 3.50, 15.61), shock index>1.0 ( OR=3.43, 95% CI 1.94,6.08), blood lactic acid>2 mmol/L ( OR=9.84, 95% CI 4.97, 19.51), fibrinogen≤1.5 g/L ( OR=2.57, 95% CI 1.39,4.74) and blood glucose>10 mmol/L ( OR=3.49, 95% CI 2.03, 5.99) were significantly correlated with their in-hospital mortality ( P<0.05 or 0.01). The ROC of the nomogram prediction model indicated that AUC of the training set was 0.91 (95% CI 0.87, 0.93) and AUC of the validation set was 0.90 (95% CI 0.84, 0.95). The calibration curve showed that the predicted probability was consistent with the actual situation in both the training set and validation set. DCA showed that the nomogram prediction model presented excellent performance in predicting in-hospital mortality. In Hosmer-Lemeshow goodness-of-fit test, χ2 value of the training set was 9.69 ( P>0.05), with validation set of 9.16 ( P>0.05). Conclusions:GCS≤8 points, ISS>25 points, shock index>1.0, blood lactic acid>2 mmol/L, fibrinogen≤1.5 g/L and blood glucose>10 mmol/L are independent risk factors for in-hospital mortality in patients with multiple trauma. The nomogram prediction model based on these 6 predictive variables shows a good predictive performance, which can help clinicians comprehensively assess the patient′s condition and identify the high-risk population.