BACKGROUND:Neuroinflammation is a major cause of exacerbation after spinal cord injury.In recent years,pyroptosis has received much attention due to its remarkable pro-inflammatory features.Some of these natural products can significantly inhibit the inflammatory response and improve the damaged nerve function by regulating the level of pyroptosis after spinal cord injury,which provides a new therapeutic idea for spinal cord injury.OBJECTIVE:To summarize the mechanism of action of natural products in regulating pyroptosis for the treatment of spinal cord injury,with a view to providing lessons and references for future research on the treatment of spinal cord injury.METHODS:The search terms"spinal cord injury,pyroptosis,inflammasome,natural products,natural compounds,traditional Chinese medicine"in Chinese and English were used to search for relevant literature since the establishment of the database up to September 2024 in the databases of PubMed,Web of Science,WanFang,and CNKI.According to the inclusion and exclusion criteria,75 relevant articles were finally obtained.RESULTS AND CONCLUSION:(1)Pyroptosis is an important pro-inflammatory pathway in spinal cord injury,and controlling pyroptosis is an effective way to improve damaged nerve function.(2)Some natural products can regulate pyroptosis via the NLRP3/Caspase-1 classical pyroptosis pathway,the NF-κB-related pathway,other upstream pathways such as Nrf2/HO-1,and autophagy,thereby affecting the level of tissue inflammation and accelerating neurological recovery after spinal cord injury.(3)The anti-pyroptosis effects of these natural products are mostly dependent on the NLRP3 classical pyroptosis pathway,and there is a lack of studies on other pyroptosis pathways.(4)There are still many problems in this field,such as the fact that these natural products are not currently supported by evidence from appropriate clinical studies.(5)The natural product has great potential in regulating pyroptosis and is expected to be a powerful weapon in the treatment of spinal cord injury.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Liver transplantation (LT) has become a standard treatment for end-stage liver diseases, and graft injury is intricately associated with poor prognosis. Granzyme B (GZMB) plays a vital role in natural killer (NK) cell biology, but whether NK-derived GZMB affects graft injury remains elusive. Through the analysis of single-cell RNA-sequencing data obtained from human LT grafts and the isolation of lymphocytes from mouse livers following ischemia-reperfusion injury (IRI), we demonstrated that 2NK cells with high expression of GZMB are enriched in patients and mice. Both systemically and liver-targeted depletion of NK cells led to a notable reduction in GZMB⁺ cell infiltration, subsequently resulting in diminished graft injury. Notably, the reconstitution of Il2rg ^-/- Rag2 ^-/- mice with purified Gzmb-KO NK cells demonstrated superior outcomes compared to those with wild-type NK cells. Crucially, global knockout of GZMB and pharmacological inhibition exhibited remarkable improvements in liver function in both mouse IRI and rat LT models. Moreover, a phosphorylated derivative of FDA-approved vidarabine was identified as an effective inhibitor of mouse GZMB activity by molecular dynamics, which could provide a potential avenue for therapeutic intervention. Therefore, targeting NK cell-derived GZMB during the LT process suggests potential therapeutic strategies to improve post-transplant outcomes.

Hand degloving injury represents one of the most severe forms of hand trauma, characterised by challenging treatment and a complex prognostic outcome. It is crucial to effectively utilise the degloved tissues in emergency or primary repair of a hand degloving injury. This consensus provides a comprehensive review of the existing literature on definition, classification, emergency assessment, debridement, judgment of skin viability, in situ repair of the degloved skin, and adjunctive treatment for degloving injury of hand. Based on conclusion of both domestic and international experiences, this expert consensus on the classification of hand degloving injury and the emergency repair with the avulsed skin is established, aiming to provide a guidance to surgeons on standardised treatment strategy and improve the management of hand degloving injury.

BACKGROUND:Neuroinflammation is a major cause of exacerbation after spinal cord injury.In recent years,pyroptosis has received much attention due to its remarkable pro-inflammatory features.Some of these natural products can significantly inhibit the inflammatory response and improve the damaged nerve function by regulating the level of pyroptosis after spinal cord injury,which provides a new therapeutic idea for spinal cord injury.OBJECTIVE:To summarize the mechanism of action of natural products in regulating pyroptosis for the treatment of spinal cord injury,with a view to providing lessons and references for future research on the treatment of spinal cord injury.METHODS:The search terms"spinal cord injury,pyroptosis,inflammasome,natural products,natural compounds,traditional Chinese medicine"in Chinese and English were used to search for relevant literature since the establishment of the database up to September 2024 in the databases of PubMed,Web of Science,WanFang,and CNKI.According to the inclusion and exclusion criteria,75 relevant articles were finally obtained.RESULTS AND CONCLUSION:(1)Pyroptosis is an important pro-inflammatory pathway in spinal cord injury,and controlling pyroptosis is an effective way to improve damaged nerve function.(2)Some natural products can regulate pyroptosis via the NLRP3/Caspase-1 classical pyroptosis pathway,the NF-κB-related pathway,other upstream pathways such as Nrf2/HO-1,and autophagy,thereby affecting the level of tissue inflammation and accelerating neurological recovery after spinal cord injury.(3)The anti-pyroptosis effects of these natural products are mostly dependent on the NLRP3 classical pyroptosis pathway,and there is a lack of studies on other pyroptosis pathways.(4)There are still many problems in this field,such as the fact that these natural products are not currently supported by evidence from appropriate clinical studies.(5)The natural product has great potential in regulating pyroptosis and is expected to be a powerful weapon in the treatment of spinal cord injury.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Hand degloving injury represents one of the most severe forms of hand trauma, characterised by challenging treatment and a complex prognostic outcome. It is crucial to effectively utilise the degloved tissues in emergency or primary repair of a hand degloving injury. This consensus provides a comprehensive review of the existing literature on definition, classification, emergency assessment, debridement, judgment of skin viability, in situ repair of the degloved skin, and adjunctive treatment for degloving injury of hand. Based on conclusion of both domestic and international experiences, this expert consensus on the classification of hand degloving injury and the emergency repair with the avulsed skin is established, aiming to provide a guidance to surgeons on standardised treatment strategy and improve the management of hand degloving injury.

To address the global shortage of donor organs, marginal donated livers are increasingly used in liver transplants, albeit with associated risks such as early graft dysfunction and primary non-function. Improving the quality of marginal donated livers is one of the focal areas in transplantation research. Machine perfusion is pivotal in this effort, reducing ischemia-reperfusion injuries and enhancing liver quality. Recently, advances in cell and gene therapy, combined with optimized machine perfusion strategies, have enabled precise interventions for specific organs, showing great potential in improving marginal donated liver quality and increasing recipient survival rates. These innovations have the potential to drive advancements in machine perfusion and organ repair and regeneration technologies.

Objective:To explore the role of ductular reaction in assessing the efficacy of liver transplantation.Method:From January 2015 to December 2020, he relevant clinical data were retrospectively reviewed for 100 recipients and their corresponding donors at Shulan (Hangzhou) Hospital. They were assigned into two groups of hepatic steatosis (HS group, 65 cases) and non-hepatic steatosis (non-HS group, 35 cases) according to whether or not receiving steatosis donated liver. Furthermore, based upon the occurrence of early allograft dysfunction (EAD), the participants were categorized into two groups of EAD (33 cases) and non-EAD (67 cases). The degree of bile duct reaction ductular reaction was defined by the percentage of staining area occupied by cytokeratin 19 (CK19) -positive bile duct cells in immunohistochemical-stained specimens. Donor of ductular reaction were compared between HS/non-HS and EAD/non-EAD groups. The risk factors for EAD were identified by univariate and multivariate Logistic regression analysis. Subgroup analysis was conducted based upon the level of ductular reaction (DR number) in donors (DR=0.4 as a threshold) and whether or not donors exhibited steatosis. The impact of DR was examined on the incidence of EAD and survival post-liver transplantation in steatosis donors.Result:The level of DR was higher in steatosis donor than that in non-steatosis donor [ (0.59%±0.385%) vs. (0.32%±0.194%), P<0.01]. And it was higher in EAD group than that in non-EAD group [ (0.72%±0.449%) vs. (0.38%±0.226%), P<0.01]. Multivariate logistic regression analysis showed that a high level of ductular reaction was an independent risk factor for EAD post-liver transplantation in donor. Subgroup analysis revealed that receiving a steatosis donor with low ductular reaction (DR<0.4%) had comparable levels of EAD occurrence and overall survival rate to receiving a non-steatosis donor. Conclusion:Steatosis with low ductular reaction donor may be safely applied for liver transplantation. And assessing donor injury based upon ductular reaction can effectively expand the clinical application of steatosis donors.

OBJECTIVE: To explore the feasibility and effectiveness of a foldable pedicled latissimus dorsi myocutaneous flap to repair soft tissue defects in the shoulder and back. METHODS: Between August 2018 and January 2023, the foldable pedicled latissimus dorsi myocutaneous flaps were used to repair soft tissue defects in the shoulder and back of 8 patients. There were 5 males and 3 females with the age ranged from 21 to 56 years (mean, 35.4 years). Wounds were located in the shoulder in 2 cases and in the shoulder and back in 6 cases. The causes of injury were chronic infection of skin and bone exposure in 2 cases, secondary wound after extensive resection of skin and soft tissue tumor in 4 cases, and wound formation caused by traffic accident in 2 cases. Skin defect areas ranged from 14 cm×13 cm to 20 cm×16 cm. The disease duration ranged from 12 days to 1 year (median, 6.6 months). A pedicled latissimus dorsi myocutaneous flap was designed and harvested. The flap was divided into A/B flap and then were folded to repair the wound, with the donor area of the flap being pulled and sutured in one stage. RESULTS: All 7 flaps survived, with primary wound healing. One patient suffered from distal flap necrosis and delayed healing was achieved after dressing change. The incisions of all donor sites healed by first intention. All patients were followed up 6 months to 4 years (mean, 24.7 months). The skin flap has a good appearance with no swelling in the pedicle. At last follow-up, 6 patients had no significant difference in bilateral shoulder joint motion, and 2 patients had a slight decrease in abduction range of motion compared with the healthy side. The patients' daily life were not affected, and linear scar was left in the donor site. CONCLUSION The foldable pedicled latissimus dorsi myocutaneous flap is an ideal method to repair the soft tissue defect of shoulder and back with simple operation, less damage to the donor site, and quick recovery after operation.
Male ; Female ; Humans ; Young Adult ; Adult ; Middle Aged ; Plastic Surgery Procedures ; Myocutaneous Flap/surgery* ; Shoulder/surgery* ; Skin Transplantation ; Superficial Back Muscles/transplantation* ; Soft Tissue Injuries/surgery* ; Wound Healing ; Treatment Outcome ; Perforator Flap