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.

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.

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.

Background and aims:Inflammatory factors play significant roles in the development and occurrence of hepatocellular carcinoma(HCC).However,the tumor-protective functions of growth differentiation factors(GDFs)in HCC are yet to be clarified.In this study,we aimed to evaluate the expression levels of 10 GDFs in tumor and paratumor tissues from patients with HCC and perform in vitro and in vivo ex-periments to elucidate the role of GDF7 in regulating the proliferation and metastasis of HCC.Methods:The gene expression of 10 GDFs was compared between HCC and paratumors using The Cancer Genome Atlas dataset and patient-derived tissues.A tumor microarray containing 108 HCC tissue samples was used to explore the prognostic value of GDF7 expression.Loss-of-function experiments were also performed in vitro and in vivo to investigate the role of GDF7 in HCC.Results:The mRNA and protein levels of GDF7 were significantly lower in HCC tumors than in para-tumors(P＜0.001).Kaplan-Meier analysis showed that decreased GDF7 expression in HCC was asso-ciated with worse overall survival(5-year rate:61.8％vs.27.5％,P＜0.001)and increased recurrence risk(P＜0.001).Multivariate Cox regression analysis demonstrated that low GDF7 expression,the presence of microvascular invasion,and elevated alpha-fetoprotein(AFP)levels were independent risk factors for tumor recurrence and poor survival.Downregulation of GDF7 also increased the tumor growth in HCC cells and in an HCC xenograft model.GDF7 knockdown promoted migration and invasion via epithelial-mesenchymal transition.Meanwhile,a negative correlation between JunB proto-oncogene(JUNB)and GDF7 was observed in HCC tissues.Modulating JUNB levels altered GDF7 protein expression.Conclusions:GDF7 is a potential biomarker for predicting superior outcomes in patients with HCC.GDF7 amplification is a potential therapeutic option for HCC.

Objective:To explore the clinical effects of nerve-carrying peroneal artery perforator flaps in repairing nerve defects in the late stage of wrist electric burns.Methods:This study was a retrospective observational study. From December 2019 to May 2023, five patients with sensory dysfunction in hands due to nerve defects in the late stage of wrist electric burns were treated in the Affiliated Hospital of Zunyi Medical University and met the inclusion criteria. There were 4 males and 1 female, aged 7 to 48 years. Four patients had defects in both median nerve and ulnar nerve, one patient had a defect solely in median nerve, and the length of nerve defects ranged from 5 to 12 cm. Four patients underwent transplantation of peroneal artery perforator flaps carrying sural nerve and superficial peroneal nerve, and 1 patient underwent transplantation of peroneal artery perforator flap only carrying sural nerve. The wounds in flap donor sites were all directly sutured. One patient had tendon adhesion and release of tendon adhesion was performed during the same surgery; 3 patients had combined defects in the wrist flexor muscle group, including 2 patients received autologous tendon grafting during the same surgery, and one patient received reconstruction of finger flexion function with a gracilis myocutaneous flap in the second stage; 1 patient had combined wrist flexion contracture which was surgically released in the second stage. During follow-up after surgery, the survival of the flaps was observed, and the healing time of the incisions or sutures in flap donor and recipient sites and the recovery time of hand sensation were recorded. At the last follow-up, the scar formation and loss of sensation in the foot were observed, and flexor strength and sensory function of the fingers were evaluated based on the evaluation criteria for tendon and nerve repair standards of hands in the trial standards for evaluation of partial function of the upper extremity by the Hand Surgery Society of Chinese Medical Association.Results:All patients were followed up after surgery for 12 to 24 months, and all flaps of patients survived. The healing time for the incisions or sutures in flap donor and recipient sites was about 2 weeks, and the hand sensation recovered in 6 months after surgery. At the last follow-up, linear scar was left in the donor site on the lower leg; patients had partial sensory impairment on the dorsum of the foot, but there was no skin ulceration, which did not affect wearing shoes or walking; finger flexor strength was rated as grade 4 in 1 patient, grade 3 in 3 patients, and grade 2 in 1 patient; the sensory function of hands was evaluated as S3 + level in 4 patients, with the two-point discrimination distance of the skin ranging from 8 to 11 mm, while the sensory function of hands was evaluated as S3 level in 1 patient, with the two-point discrimination distance of the skin of 13 mm. Conclusions:Using the nerve-carrying peroneal artery perforator flaps to repair the nerve defects in the late stage of wrist electric burns, the sensation of hands can be restored in 6 months after surgery, with only linear scar in the flap donor sites and hypoesthesia in some areas of the dorsum of the foot. When combined with the reconstruction of finger flexion function, the overall function of hands can be effectively improved.

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