Objective To preliminarily investigate the safety and efficacy of donor pancreas needle biopsy in simultaneous pancreas-kidney transplantation. Methods Clinical data of 7 cases undergoing donor pancreas biopsy were collected retrospectively. All cases underwent donor pancreas biopsy before or during simultaneous pancreas-kidney transplantation. Frozen section or paraffin sectioning techniques were used for tissue preparation, and hematoxylin-eosin and Masson staining were performed to histologically evaluate the donor pancreas. The quality of donor pancreas was comprehensively assessed by combining histological findings with the donor's clinical data. Postoperative follow-up data of 5 simultaneous pancreas-kidney transplant recipients were collected to summarize the safety of donor pancreas biopsy and the prognosis of transplant recipients. Results The 7 pancreas donors were aged 28 to 62 years, with a body mass index ranging from 20.76 to 27.68 kg/m². Liver ultrasound indicated fatty liver in 3 cases, while pancreatic ultrasound did not reveal any significant abnormalities. Among them, biopsy was performed on 2 donors after completion of pancreatic procurement and processing, and the frozen section histology showed moderate acute pancreatitis changes (edema of acinar cells, necrosis and inflammatory cell infiltration). Combined with a serum amylase level elevated more than 3 times the upper limit of normal value, these two donor pancreases were finally discarded. The remaining 5 cases underwent biopsy immediately after pancreatic vascular anastomosis during simultaneous pancreas-kidney transplantation, and histological evaluation was performed on paraffin-embedded sections. No biopsy-related complications (such as bleeding, pancreatic fistula, etc.) occurred after transplantation. One recipient died of severe infection 2 months after transplantation, while the other 4 recipients were followed up for more than 5 years, with well-functioning transplant kidneys and pancreases. Conclusions Donor pancreas biopsy is relatively safe, and the risk of biopsy-related complications after transplantation is controllable. Comprehensive assessment of donor pancreas quality by combining histological evaluation with the donor's clinical indicators is conducive to improving the accuracy of donor pancreas selection and organ utilization.

This study aims to understand the biological characteristics of Streptococcus dysgalacti-ae of yak origin.Bacterial isolation and identification,drug susceptibility test,virulence gene test and pathogenicity test were carried out on milk samples of yaks from Naqu City to evaluate the bi-ological characteristics of the isolated strains.Meanwhile,molecular biological information such as virulence factors and drug resistance genes were analyzed by whole genome sequencing,and viru-lence genes were verified by PCR.The results showed that a strain of Streptococcus dysgalactiae was isolated from the milk of yak,and its colony morphology was pinpoint size,smooth edge and milky white.This strain is sensitive to many antibiotics(penicillin G,cephalosporin,ciprofloxacin,tetracycline,erythromycin,etc.).Virulence gene test results showed that the strain carries six key virulence genes(cyl,eno,scpB,bca,bac and napr),which may be closely related to its pathoge-nicity.In the pathogenicity test,the mice were listless and less active after infection,but no death occurred during the observation period.The pathological changes of spleen,kidney,liver and lung tissue were found,suggesting that the strain had certain pathogenic potential but not high lethali-ty.Whole genome sequencing data showed that the gene length of this strain was 4 079 280 bp,the GC content was 39.41％,3 964 coding genes were predicted,604 of which were annotated as viru-lence factors,and another 28 gene mutations may enhance its pathogenic ability.Through annota-tion of CARD database,two Pat A resistance genes and two lmrp resistance genes were found,re-vealing their potential resistance mechanism.Through whole genome sequencing technology and bioinformatics analysis method,this study revealed the genomic characteristics,drug resistance and pathogenicity mechanism of Streptococcus dysgalactiae of yak origin.The findings provide impor-tant scientific evidence for further exploration of the pathogenicity,drug resistance mechanisms,and molecular evolution of yak-derived Streptococcus agalactiae.

AIM:To clarify the molecular mechanism by which astragaloside Ⅳ(AS-Ⅳ)suppresses oxida-tive stress and alleviates cerebral ischemia-reperfusion injury(CIRI)via the PTEN-induced kinase 1(PINK1)/parkin mi-tophagy-associated pathway.METHODS:A middle cerebral artery occlusion/reperfusion(MCAO/R)model was estab-lished in Sprague-Dawley rats.The animals were allocated to sham,MCAO/R,AS-Ⅳ,and mitochondrial division inhibi-tor-1(Mdivi-1)treatment groups.The rats in AS-Ⅳ and Mdivi-1 groups were intraperitoneally injected once daily with AS-Ⅳ(20 mg/kg)for 7 d,while those in Midivi-1 group also received intraperitoneal injection of Mdivi-1(1.2 mg·kg-1·d-1).The rats in sham and MCAO/R groups were given equivalent volume of distilled water.Neurological deficits were as-sessed using Zea Longa scoring,infarcted area volumes were measured using TTC staining,and brain tissue pathology was examined using hematoxylin and eosin staining.The levels of malondialdehyde(MDA)and superoxide dismutase(SOD)were assessed by ELISA,while those of reactive oxygen species(ROS)were measured using flow cytometry.The expres-sion levels of PINK1,parkin and microtubule-associated protein 1 light chain 3(LC3)were quantified using Western blot and RT-qPCR.RESULTS:AS-Ⅳ administration significantly alleviated neuronal and mitochondrial damage in MCAO/R model rat brains(P＜0.05),together with significant reductions in the cerebral infarct volume and neurological dysfunc-tion(P＜0.05).Significant increases in PINK1,parkin and LC3 protein and mRNA levels were observed in response to AS-Ⅳ(P＜0.05),SOD activity rose,and ROS and MDA levels declined significantly(P＜0.05).The co-administration of Mdivi-1 abrogated the protective benefits of AS-Ⅳ,inhibited activation of the PINK1/parkin pathway,down-regulated LC3 at the mRNA and protein levels,and significantly increased mitochondrial damage.Mdivi-1 also markedly reduced autophagosome formation and SOD activity level,but increased both ROS and MDA levels,cerebral infarct volume,and the severity of neurological deficits(P＜0.05).CONCLUSION:Astragaloside Ⅳ activates the PINK/parkin-mediated mitophagy pathway,inhibits oxidative stress and alleviates CIRI in rats.

Objective To investigate the distribution and antibiotic resistance of clinical isolates in the Traditional Chinese Medicine Hospital of Xinjiang Medical University.Methods Bacterial strains were collected from January 1,2020,to December 31,2023,and tested for antimicrobial susceptibility using automated systems and disk diffusion methods.The results were interpreted according to the breakpoints recommended in the CLSI M100.Results Over the four-year period,22 121 bacterial strains were analyzed,including Gram-positive bacteria(24.1％,5 338/22 121)and Gram-negative bacteria(75.9％,16 783/22 121).The prevalence of methicillin-resistant S.aureus(MRSA)and methicillin-resistant coagulase-negative Staphylococcus(MRCNS)was 26.4％and 68.9％,respectively.MRSA and MRCNS strains showed higher resistance rates to most antimicrobial agents compared to methicillin-susceptible strains(MSSA and MSCNS).No Staphylococcus strains were found resistant to linezolid or vancomycin.E.faecium showed higher resistance rates to most of the antimicrobial agents tested than E.faecalis.A few vancomycin-resistant strains were identified in E.faecium and limited number of linezolid-resistant strains were identified in E.faecalis.All S.pneumoniae isolates were isolated from specimens other than cerebrospinal fluid.The prevalence of penicillin-resistant S.pneumoniae(PRSP)was 9.4％.Overall,13.2％of Klebsiella isolates were resistant to imipenem and 13.1％to meropenem,while the prevalence of carbapenem-resistant strains was less than 10％in Escherichia coli or other genera of Enterobacterales.As for non-fermenting Gram-negative bacteria,P.aeruginosa was largely susceptible to most antimicrobial agents.Overall,11.8％and 10.9％of P.aeruginosa strains were resistant rates to imipenem and meropenem,respectively.However,47.1％of Acinetobacter strains were resistant to imipenem and 47.9％to meropenem,while 14.9％to 53.2％of the strains were resistant to other antimicrobial agents tested.Conclusions The clinical strains isolated in the Traditional Chinese Medicine Hospital of Xinjiang Medical University were predominantly Gram-negative bacteria.The prevalence of carbapenem-resistant strains in Klebsiella spp.was higher than that in other species of Enterobacterales.Acinetobacter spp.showed high resistance rate to carbapenems.The prevalence of methicillin-resistant strains was high in Staphylococcus.Vancomycin-resistant and linezolid-resistant strains were identified in Enterococcus spp.Infection prevention and control and stewardship of antimicrobial agents should be strengthened to contain the emergence and spread of resistant bacteria.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. 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. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

BACKGROUND:Diabetes exacerbates intervertebral disc degeneration in a number of ways,and good glycemic control is beneficial in preventing intervertebral disc degeneration.OBJECTIVE:To review the relationship between diabetes and intervertebral disc degeneration to provide a reference for the clinical treatment of disc degeneration in patients with diabetes.METHODS:Literature searches were performed in CNKI and PubMed databases for articles published from 1980 to 2023.The Chinese and English search terms were"diabetes,intervertebral disc degeneration,cartilage endplate degeneration,apoptosis,advanced glycation end products,osmotic stress."Finally,73 articles were included for summary and analysis.RESULTS AND CONCLUSION:(1)The pathophysiological process of diabetes-induced intervertebral disc degeneration is different from that of physiological degeneration.The main mechanisms of diabetes-induced intervertebral disc degeneration include:intracellular hyperglycemia,impaired blood supply to the intervertebral discs due to microvascular pathology,cellular senescence,apoptosis,and autophagy,accumulation of advanced glycation end products,osmotic stress,and destruction of the extracellular matrix components due to other pathways.(2)Drugs such as curcumin,resveratrol,and lupeol have therapeutic effects on intervertebral disc degeneration,but their safety and effectiveness need to be further demonstrated in clinical treatment.

Objective To investigate the mechanisms by which astaxanthin(AST)alleviates oxaliplatin(OXA)-induced neuropathic pain through antioxidant pathways so as to provide theoretical basis for clinical intervention.Methods Animal experiments:SD rats were divided into five groups(n=6):control group,OXA(4 mg/kg)group,OXA+Oil group,OXA+AST(5 mg/kg)group,and OXA+AST(10 mg/kg)group.Mechanical and cold pain thresholds were measured at day 0,7,14,and 21.Malondialdehyde(MDA)content and superoxide dismutase(SOD)activity in the dorsal root ganglia(DRG)were detected using the thiobarbituric acid(TBA)method and WST-1 assay,respectively.Western blotting was performed to analyze the expressions of Nrf2 and HO-1.Cell experiments:neuro-2a cells were divided into control group,OXA(50 μmol/L)group,AST(10 μmol/L)group,and OXA(50 μmol/L)+AST(10 μmol/L)group.Cells were treated with nerve growth factor(NGF,50 ng/mL)to induce growth,and morphological changes were observed under an inverted microscope.Intracellular reactive oxygen species(ROS)level and mitochondrial superoxide were measured using DCFH-DA fluorescent probe and MitoSOXTM red,respectively.Mitochondrial function was assessed by JC-1 assay.Western blotting was used to detect Nrf2 and HO-1 expressions.Results Animal experiments:① Mechanical and cold pain thresholds were reduced in OXA and OXA+Oil groups(P＜0.05),while AST significantly increased these thresholds in OXA-treated rats(P＜0.05).② SOD activity decreased while MDA content increased in the DRG of OXA-treated rats(P＜0.05).AST restored SOD activity and reduced MDA level(P＜0.05,P＜0.01).③ Western blotting showed elevated Nrf2 and HO-1 expressions in OXA group(P＞0.05),which were further upregulated by AST(P＜0.05,P＜0.01).Cell experiments:① OXA reduced the number of neurite-bearing cells and shortened the average neurite length(P＜0.05).Inverted microscopic observation revealed that AST intervention increased both parameters(P＜0.01,P＜0.001).② OXA increased intracellular and mitochondrial ROS fluorescence intensity(P＜0.05),which was attenuated by AST(P＜0.01).③ JC-1 assay revealed decreased mitochondrial membrane potential in OXA group(P＜0.01),which was partially reversed by AST(P＜0.05).④ Western blotting results showed that OXA upregulated Nrf2 and HO-1 expressions(P＜0.05,P＜0.01),and AST further enhanced their levels(P＜0.01).Conclusion AST alleviates OXA-induced neuropathic pain by promoting Nrf2/HO-1 expression,enhancing SOD activity,reducing lipid peroxidation and ROS production,and improving mitochondrial function.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. 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. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

BACKGROUND:Diabetes exacerbates intervertebral disc degeneration in a number of ways,and good glycemic control is beneficial in preventing intervertebral disc degeneration.OBJECTIVE:To review the relationship between diabetes and intervertebral disc degeneration to provide a reference for the clinical treatment of disc degeneration in patients with diabetes.METHODS:Literature searches were performed in CNKI and PubMed databases for articles published from 1980 to 2023.The Chinese and English search terms were"diabetes,intervertebral disc degeneration,cartilage endplate degeneration,apoptosis,advanced glycation end products,osmotic stress."Finally,73 articles were included for summary and analysis.RESULTS AND CONCLUSION:(1)The pathophysiological process of diabetes-induced intervertebral disc degeneration is different from that of physiological degeneration.The main mechanisms of diabetes-induced intervertebral disc degeneration include:intracellular hyperglycemia,impaired blood supply to the intervertebral discs due to microvascular pathology,cellular senescence,apoptosis,and autophagy,accumulation of advanced glycation end products,osmotic stress,and destruction of the extracellular matrix components due to other pathways.(2)Drugs such as curcumin,resveratrol,and lupeol have therapeutic effects on intervertebral disc degeneration,but their safety and effectiveness need to be further demonstrated in clinical treatment.

Objective:To evaluate the efficacy of modified splenic arteriovenous shunt surgery at the distal pancreatic tail in combined pancreas-kidney transplantation.Methods:A retrospective analysis was conducted on 24 recipients who underwent combined pancreas-kidney transplantation with the modified splenic arteriovenous shunt at the pancreatic tail from November 2023 to October 2024 (shunt group) and 231 recipients who received conventional splenic artery and vein ligation since 2016 (ligation group). The incidence of perioperative thrombosis and severe adverse events was compared between the two groups using the chi-square test or Fisher's exact test. Independent sample t-tests were performed to assess postoperative pancreatic and renal function recovery as well as blood perfusion in 15 recipients from the shunt group and 20 from the ligation group who underwent CT perfusion imaging (CTP).Results:The incidence of perioperative splenic arteriovenous thrombosis was lower in the shunt group (0) compared to the ligation group (4.76%, 11/231), though the difference was not statistically significant ( P=0.606). One month postoperatively, the shunt group demonstrated significantly lower serum amylase levels than the ligation group (99.61±19.62 vs. 148.20±70.67 U/L, P=0.018). However, at the time of CTP examination, serum lipase (67.87±32.35 vs. 45.11±17.94 U/L, P=0.014) and creatinine levels (131.79±26.41 vs. 112.1±24.98 μmol/L, P=0.034) were significantly higher in the shunt group. Urea nitrogen levels were also significantly higher in the shunt group both one month postoperatively (11.24±4.64 vs. 8.51±3.01 mmol/L, P=0.043) and at the CTP examination (10.41±1.78 vs. 6.87±1.91 mmol/L, P=0.001). Regarding pancreatic perfusion, blood volume in both the pancreatic head (15.99 ± 3.51 vs. 20.67 ± 5.47 ml/100 g, P = 0.024) and tail (17.19±4.24 vs. 27.40±19.80 ml/100 g, P=0.039) was significantly lower in the shunt group. After one minute of splenic artery perfusion, the shunt group exhibited significantly higher splenic artery blood flow (755.85±101.50 vs. 574.00 ± 142.06 ml·min -1· (100 g) -1, P<0.001) and blood volume (58.90 ±19.93 vs. 23.21±17.02 ml/100 g, P=0.007) compared to the ligation group. These differences persisted after two minutes of perfusion (blood flow: 793.83±68.57 vs. 503.78 ± 130.80 ml·min -1· (100 g) -1, P<0.001; blood volume: 64.22±15.74 vs. 34.32±20.39 ml/100 g, P=0.002). For the transplanted kidney, the shunt group had significantly lower blood flow (113.10±28.55 vs. 232.76±113.37 ml·min -1· (100 g) -1, P<0.001), blood volume (28.95±10.79 vs. 38.36±12.38 ml/100 g, P=0.047), and capillary surface permeability (PS) (26.49±16.57 vs. 43.02±20.37, P = 0.042) in the upper pole. Similar reductions in blood flow, blood volume, and PS were observed in the middle dorsal region ( P=0.018, 0.021, and 0.048, respectively) and lower pole ( P<0.001, P=0.048, and P=0.012, respectively). Conclusion:The modified splenic arteriovenous shunt at the pancreatic tail appears to be a safe and effective approach to reducing the risk of pancreatic graft thrombosis. This technique facilitates effective diversion of pancreatic parenchymal blood flow into the splenic vein, alleviating hyperperfusion of the transplanted pancreas. While renal blood perfusion was reduced postoperatively, it did not adversely affect renal function.