Diabetic kidney disease（DKD） is a chronic kidney structural and functional disorder caused by diabetes. With the global prevalence of diabetes continuing to rise， DKD has gradually become a major cause of chronic kidney disease and end-stage renal disease（ESRD）， posing a serious threat to patients' quality of life and long-term health outcomes. Studies have shown that apoptosis plays a pivotal role in the development and progression of DKD， with its mechanisms involving abnormal activation of multiple signaling pathways such as Toll-like receptor 4（TLR4）/nuclear transcription factor-κB（NF-κB）/B-cell lymphoma-2（Bcl-2）/cysteinyl aspartate-specific proteinase（Caspase）-3， protein kinase R-like endoplasmic reticulum kinase（PERK）/eukaryotic initiation factor 2α（eIF2α）/activating transcript factor 4（ATF4）/CCAAT enhancer-binding protein homologous protein（CHOP）， phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt）/glycogen synthase kinase-3β（GSK-3β）， Janus kinase 2（JAK2）/signal transducer and activator of transcription 3（STAT3）， adenosine monophosphate-activated protein kinase（AMPK）/mammalian target of rapamycin（mTOR） and silent information regulator 1（SIRT1）/tumor suppressor protein 53（p53）， thereby accelerating renal pathological damage in DKD. Extensive evidence-based medical studies have confirmed that traditional Chinese medicine（TCM）， leveraging its unique therapeutic advantages of multi-target， multi-component and multi-pathway approaches， has demonstrated remarkable efficacy and favorable safety profiles in treating DKD. Recent studies have demonstrated that active components of TCM can specifically target and modulate key effectors in apoptotic signaling pathways. Meanwhile， traditional compound formulations exert synergistic effects through multiple approaches such as replenishing deficiency and activating blood circulation， detoxifying and dredging collaterals， tonifying kidney essence， and removing stasis and purging turbidity， thereby comprehensively regulating critical pathological processes including endoplasmic reticulum stress and mitochondrial apoptosis pathways. This combined therapeutic approach of molecular targeting and holistic regulation provides novel strategies for delaying the progression of DKD. Based on this， this paper provides an in-depth analysis of key apoptotic signaling pathways and their regulatory mechanisms， while systematically summarizing recent research advances regarding the therapeutic effects of TCM active components， compound formulations， and proprietary Chinese medicines on DKD through modulation of these pathways， with particular emphasis on their underlying molecular mechanisms. These findings not only elucidate the modern scientific connotation and theoretical basis of TCM in treating DKD but also establish a solid theoretical and practical foundation for promoting the wider clinical application and further research of TCM in the field of DKD treatment.

OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

Background and Aims:Extrahepatic cholangiocarcinoma(ECCA)is a malignancy with insidious onset,strong invasiveness,and poor prognosis,characterized by a high postoperative recurrence rate and a 5-year overall survival of less than 20％.Most existing prognostic models are based on the Cox proportional hazards model,which is limited by the proportional hazards assumption and linearity constraints.The random survival forest(RSF)model,a novel machine learning algorithm,can capture complex interactions and nonlinear effects among variables;however,its application in ECCA remains scarce.Therefore,this study developed a prognostic model for ECCA patients after radical resection using the RSF algorithm,aiming to provide precise and individualized prognostic assessments and support clinical decision-making.Methods:A total of 515 postoperative ECCA patients from the SEER database(2016-2021)were retrospectively enrolled and randomly divided into a training set(n=361)and a test set(n=154).Demographic and clinical variables were collected.Cox models were developed using univariate and multivariate regression,while RSF models were constructed using variable importance(VIMP)and minimal depth methods.Model performance was evaluated using the concordance index(C-index),time-dependent area under the curve(AUC),Brier scores,calibration plots,and decision curve analysis.Survival differences were assessed using Kaplan-Meier analysis,and interpretability was enhanced through the use of SurvSHAP and SurvLIME.Results:Multivariate Cox regression identified seven independent prognostic factors:age,race,income,T stage,N stage,tumor size,and chemotherapy.The RSF model selected four key predictors:age,tumor size,lymph node positive rate,and chemotherapy.In the test cohort,the RSF model achieved a C-index of 0.751,outperforming the Cox model(0.711).The RSF model yielded AUCs of 0.843,0.749,and 0.814 at 1,2,and 3 years,respectively,with superior calibration,overall performance,and net clinical benefit.Nonlinear associations were observed for lymph node positive rate,age,and tumor size,while chemotherapy was associated with reduced mortality risk.Stratified survival curves indicated poorer prognosis in patients without chemotherapy,lymph node positive rate＞0.1,age＞70 years,or tumor size＞20 mm.Conclusion:The RSF model,based on only four readily available clinical variables,demonstrated superior predictive performance compared with the Cox model.It provides a reliable tool for individualized prognosis and postoperative management in ECCA patients.The integration of interpretability frameworks further enhances its clinical applicability,offering potential to improve survival outcomes and quality of life.

Background and Aims:Extrahepatic cholangiocarcinoma(ECCA)is a malignancy with insidious onset,strong invasiveness,and poor prognosis,characterized by a high postoperative recurrence rate and a 5-year overall survival of less than 20％.Most existing prognostic models are based on the Cox proportional hazards model,which is limited by the proportional hazards assumption and linearity constraints.The random survival forest(RSF)model,a novel machine learning algorithm,can capture complex interactions and nonlinear effects among variables;however,its application in ECCA remains scarce.Therefore,this study developed a prognostic model for ECCA patients after radical resection using the RSF algorithm,aiming to provide precise and individualized prognostic assessments and support clinical decision-making.Methods:A total of 515 postoperative ECCA patients from the SEER database(2016-2021)were retrospectively enrolled and randomly divided into a training set(n=361)and a test set(n=154).Demographic and clinical variables were collected.Cox models were developed using univariate and multivariate regression,while RSF models were constructed using variable importance(VIMP)and minimal depth methods.Model performance was evaluated using the concordance index(C-index),time-dependent area under the curve(AUC),Brier scores,calibration plots,and decision curve analysis.Survival differences were assessed using Kaplan-Meier analysis,and interpretability was enhanced through the use of SurvSHAP and SurvLIME.Results:Multivariate Cox regression identified seven independent prognostic factors:age,race,income,T stage,N stage,tumor size,and chemotherapy.The RSF model selected four key predictors:age,tumor size,lymph node positive rate,and chemotherapy.In the test cohort,the RSF model achieved a C-index of 0.751,outperforming the Cox model(0.711).The RSF model yielded AUCs of 0.843,0.749,and 0.814 at 1,2,and 3 years,respectively,with superior calibration,overall performance,and net clinical benefit.Nonlinear associations were observed for lymph node positive rate,age,and tumor size,while chemotherapy was associated with reduced mortality risk.Stratified survival curves indicated poorer prognosis in patients without chemotherapy,lymph node positive rate＞0.1,age＞70 years,or tumor size＞20 mm.Conclusion:The RSF model,based on only four readily available clinical variables,demonstrated superior predictive performance compared with the Cox model.It provides a reliable tool for individualized prognosis and postoperative management in ECCA patients.The integration of interpretability frameworks further enhances its clinical applicability,offering potential to improve survival outcomes and quality of life.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

Objective:To investigate the relationship between protein tyrosine phosphatase non-receptor type 1 (PTPN1) gene polymorphism and the risk of gestational diabetes mellitus (GDM).Methods:In this case-control study, 4 835 pregnant women who delivered from March, 2012 to July, 2014 in the Department of Gynecology and Obstetrics at the First Hospital of Shanxi Medical University were consecutively enrolled. Among them, 789 cases were diagnosed with GDM. A simple random sampling method was used to select 334 pregnant women with GDM as the case group, and 334 healthy pregnant women matched by maternal age, gestation time and residence were set as control. The DNA genotyping was performed in the subjects, and those with genotyping deletions10% were excluded; and finally, 322 and 317 subjects were included in case and control group, respectively. Under the codominant, dominant, recessive, and allelic genetic models, the unconditional logistic regression model was used to check the relationship between 13 candidate single nucleotide polymorphism (snp) loci in PTPN1 gene and the risk of GDM. The Haploview was used to analyze the relationship between haplotypes and risk of GDM, and multiple comparisons were adjusted with the false discovery rate (FDR) method.Results:The age of the 639 pregnant women analyzed in this study was (30.28±4.32) years. The proportions of pre-pregnancy body mass index (BMI)≥24.0 kg/m 2 and having a family history of diabetes were significantly higher in the GDM group compared to those in the control group (29.19% vs 16.72% and 13.04% vs 6.31%, respectively, both P0.05). The rs6096644 locus was positively associated with increased risk of GDM in co-dominant (GG vs AA, OR=2.76, 95% CI: 1.18-6.44) and recessive (GG vs AA+AG, OR=2.78, 95% CI: 1.20-6.46) genetic models (all q0.2). The rs6096655 locus was positively associated with increased risk of GDM in codominant (AA vs GG, OR=5.90, 95% CI: 1.27-27.36) and recessive (AA vs GG+GA, OR=5.50, 95% CI: 1.19-25.38) and alleles (A vs G, OR=1.51, 95% CI: 1.09-2.08) genetic models (all q0.2). The rs6013317 locus was associated with an increased risk of GDM in the allele (A vs G, OR=1.74, 95% CI: 1.15-2.63) genetic model (all q0.2). The GAGG haplotype and GGAG haplotype in haplotype block 1 (rs4811262, rs6096646, rs6096655, rs6013317), and the GGGA haplotype in haplotype block 2 (rs6068018, rs6123105, rs6013324, rs2869621) of the PTPN1 gene were all positively associated with an increased risk of GDM (all P0.05). Conclusion:PTPN1 gene polymorphisms may associated with risk of GDM, moreover, complex haplotype structures within the gene influence the risk of GDM.

Objective:To investigate the relationship between protein tyrosine phosphatase non-receptor type 1 (PTPN1) gene polymorphism and the risk of gestational diabetes mellitus (GDM).Methods:In this case-control study, 4 835 pregnant women who delivered from March, 2012 to July, 2014 in the Department of Gynecology and Obstetrics at the First Hospital of Shanxi Medical University were consecutively enrolled. Among them, 789 cases were diagnosed with GDM. A simple random sampling method was used to select 334 pregnant women with GDM as the case group, and 334 healthy pregnant women matched by maternal age, gestation time and residence were set as control. The DNA genotyping was performed in the subjects, and those with genotyping deletions10% were excluded; and finally, 322 and 317 subjects were included in case and control group, respectively. Under the codominant, dominant, recessive, and allelic genetic models, the unconditional logistic regression model was used to check the relationship between 13 candidate single nucleotide polymorphism (snp) loci in PTPN1 gene and the risk of GDM. The Haploview was used to analyze the relationship between haplotypes and risk of GDM, and multiple comparisons were adjusted with the false discovery rate (FDR) method.Results:The age of the 639 pregnant women analyzed in this study was (30.28±4.32) years. The proportions of pre-pregnancy body mass index (BMI)≥24.0 kg/m 2 and having a family history of diabetes were significantly higher in the GDM group compared to those in the control group (29.19% vs 16.72% and 13.04% vs 6.31%, respectively, both P0.05). The rs6096644 locus was positively associated with increased risk of GDM in co-dominant (GG vs AA, OR=2.76, 95% CI: 1.18-6.44) and recessive (GG vs AA+AG, OR=2.78, 95% CI: 1.20-6.46) genetic models (all q0.2). The rs6096655 locus was positively associated with increased risk of GDM in codominant (AA vs GG, OR=5.90, 95% CI: 1.27-27.36) and recessive (AA vs GG+GA, OR=5.50, 95% CI: 1.19-25.38) and alleles (A vs G, OR=1.51, 95% CI: 1.09-2.08) genetic models (all q0.2). The rs6013317 locus was associated with an increased risk of GDM in the allele (A vs G, OR=1.74, 95% CI: 1.15-2.63) genetic model (all q0.2). The GAGG haplotype and GGAG haplotype in haplotype block 1 (rs4811262, rs6096646, rs6096655, rs6013317), and the GGGA haplotype in haplotype block 2 (rs6068018, rs6123105, rs6013324, rs2869621) of the PTPN1 gene were all positively associated with an increased risk of GDM (all P0.05). Conclusion:PTPN1 gene polymorphisms may associated with risk of GDM, moreover, complex haplotype structures within the gene influence the risk of GDM.

Objective:To investigate the clinical value of adjuvant therapy after conversion resection for pancreatic cancer.Methods:The retrospective cohort study was conducted. The clinicopathological data of 173 patients with pancreatic cancer who underwent surgical resection after neoadjuvant and/or induction therapy in The First Affiliated Hospital of Naval Medical University from January 2019 to December 2021 were collected. There were 107 males and 66 females, aged (59±9)years. Observation indicators: (1) comparison of clinicopathological data between patients with and without adjuvant therapy after conversion resection for pancreatic cancer; (2) analysis of influencing factors for prognosis of pancreatic cancer after conversion resection; (3) follow-up and prognosis; (4) survival benefit of adjuvant therapy in subgroup populations. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the Mann-Whitney U test. Count data were expressed as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Comparison of ordinal data was conducted using the non-parameter rank sum test. The Graphpad prism 8 software was used to draw survival curves, the Kaplan-Meier method was used to calculate survival time and survival rates, and the Log-Rank test was used for survival analysis. The COX proportional hazards regression model was used for univariate and multivariate analyses. Interaction analysis was used to determine the benefit of adjuvant therapy in subgroup populations. Results:(1) Comparison of clinicopathological data between patients with and without adjuvant therapy after conversion resection for pancreatic cancer. Of the 173 pancreatic cancer patients, there were 108 cases with adjuvant therapy after conversion resection and 65 cases without adjuvant therapy after conversion resection, respectively. Age and body mass index were (58±9)years and (23.2±2.8)kg/m 2 in patients with adjuvant therapy, versus (61±8)years and (22.2±2.8)kg/m 2 in patients without adjuvant therapy, showing significant differences in the above indicators between them ( t=-2.036, 2.200, P<0.05). (2) Analysis of influencing factors for prognosis of pancreatic cancer after conversion resection. Results of multivariate analysis showed that CA19-9 normalization, pathological N staging, degree of tumor differentiation and postoperative adjuvant therapy were independent factors influencing overall survival time in pancreatic cancer patients receiving conversion resection ( hazard ratio=1.598, 1.541, 2.004, 2.571, 95% confidence interval as 1.041-2.453, 1.021-2.327, 1.288-3.118, 1.721-3.843, P<0.05). (3) Follow-up and prognosis. All 173 patients were followed up for 24.5(5.0,52.0)months. The postoperative median overall survival time of 173 patients was 28.9(5.7,51.9)months, and the 1-, 2-, 3-year overall survival rates were 90%, 59%, 40%, respectively. Of 2019, 2020, 2021, the proportions of patients receiving adjuvant therapy after conversion resection were 62.8%(27/43), 57.7%(30/52) and 65.4%(51/78) respectively. The postoperative median overall survival time was 42.2(8.8,49.7)months in patients with adjuvant therapy after conversion resection, versus 20.4(5.7,51.9)months in patients without adjuvant therapy after conversion resection, showing a significant difference between them ( χ2=29.893, P<0.05). (4) Survival benefit of adjuvant therapy in subgroup populations. Results of interaction analysis showed that in subgroup populations with CA19-9 normalization, pathological stage N0, pathological stage N1-2, moderate to well differentiated tumors, adjuvant therapy after conversion resection can bring a better survival benefit for patients with pancreatic cancer ( adjustment hazard ratio=0.220, 0.300, 0.410, 0.340, 95% confidence interval as 0.120-0.400, 0.170-0.560, 0.240-0.690, 0.210-0.690). Conclusions:Postoperative adjuvant therapy is an independent factor influencing overall survival time in pancreatic cancer patients receiving conversion resection. Adjuvant therapy after conversion resection can bring additional survival benefits for pancreatic cancer, particularly for patients who respond favorably to neoadjuvant and/or induction therapy.

Acute myeloid leukemia (AML) is a common malignant disease of the hematological system, with high EVI1 expression accounting for 8%-10% of adult AML. Studies have shown that high EVI1 expression plays an important role in the treatment and prognosis of AML. In recent years, researchers have continuously revealed the structure and role of EVI1, but its mechanism of mediating AML has not been fully clarified. Therefore, systematically exploring the role of EVI1 in AML may provide a useful reference for the precise treatment of AML patients with high EVI1 expression.

Objective:To investigate and compare the clinical outcomes of the arterial pre-occlusion technique(APOT) and the traditional technique in the surgery of locally advanced pancreatic cancer with arterial involvement after conversion therapy.Methods:This is a retrospective cohort study. The clinical data of 145 patients with locally advanced pancreatic cancer with arterial involvement admitted to the Department of Hepato-Biliary-Pancreatic Surgery of the First Hospital Affiliated to Naval Medical University,from January 2020 to December 2022 were retrospectively analyzed. All patients completed neoadjuvant therapy for tumors, and the feasibility of radical surgical treatment was determined by a multidisciplinary collaborative team evaluation before surgery. According to whether the intraoperative artery was pre-occluded, 145 patients were divided into two groups, including 28 cases in the APOT group(16 males, 12 females, aged (59.0±9.4) years), and 117 cases in the routine surgery group(76 males, 41 females, aged (55.1±8.2) years). To ensure comparability of baseline data between the APOT group and the routine surgery group, a 1∶2 match was performed using the propensity score matching method, and the caliper value was 0.006 45. The t-test,the Mann-Whitney U test, χ2 test or Fisher′s exact test were used to compare the data between the two groups,respectively. Results:After matching the propensity score,there were 28 cases in the APOT group and 56 cases in the routine surgery group. There were no significant differences in gender,age,preoperative comorbidities,preoperative body mass index,surgical approaches,chemotherapy regimen,stereotactic body radiation therapy ratio,tumor markers,and type of invaded artery between the two groups (all P>0.05).The arterial occlusion time M(IQR) in the APOT group was 7.0(3.8)minutes(range:3 to 15 minutes),and no ischemic manifestations were observed in the distal target organs that blocked blood vessels after surgery. The operation time was (170.3±57.7)minutes in the APOT group and (235.0±80.2)minutes in the routine surgery group,and the difference was statistically significant ( t=-3.800, P<0.01). The APOT group also experienced less intraoperative blood loss(650(588)ml vs. 800(600)ml; U=1 026.500, P=0.021). No significant differences were found between the groups in combined vein resection and reconstruction,celiac trunk resection,early postoperative complications, readmission rates at 30 days,and postoperative length of stay(all P>0.05). Extra-arterial dissection was performed in all patients,with arterial resection and reconstruction in 3 cases: 2 cases in the APOT group(1 case involving the superior mesenteric artery and 1 case involving the common hepatic artery) and 1 case in the routine group(involving the common hepatic artery). Postoperative abdominal bleeding occurred in 4 cases,with 3 cases in the routine group,1 case in the routine group. The R0 resection rate was 85.7%(24/28) in the APOT group and 80.4%(45/56) in the routine group,without significant differences between the groups( P=0.763). The median overall survival time was 27.6 months for the APOT group and 22.5 months for the routine group,while the median disease-free survival was 11.7 months and 16.8 months,respectively,with no significant differences between the two groups( P=0.532, P=0.927). Conclusion:The arterial pre-occlusion technique can be used for extra-arterial dissection in patients with locally advanced pancreatic cancer involving the arteries,reducing surgery time and intraoperative blood loss.