Objective: To evaluate the long-term effectiveness of an intelligent blood transfusion system in intraoperative blood management by comparing its performance with clinicians' decisions. Methods: A retrospective analysis of 26 760 surgical cases (2017-2024) was conducted, comparing pre- and post-implementation (2017-2019 vs 2020-2024) metrics, including transfusion prediction accuracy, rationality of blood use, and clinical outcomes. The system, powered by XGBoost, integrated patient demographics, laboratory results, and surgical data to predict red blood cell transfusion needs. Results: The intelligent blood transfusion systems achieved an accuracy of 80.62% in predicting transfusion necessity, significantly outperforming clinicians (24.83%, P<0.001). Its blood-use rationality rate was 83.92% vs 18.02% for clinicians (P<0.001). Post-implementation, major surgeries (grades Ⅲ-Ⅳ) increased while the requested blood units decreased. High physician compliance (>75%) correlated with 88.18% rationality. Conclusion: The intelligent blood transfusion system significantly improves the accuracy of transfusion decision-making, reduces excessive red blood cell use, optimizes perioperative transfusion management, and enhances the utilization of blood medical resources.

Objective:To investigate the preventive and therapeutic effects and mechanisms of Dachaihu decoction(DCD)on dextran sodium sulfate(DSS)-induced ulcerative colitis(UC)and liver injury in mice,as well as the association between DCD benefits and gut microbiota modulation.Methods:Mice were treated with DCD(20.10 and 10.05 g/kg)for 2 weeks,with free access to drinking water containing 3%DSS in the second week to induce UC.Histopathological examination,RT-qPCR and 16S rRNA sequencing were used to investigate the effect of DCD on UC mice.Results:DCD pretreatment significantly alleviated weight loss,bloody diarrhea with mucus,histopathological abnormalities of the colon,and colon shortening in mice with DSS-induced UC.In addition,DCD pretreat-ment significantly upregulated the levels of Occludin,ZO-1,and MUC-2 in the colon and protected the intestinal barrier of mice.DCD pretreatment also alleviated inflammatory cell infiltration in the colon and the liver and significantly reduced the expression levels of the proinflammatory factors such as IL-1β,IL-6,TNF-α,iNOS,COX-2,and NLRP3,thereby exerting a protective effect against UC and liver injury.It should be noted that DCD corrected gut micro-biota imbalance in UC mice by enriching probiotic bacteria such as Lactobacillus and Bifidobacterium and reducing harmful bacteria such as Norank_f_Desulfovibrionaceae and Escherichia-Shigella.Conclusion:DCD can alleviate DSS-induced UC and exert a liver-protecting effect by protecting intestinal barrier,inhibiting inflam-mation,and regulating gut microbiota.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective To gain an in-depth understanding of influencing factors on the preventive behaviors of operating room nurses regarding hypothermia during cesarean sections,and to provide references for promoting the prevention of hypothermia during cesarean sections.Methods A descriptive qualitative research approach was adopted,based on the theoretical framework of the Precede-Proceed Model to develop an interview outline.Semi-structured interviews were conducted with 12 operating room nurses,and the interview data were analyzed using directed content analysis.Results The influencing factors were categorized into 3 main themes and 8 sub-themes.①Predisposing factors:insufficient theoretical knowledge,low risk awareness among healthcare providers.②Enabling factors:rational allocation of equipment/resources,proficient operational skills,targeted and effective training programs,and standardized preventive procedures.③Reinforcing factors:the need to enhance team cooperation,the need to improve the regulatory mechanism.Conclusion Nursing managers should strengthen education and training to enhance nurses'awareness and skill levels in preventing hypothermia,improve equipment resources and build an information-based assessment and decision support system to promote the implementation of hypothermia prevention behaviors.Additionally,facilitating team collaboration and utilizing information construction to collect and analyze quality indicators can strengthen quality management and promote continuous improvement in the quality of hypothermia prevention during cesarean sections.

Objective : To investigate the diagnostic value of combined detection of folate receptor-positive circulat- ing tumor cells ( FR + -CTCs) and hemostatic function indicators in improving the diagnosis of gastrointestinal tumors ( GITs) metastasis． Methods: A retrospective analysis was conducted on the clinical data of 115 patients aged 18 to 80 years who were diagnosed with gastrointestinal tumors via pathology and received treatment，including data on FR + -CTCs，hemostatic function indicators，and pathological staging．The collected data encompassed FR + -CTCs levels，coagulation parameters，and pathological staging．Statistical analysis included t-tests，chi-square tests，fish- er ’s exact test，Logistic regression analysis，and receiver operating characteristic ( ROC) curves to assess the diag- nostic value of combined FR + -CTCs and coagulation parameters in detecting tumor metastasis． Results: FR + -CTCs levels and positive rates demonstrated significant associations with clinicopathological characteristics ( gender，histo- logical type，N staging) in GITs patients ( P＜0. 05) ．In patients with metastasis，elevated fibrinogen levels were observed．Adithonallly，platelet counts showed significant increases in N1 －N3 stages ( P＜0. 05) ．Logistic regres- sion analysis showed that PLT and antithrombin Ⅲ ( AT-Ⅲ) were independent risk factors for GITs metastasis ( P＜0. 05) ． The areas under the ROC curves for predicting GITs metastasis were 0. 678 ( 95% CI: 0. 540 - 0. 816) and 0. 664 ( 95% CI: 0. 512 －0. 815) ，respectively．When combining multiple factors，including FR + - CTCs，PLT，AT-Ⅲ , pathological type，FIB，TT，and gender，for the diagnosis of GITs metastasis，the AUC in- creased to 0. 757 ( 95% CI: 0. 621 －0. 893) ，indicating higher sensitivity and specificity compared to using each indicator alone． Conclusion The combined detection of FR + -CTCs and anticoagulation function indicators has a higher diagnostic value for the diagnosis of GITs，providing a valuable basis for the early diagnosis of GITs，espe- cially in metastasis surveillance．

Background and Aims:Microsatellite-stable(MSS)colorectal cancer(CRC)generally exhibits poor responsiveness to immune checkpoint inhibitors(ICIs),and effective immunotherapy strategies remain lacking.Anti-angiogenic agents such as bevacizumab(BEV)can improve the tumor immune microenvironment and act synergistically with ICIs.This multicenter real-world study compared the efficacy of different immunotherapy-based combination regimens in patients with MSS/MSI-L/pMMR advanced CRC,aiming to identify the optimal treatment strategy.Methods:A total of 100 patients with MSS/MSI-L/pMMR advanced CRC who received systemic treatment between November 2019 and February 2025 at four tertiary hospitals in Hunan,China,were retrospectively enrolled.Patients were classified into six treatment groups:chemotherapy alone,chemotherapy+targeted therapy,immunotherapy alone,immunotherapy+chemotherapy,immunotherapy+targeted therapy,and immunotherapy+chemotherapy+targeted therapy.The primary endpoints were overall survival(OS)and progression-free survival(PFS),while secondary endpoints were objective response rate(ORR)and disease control rate(DCR).Additionally,among patients receiving immunotherapy,subgroup analysis was performed according to BEV administration.Results:Among all 100 patients,the immunotherapy+chemotherapy+targeted therapy group achieved the highest ORR(32.0%)and DCR(76.0%)and was the only regimen yielding a complete response(CR).Compared with chemotherapy or immunotherapy alone,the triplet regimen significantly improved OS(P<0.05);although PFS improvement did not reach statistical significance,a clear late-stage separation of survival curves was observed.In the immunotherapy subgroup,BEV-containing regimens achieved markedly better outcomes than non-BEV regimens,with DCR of 75.0%vs.48.8%,median OS of 18.9 vs.11.5 months,and median PFS of 13.8 vs.7.2 months(all P<0.001).Cox regression analysis showed that compared with chemotherapy alone,the triplet regimen significantly reduced the risk of death(HR=0.11)and disease progression(HR=0.25)(both P=0.002).Vascular invasion was identified as an adverse prognostic factor for PFS(HR=3.0,P=0.007).Conclusion:This multicenter real-world study demonstrated that combining immunotherapy with chemotherapy and targeted therapy significantly improves DCR and survival outcomes in patients with MSS/MSI-L/pMMR advanced CRC,with BEV-containing triplet regimens providing the most pronounced benefit.BEV may enhance immune responsiveness by modulating the tumor microenvironment and promoting effector T-cell infiltration,offering a promising therapeutic direction for"immune-cold"CRC.Prospective randomized studies are warranted to further validate its clinical value and define appropriate patient populations.

BACKGROUND:Ankle inversion injuries frequently occur during landing,injuring the anterior talofibular and calcaneofibular ligaments.Previous studies usually used indirect indicators,such as inversion angle,as an injury risk indicator,but epidemiological support is lacking.OBJECTIVE:To calculate anterior talofibular and calcaneofibular ligament strains using a three-dimensional multi-body foot model during a drop-landing and to investigate whether increasing the foot toe-out angle for landing would reduce the risk of inversion sprains.METHODS:Thirty-five participants with high sports demands[15 males and 20 females,age:(21.0±0.9)years,height:(176.2±8.8)cm,body mass:(71.6±12.8)kg]were recruited to perform a drop-landing test using a trapdoor device to simulate ankle inversion sprains.Two landing conditions were tested,i.e.,natural landing and toe-out landing.Kinematic data were collected using a 12-camera motion analysis system,the strains of the anterior talofibular and calcaneofibular ligaments were calculated using a three-dimensional rigid-body foot model.RESULTS AND CONCLUSION:From natural landing to toe-out landing conditions,the anterior talofibular ligament strain decreased[natural landing=(3.57±1.92)％,toe-out landing=(0.36±1.18)％,P＜0.001,Cohen's d=2.01),as was the calcaneofibular ligament strain[natural landing=(1.38±1.80)％,toe-out landing=(0.28±2.29)％,P=0.003,Cohen's d=0.81).It could be concluded that increasing foot toe-out angle reduces anterior talofibular and calcaneofibular ligament strains during drop-landing with ankle inversion,thereby reducing the potential of ankle inversion sprains.

Alzheimer disease(AD)is a neurological disease characterized by cognitive decline.AD continuum refers to the dynamic development progression from early pathological changes to obvious clinical symptoms,covering the continuous spectrum of subjective cognition decline(SCD)stage,mild cognitive impairment(MCI)stage and dementia stage.As one of the key brain regions involved in early stage,hippocampus(HP)in AD continuum is closely related to the progression of disease.MRI has been widely used in the study of HP in the AD continuum.The progresses in structural and functional MRI research of HP changes in AD continuum were reviewed in this article.

BACKGROUND:Ankle inversion injuries frequently occur during landing,injuring the anterior talofibular and calcaneofibular ligaments.Previous studies usually used indirect indicators,such as inversion angle,as an injury risk indicator,but epidemiological support is lacking.OBJECTIVE:To calculate anterior talofibular and calcaneofibular ligament strains using a three-dimensional multi-body foot model during a drop-landing and to investigate whether increasing the foot toe-out angle for landing would reduce the risk of inversion sprains.METHODS:Thirty-five participants with high sports demands[15 males and 20 females,age:(21.0±0.9)years,height:(176.2±8.8)cm,body mass:(71.6±12.8)kg]were recruited to perform a drop-landing test using a trapdoor device to simulate ankle inversion sprains.Two landing conditions were tested,i.e.,natural landing and toe-out landing.Kinematic data were collected using a 12-camera motion analysis system,the strains of the anterior talofibular and calcaneofibular ligaments were calculated using a three-dimensional rigid-body foot model.RESULTS AND CONCLUSION:From natural landing to toe-out landing conditions,the anterior talofibular ligament strain decreased[natural landing=(3.57±1.92)％,toe-out landing=(0.36±1.18)％,P＜0.001,Cohen's d=2.01),as was the calcaneofibular ligament strain[natural landing=(1.38±1.80)％,toe-out landing=(0.28±2.29)％,P=0.003,Cohen's d=0.81).It could be concluded that increasing foot toe-out angle reduces anterior talofibular and calcaneofibular ligament strains during drop-landing with ankle inversion,thereby reducing the potential of ankle inversion sprains.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.