Objective: To assess the clinical application value of a prediction model for red blood cell (RBC) demand in surgical procedures. Methods: Demographic data, laboratory parameters, anesthesia and transfusion records, and model prediction data were retrospectively collected from surgical patients at the First Medical Center of Chinese PLA General Hospital between 2018 and 2024. Statistical analysis was performed using the Chi-square test, t-test, and Mann-Kendall trend test. Results: From 2018 to 2024, the predictive model for RBC demand in surgical procedures was used to evaluate a total of 112 293 surgeries. During this period, the model call rate (77.49%-98.91%, P<0.05), compliance rate (56.81%-84.92%, P<0.05), and prediction accuracy rate (66.82%-94.17%, P<0.05) all showed significant upward trends. The total blood usage across the hospital (13645.4-7723.5 units, P<0.05) and the average blood usage per surgery (0.21-0.1 units, P<0.05) exhibited overall downward trends. Postoperative average hemoglobin levels in the non-compliance group (112.1-105.3 g/L in the non-compliance group vs 106.9-92.7 g/L in the compliance group, P<0.05) and the intraoperative excessive transfusion rate (5.06%-6.05% in the non-compliance group vs 0.09%-0.04% in the compliance group, P<0.05) were significantly higher in the non-compliance group compared to the compliance group. Conclusion: The predictive model for RBC demand in surgical procedures has played a positive role in conserving blood resources, optimizing blood resource allocation, and reducing intraoperative risks.

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.

Objective To understand the clinical applicability and implementation of expert consensus on the implementation and removal of protective restraints in psychiatry,and to provide references for promoting the standardized practice of psychiatric protective restraints and updating the consensus.Methods By the convenience sampling method,a questionnaire survey was conducted among nurses from 480 hospitals in 30 provinces from June 15 to July 15,2024.The survey was conducted using the instrument for evaluating clinical applicability of guide-lines(version 2.0)and a self-compiled questionnaire on the clinical implementation of the restraint consensus.Results A total of 7,844 valid questionnaires were collected,with a valid questionnaire recovery rate of 93.78％.The results of clinical applicability scoring showed that the consensus had the lowest availability score(64.72％)and the highest acceptability score(76.74％).The results showed that nurses' receiving training and the level of their hospitals were the main influencing factors for scores in various dimensions(P＜0.05).4,774 participants(87.42％)believed that the application of consensus could enhance the standardization of nurses' restraint operations.The safety rate of the restraint consensus was 79.51％,and the economic ratio was 76.87％.Among the evaluators,1,739(22.17％)believed that there were implementation obstacles in the consensus.Conclusion The clinical applicability of the consensus is relatively good,and the application of the consensus helps to improve the standardization of clinical operations.In the future,efforts should be made to strengthen the promotion and training of the consensus,develop hierarchical promotion strategies according to the characteristics of medical institutions,and improve the quality of evidence for the consensus,so as to further enhance the clinical application effect of the consensus.

Objective To understand the clinical applicability and implementation of expert consensus on the implementation and removal of protective restraints in psychiatry,and to provide references for promoting the standardized practice of psychiatric protective restraints and updating the consensus.Methods By the convenience sampling method,a questionnaire survey was conducted among nurses from 480 hospitals in 30 provinces from June 15 to July 15,2024.The survey was conducted using the instrument for evaluating clinical applicability of guide-lines(version 2.0)and a self-compiled questionnaire on the clinical implementation of the restraint consensus.Results A total of 7,844 valid questionnaires were collected,with a valid questionnaire recovery rate of 93.78％.The results of clinical applicability scoring showed that the consensus had the lowest availability score(64.72％)and the highest acceptability score(76.74％).The results showed that nurses' receiving training and the level of their hospitals were the main influencing factors for scores in various dimensions(P＜0.05).4,774 participants(87.42％)believed that the application of consensus could enhance the standardization of nurses' restraint operations.The safety rate of the restraint consensus was 79.51％,and the economic ratio was 76.87％.Among the evaluators,1,739(22.17％)believed that there were implementation obstacles in the consensus.Conclusion The clinical applicability of the consensus is relatively good,and the application of the consensus helps to improve the standardization of clinical operations.In the future,efforts should be made to strengthen the promotion and training of the consensus,develop hierarchical promotion strategies according to the characteristics of medical institutions,and improve the quality of evidence for the consensus,so as to further enhance the clinical application effect of the consensus.

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.

Objective:To analyze the distribution and epidemiological characteristics of common non-bacterial pathogens in hospitalized children with acute respiratory tract infections(ARTI)from a multi-center study covering 4 regions in Fujian Province in 2023.Methods:A retrospective cohort study was conducted using medical record analysis.A total of 22 769 hospitalized children with ARTI were enrolled from January to December 2023 across seven regional pediatric medical centers in Fujian Province (covering four major geographical divisions of Fuzhou, Nanping, Sanming and Longyan; all selected hospitals were regional children′s medical centers).Using single-tube multiplex PCR with fragment analysis on a Sanger sequencing platform, the nucleic acids of 11 common non-bacterial respiratory pathogens were tested in nasopharyngeal swabs collected from 22 769 children. These pathogens included influenza A virus(FluA), influenza B virus(FluB), parainfluenza virus(PIV), respiratory syncytial virus (RSV), adenovirus (ADV), human rhinovirus (HRV), human bocavirus (HBoV), human coronavirus (HCoV), human metapneumovirus(HMPV), Mycoplasma pneumoniae(MP), and Chlamydia (Ch). Count data were described as [ n(%)], and the chi-square test/Fisher′s exact test was used to compare the differences in rates between groups. Epidemiological features, including positive detection rates, pathogen profiles, and correlations with region, sex, age and month, were analyzed. Results:Among 22 769 children with ARTI, pathogens were detected in 16 213 cases (71.21%), including 13 340 single infections (58.59%).The detection rates of single pathogens in descending order were human rhinovirus (HRV, 12.95%), Mycoplasma pneumoniae(MP, 12.27%), respiratory syncytial virus(RSV, 11.12%), influenza A virus (Flu-A, 7.98%), parainfluenza virus(PIV, 4.66%), human metapneumovirus(HMPV, 4.60%), adenovirus(ADV, 2.70%), human bocavirus(HBoV, 0.84%), human coronavirus(HCoV, 0.82%), influenza B virus(Flu-B, 0.47%) and Chlamydia(Ch, 0.18%).Mixed infections occurred in 2 873 cases(12.62%), primarily dual infections(2 679 cases).Regional analysis revealed significant disparities:Luoyuan County Hospital (Fuzhou) exhibited the highest total detection rate(86.59%, 1 414/1 633)and mixed infection rate(23.27%, 380/1 633)(both P<0.001), with notably elevated MP (26.39%, 431/1 633);Jian′ou City Hospital(Nanping) ranked second for Flu-A(14.21%, 409/2 879), RSV(13.20%, 380/2 879) and mixed infections(17.12%, 493/2 879);Lianjiang County Hospital(Fuzhou) showed distinct prevalence of Flu-A(10.68%, 130/1 217), PIV(6.00%, 73/1 217), and HBoV(1.73%, 21/1 217); Yong′an City Hospital (Sanming) reported high MP (26.07%, 238/913) and RSV(12.38%, 113/913);Shaowu City Hospital(Nanping) was dominated by MP (18.60%, 407/2 188) and HRV(13.39%, 293/2 188); Tingzhou Hospital(Longyan) had the highest HRV (17.88%, 407/2 276) and Flu-B (0.75%, 17/2 276); and Fuzhou Children′s Hospital showed elevated ADV(3.38%, 394/11 663) and HCoV(1.08%, 126/11 663). Except for Flu-B(0.47%, 108/22 769; P=0.054) and Ch(0.18%, 40/22769; P=0.900), all pathogens and mixed infections exhibited significant regional variations ( P<0.05).Gender analysis indicated higher detection rates of HRV, RSV, Flu-A, ADV, PIV, HBoV and mixed infections in males, while MP, HMPV, Flu-B, HCoV, and Ch were more prevalent in females, with statistically significant differences for HRV and MP (both P<0.001). Age stratification showed the highest overall detection rate in the 3-<6 years group (75.48%; P<0.001): RSV and Ch peaked in infants (<1 year), HRV, PIV, ADV and HBoV in toddlers (1-<3 years), HMPV, HCoV, and mixed infections in preschool children (3-<6 years), and MP, Flu-A and Flu-B in older children (6-<18 years).Analyzing the prevalent months, the monthly prevalence trends of pathogens in various regions are similar.Seasonal trends demonstrated year-round HRV activity (peaking in spring/autumn), MP prevalence in autumn/winter, RSV surges in spring-summer (April-June) and late summer-autumn (August-October), and Flu-A predominanced in winter-spring. Conclusion:Multiplex PCR with fragment analysis demonstrated high diagnostic efficacy. The top 4 non-bacterial pathogens in Fujian Province′s ARTI-hospitalized children in 2023 were HRV, MP, RSV and Flu-A. Pathogen distribution exhibited significant regional, age and seasonal variations, emphasizing the need for targeted prevention strategies.

Objective:To explore the role of nuclear receptor-binding SET-domain protein 1(NSD1)in the pathogenesis of nonobstructive azoospermia(NOA)by regulating the expressions of relevant genes.Methods:We detected the expression of NSD1 in the testis tissue of 7 male patients with obstructive azoospermia(OA)and 18 with NOA by qPCR and immunofluorescence assay,and determined the modification level of H3K36me2 in the testes of two groups of patients by immunofluorescence staining,Western blot and immunoprecipitation(IP).We examined the difference in the enrichment of H3K36me2 in the testis tissue by chromatin IP-based sequencing(ChIP-Seq),analyzed the genomic distribution and target genes using bioinformatics,and verified the expression levels of the target genes in the testes of the two groups of patients by qPCR.Results:Compared with the patients with OA,those with NOA showed dramatically decreased mRNA and protein expressions of NSD1(P=0.000 8).The binding of NSD1 to H3K36me2 was observed in the testis tissue of both the two groups of patients,while the modification level of H3K36me2 was evidently reduced in the NOA males.H3K36me2 was distributed mainly in the intergenic region in the testes of the two groups of patients,but the enrich-ment of H3K36me2 was obviously decreased in the NOA group.The differentially H3K36me2-enriched genes were involved in various biological processes,including tissue development,and cell morphogenesis.Results of ChIP-Seq and qPCR showed significantly down-regulated expressions of the target genes KIT,SPO11 and ACRV1 in the testis tissue of the NOA males compared with those in the OA patients(P＜0.01).Conclusion:The levels of NSD1 and H3K36me2 are decreased in testis tissue of the NOA patient,H3K36me2 is highly enriched in the spermatogenesis-related key genes KIT,SPO11 and ACRV1,and the down-regulated expression of NSD1 impairs spermatogenesis.

Artificial joint replacement is an effective treatment for end-stage osteoarthritis. Joint perception is a new patient-specific outcome measurement tool for patient-reported outcomes that provides healthcare professionals with a new perspective on condition monitoring and follow-up management after joint replacement. This paper reviews the concept, research state, shortcomings and insights of postoperative joint perception in joint replacement patients, so as to improve the understanding and application of joint perception by healthcare professionals, and to provide a reference for optimizing clinical postoperative assessment of joint replacement and formulating intervention strategies for joint perception.

Objective:To investigate the effect of dulagopeptide on physical examination indexes,plasma glucose metabolism and islet function in type 2 diabetes mellitus(T2DM)patients with poorly controlled plasma glucose.Methods:135 T2DM patients with poorly controlled plasma glucose who were admitted in our hospital from January 2023 to July 2024 were selected.A prospective randomized controlled design was adopted,they were divided into control group 1(received treatment with sitagliptin,n=45),control group 2(received treatment with insulin glargine,n=45),and observation group(received treatment with dulaglutide,n=45)according to the random number table method.Physical examination indexes,plasma glucose indicators,islet function,and incidence of adverse reactions were compared among the three groups.Results:12 weeks after treatment,body mass index(BMI),waist circumference,fasting plasma glucose(FPG),glycated hemoglobin(HbA1c),and postprandial 2-hour plasma glucose(2hPG)in the observation group were lower than those in control group 1 and control group 2(P＜0.05).12 weeks after treatment,the observation group had the highest HbA1c compliance rate,reaching 71.1％(P＜0.05).12 weeks after treatment,the fasting C-peptide(FC-P)and HOMA-islet(CP-DM)levels in the observation group were higher than those in control group 1 and control group 2(P＜0.05).Conclusion:Dulagopeptide can effectively improve physical examination indexes,plasma glucose indicators,and islet function in T2DM patients with poorly controlled plasma glucose.

Objective To compare the efficacy and tolerability of the novel bowel-cleansing agent TCIC-001 and the traditional polyethylene glycol (PEG) regimen for bowel preparation prior to colonoscopy. Methods Prospective inclusion of 62 patients who were scheduled to undergo colonoscopy at Zhongshan Hospital, Fudan University from July 2021 to July 2022. They were randomly divided into TCIC-001 group (n=31) and PEG group (n=31) using a random number table method. The TCIC-001 group took TCIC-001 orally, drinking water in stages, with a total liquid intake of 1 500 mL; the PEG group took PEG orally, taking it in 4 doses, with a total liquid intake of 3 000 mL. The primary endpoint indicator is the quality of intestinal hygiene evaluated by the Boston Bowel Preparation Scale (BBPS), the secondary endpoint indicators were medication adherence, medication duration, frequency of bowel movements, duration of bowel movements, and incidence of adverse events between two groups. Results No significant differences were observed in sex, age, or defecation frequency between the two groups. For efficacy, both groups achieved equivalent bowel cleanliness, with a “good preparation” rate of 93.55% and comparable BBPS score of each intestinal segment and total scores. For tolerability, the TCIC-001 group had a shorter medication duration compared to the PEG group ([48.8±25.9] min vs [82.8±28.4] min, P<0.001), a longer defecation duration ([288.6±74.0] min vs [236.5±74.3] min, P<0.001), and a lower incidence of first defecation before medication completion (9.68% vs 41.94%, P=0.004). Regarding safety, no significant differences were observed between the TCIC-001 group and the PEG group in incidences of chloride disturbances (0% vs 9.68%) and calcium disturbances (3.23% vs 6.45%), and no other adverse events. Conclusions TCIC-001 demonstrated comparable bowel-cleansing efficacy to PEG while significantly improving tolerability (reduced medication time and lower risk of premature defecation) and maintaining favorable safety.