Objective:To evaluate the value of peripheral blood systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), carcinoembryonic antigen (CEA), D-dimer, and albumin (ALB) alone or their combination in the diagnosis of early-onset colorectal cancer (EOCRC).Methods:From January 1, 2023 to November 30, 2024, 104 patients with EOCRC (EOCRC group) hospitalized at Renmin Hospital of Wuhan University were enrolled. During the same period, by simple random sampling method, 104 patients with benign colorectal polyps (benign polyp group) and 104 healthy individuals for health examinations (healthy control group) from outpatient department were enrolled. The peripheral blood parameters (including neutrophil count, lymphocyte count, CEA, and others) and pathological characteristics of EOCRC (including TNM stage, tumor differentiation grade, and depth of invasion) were collected. The relationship between peripheral blood parameters and EOCRC pathological features were analyzed. Receiver operating characteristic curves (ROC) were plotted, and the area under the curve (AUC) was calculated to evaluate the diagnostic value. Multivariate logistic regression analysis was performed to analyze the peripheral blood parameters which independently correlated with EOCRC and a combined diagnostic model was established. Simple random sampling method was used to divide the subjects in the negative control group (healthy control group + benign polyp group) and positive group (EOCRC group) into a training set (218 cases) and a validation set (94 cases) at a ratio of 7∶3, and the diagnostic performance of the combined diagnostic model in the training and validation sets was assessed. Hosmer-Lemeshow test and calibration curve were used to evaluate the fit and consistency of the model. Independent sample t-test, one-way ANOVA, Mann-Whitney U test and Kruskal-Wallis H test were used for statistical analysis. Results:EOCRC group had the highest levels of SII(744.03 (473.01, 1 246.28), 437.77 (342.28, 607.47), 497.31 (385.76, 721.63)×10 9/L), NLR(2.42 (1.76, 3.94), 1.96 (1.54, 2.52), 1.91 (1.55, 2.75)), CEA (3.58 (1.96, 20.85), 1.31 (0.95, 1.93), 1.21 (0.76, 2.11) μg/L) and D-dimer (0.36 (0.20, 0.90), 0.19 (0.12, 0.28), 0.18 (0.12, 0.30) mg/L), and the lowest levels of LMR(3.51±1.56, 4.38±1.37, 4.72±1.84) and ALB(42.40 (39.90, 44.70), 44.57 (42.83, 46.25), 44.95 (43.10, 46.58) g/L) than benign polyp group and healthy control group, and the differences were statistically significant ( H=31.18, 16.21, 76.72 and 47.72, F=15.40, H=34.19; all P<0.001). In EOCRC patients, there were statistically significant differences in SII and LMR between patients with different tumor invasion depth ( Z=-2.48, t=2.31; both P<0.05), in CEA between patients with different TNM stage, with or without lymph node metastasis and distant metastasis( Z=-2.68, -2.50 and -2.65; all P<0.05), in D-dimer between patients with different TNM stage, differentiation grade, invasion depth, and with or without lymph node metastasis and distant metastasis ( Z=-2.50, -2.60, -2.06, -2.14 and -3.33; all P<0.05), and in ALB between patients with or without distant metastasis ( Z=-2.52, P=0.012).The AUC of combination of SII, NLR, LMR, CEA, D-dimer, and ALB in differential diagnosis of the healthy control group and the EOCRC group was 0.914 (95% confidence interval (95% CI): 0.870 to 0.958, P<0.001), and the AUC of the combination in differential diagnosis of the benign polyp group and the EOCRC group was 0.904 (95% CI: 0.857 to 0.950, P<0.001). The results of multivariate logistic regression analysis revealed that SII, NLR, LMR, CEA, and ALB were all independently correlated with EOCRC (all P<0.05). The diagnostic model for EOCRC was established by the combination of SII, NLR, LMR, CEA, and ALB, and the AUC of the model in the training set and validation set was 0.911 and 0.883, respectively. The Hosmer-Lemeshow goodness-of-fit test indicated good model fit ( P=0.437). Calibration curve analysis showed strong consistency between predicted probabilities and actual probabilities, and the mean absolute error was 0.015. Conclusions:SII, NLR, LMR, CEA, D-dimer, and ALB all demonstrate diagnostic value in the diagnosis of EOCRC. The combined diagnostic model based on SII, NLR, LMR, CEA, and ALB demonstrates excellent diagnostic performance, which may serve as an adjunctive diagnostic approach for EOCRC.

Objective:To investigate the interplay between childhood traumatic experiences,mental disorders,and cognitive function among patients with alcohol use disorder(AUD).Methods:A cross-sectional study was con-ducted involving 160 patients with AUD hospitalized in the department of alcohol dependence.They were catego-rized into three groups based on their symptoms,namely the simple alcohol dependence group(AD group,n=86),the alcohol dependence with hallucinatory delusion group(ADHD group,n=43),and the alcohol dependence with delirium tremens group(ADDT group,n=31).The adverse childhood experiences,cognitive function,and child-hood trauma were assessed with the Adverse Childhood Experiences International Questionnaire(ACE-IQ),Mont-real Cognitive Assessment(MoCA),and Childhood Trauma Questionnaire(CTQ-SF).Results:The MoCA scores were significantly higher in the AD group than in the ADHD and ADDT groups[21.0(19.0,23.0)vs.19.0(15.0,22.0)vs.18.0(16.0,22.0),P＜0.01].The ADDT group had higher sexual abuse scores of CTQ-SF than the AD group.Lower MoCA score was associated with hallucination and delusion(OR=0.82,P＜0.01)or deliri-um tremens(OR=0.81,P＜0.01)in AUD patients.Higher ACE-IQ score was associated with hallucination and delusion in AUD patients(OR=1.51,P＜0.01).Conclusion:Mostpatients with alcohol use disorder have child-hood trauma and cognitive dysfunction.Poor cognitive function and adverse childhood experiences are risk factors for mental disorders among patients with alcohol use disorder.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective:To investigate the interplay between childhood traumatic experiences,mental disorders,and cognitive function among patients with alcohol use disorder(AUD).Methods:A cross-sectional study was con-ducted involving 160 patients with AUD hospitalized in the department of alcohol dependence.They were catego-rized into three groups based on their symptoms,namely the simple alcohol dependence group(AD group,n=86),the alcohol dependence with hallucinatory delusion group(ADHD group,n=43),and the alcohol dependence with delirium tremens group(ADDT group,n=31).The adverse childhood experiences,cognitive function,and child-hood trauma were assessed with the Adverse Childhood Experiences International Questionnaire(ACE-IQ),Mont-real Cognitive Assessment(MoCA),and Childhood Trauma Questionnaire(CTQ-SF).Results:The MoCA scores were significantly higher in the AD group than in the ADHD and ADDT groups[21.0(19.0,23.0)vs.19.0(15.0,22.0)vs.18.0(16.0,22.0),P＜0.01].The ADDT group had higher sexual abuse scores of CTQ-SF than the AD group.Lower MoCA score was associated with hallucination and delusion(OR=0.82,P＜0.01)or deliri-um tremens(OR=0.81,P＜0.01)in AUD patients.Higher ACE-IQ score was associated with hallucination and delusion in AUD patients(OR=1.51,P＜0.01).Conclusion:Mostpatients with alcohol use disorder have child-hood trauma and cognitive dysfunction.Poor cognitive function and adverse childhood experiences are risk factors for mental disorders among patients with alcohol use disorder.

Objective To observe the correlations of artificial intelligence(AI)measured parameters on anteroposterior and lateral spinal X-ray films with the severity of adolescent idiopathic scoliosis(AIS).Methods Totally 1 786 AIS patients were retrospectively enrolled.Parameters including Cobb angle(CA),coronal balance distance(CBD),T1 slope(T1S),pelvic tilt(PT),sacral slope(SS),apical vertebral translation(AVT),thoracic trunk shift(TTS),thoracic kyphosis(TK)and sagittal vertical axis(SVA)on anteroposterior and lateral spinal X-ray films were measured using uAI DR scoliosis analysis system.The severity of AIS was evaluated according to CA,and the correlations between other parameters and the severity of AIS were explored.The above parameters were compared under different severity levels and coronal/sagittal equilibrium states.Multivariate logistic regression analysis was performed to screen the independent impact factors on the severity of AIS.Results Significant differences of the above parameters were found among different severity levels except for SVA(all P＜0.001).With the aggravation of AIS,CA,CBD,AVT and TTS increased successively(all P＜0.001).T1S of severe AIS was higher than that of mild and moderate AIS(both P＜0.001),PT and SS of moderate and severe AIS were all bigger,while their TK were smaller than those of mild AIS(all P＜0.001).Significant differences of CA,T1S,PT,SS,AVT,TTS and TK were found between coronal balanced and imbalanced AIS(all P＜0.05),while of TK were found between sagittal balanced and unbalanced AIS(P=0.026).CBD,T1S,PT,SS,AVT and TTS were all positively correlated(r,=0.136-0.606,all P＜0.001),while TK was negatively correlated(r,=—0.404,P＜0.001)with the severity of AIS.T1S,AVT and TTS were all independent impact factors of the severity of AIS(all P＜0.001).Conclusion Among AI measured parameters on anteroposterior and lateral spinal X-ray films,CBD,T1S,PT,SS,AVT and TTS were positively correlated,while TK was negatively correlated with the severity of AIS.

Objective To observe the correlations of artificial intelligence(AI)measured parameters on anteroposterior and lateral spinal X-ray films with the severity of adolescent idiopathic scoliosis(AIS).Methods Totally 1 786 AIS patients were retrospectively enrolled.Parameters including Cobb angle(CA),coronal balance distance(CBD),T1 slope(T1S),pelvic tilt(PT),sacral slope(SS),apical vertebral translation(AVT),thoracic trunk shift(TTS),thoracic kyphosis(TK)and sagittal vertical axis(SVA)on anteroposterior and lateral spinal X-ray films were measured using uAI DR scoliosis analysis system.The severity of AIS was evaluated according to CA,and the correlations between other parameters and the severity of AIS were explored.The above parameters were compared under different severity levels and coronal/sagittal equilibrium states.Multivariate logistic regression analysis was performed to screen the independent impact factors on the severity of AIS.Results Significant differences of the above parameters were found among different severity levels except for SVA(all P＜0.001).With the aggravation of AIS,CA,CBD,AVT and TTS increased successively(all P＜0.001).T1S of severe AIS was higher than that of mild and moderate AIS(both P＜0.001),PT and SS of moderate and severe AIS were all bigger,while their TK were smaller than those of mild AIS(all P＜0.001).Significant differences of CA,T1S,PT,SS,AVT,TTS and TK were found between coronal balanced and imbalanced AIS(all P＜0.05),while of TK were found between sagittal balanced and unbalanced AIS(P=0.026).CBD,T1S,PT,SS,AVT and TTS were all positively correlated(r,=0.136-0.606,all P＜0.001),while TK was negatively correlated(r,=—0.404,P＜0.001)with the severity of AIS.T1S,AVT and TTS were all independent impact factors of the severity of AIS(all P＜0.001).Conclusion Among AI measured parameters on anteroposterior and lateral spinal X-ray films,CBD,T1S,PT,SS,AVT and TTS were positively correlated,while TK was negatively correlated with the severity of AIS.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective:To evaluate the value of peripheral blood systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), carcinoembryonic antigen (CEA), D-dimer, and albumin (ALB) alone or their combination in the diagnosis of early-onset colorectal cancer (EOCRC).Methods:From January 1, 2023 to November 30, 2024, 104 patients with EOCRC (EOCRC group) hospitalized at Renmin Hospital of Wuhan University were enrolled. During the same period, by simple random sampling method, 104 patients with benign colorectal polyps (benign polyp group) and 104 healthy individuals for health examinations (healthy control group) from outpatient department were enrolled. The peripheral blood parameters (including neutrophil count, lymphocyte count, CEA, and others) and pathological characteristics of EOCRC (including TNM stage, tumor differentiation grade, and depth of invasion) were collected. The relationship between peripheral blood parameters and EOCRC pathological features were analyzed. Receiver operating characteristic curves (ROC) were plotted, and the area under the curve (AUC) was calculated to evaluate the diagnostic value. Multivariate logistic regression analysis was performed to analyze the peripheral blood parameters which independently correlated with EOCRC and a combined diagnostic model was established. Simple random sampling method was used to divide the subjects in the negative control group (healthy control group + benign polyp group) and positive group (EOCRC group) into a training set (218 cases) and a validation set (94 cases) at a ratio of 7∶3, and the diagnostic performance of the combined diagnostic model in the training and validation sets was assessed. Hosmer-Lemeshow test and calibration curve were used to evaluate the fit and consistency of the model. Independent sample t-test, one-way ANOVA, Mann-Whitney U test and Kruskal-Wallis H test were used for statistical analysis. Results:EOCRC group had the highest levels of SII(744.03 (473.01, 1 246.28), 437.77 (342.28, 607.47), 497.31 (385.76, 721.63)×10 9/L), NLR(2.42 (1.76, 3.94), 1.96 (1.54, 2.52), 1.91 (1.55, 2.75)), CEA (3.58 (1.96, 20.85), 1.31 (0.95, 1.93), 1.21 (0.76, 2.11) μg/L) and D-dimer (0.36 (0.20, 0.90), 0.19 (0.12, 0.28), 0.18 (0.12, 0.30) mg/L), and the lowest levels of LMR(3.51±1.56, 4.38±1.37, 4.72±1.84) and ALB(42.40 (39.90, 44.70), 44.57 (42.83, 46.25), 44.95 (43.10, 46.58) g/L) than benign polyp group and healthy control group, and the differences were statistically significant ( H=31.18, 16.21, 76.72 and 47.72, F=15.40, H=34.19; all P<0.001). In EOCRC patients, there were statistically significant differences in SII and LMR between patients with different tumor invasion depth ( Z=-2.48, t=2.31; both P<0.05), in CEA between patients with different TNM stage, with or without lymph node metastasis and distant metastasis( Z=-2.68, -2.50 and -2.65; all P<0.05), in D-dimer between patients with different TNM stage, differentiation grade, invasion depth, and with or without lymph node metastasis and distant metastasis ( Z=-2.50, -2.60, -2.06, -2.14 and -3.33; all P<0.05), and in ALB between patients with or without distant metastasis ( Z=-2.52, P=0.012).The AUC of combination of SII, NLR, LMR, CEA, D-dimer, and ALB in differential diagnosis of the healthy control group and the EOCRC group was 0.914 (95% confidence interval (95% CI): 0.870 to 0.958, P<0.001), and the AUC of the combination in differential diagnosis of the benign polyp group and the EOCRC group was 0.904 (95% CI: 0.857 to 0.950, P<0.001). The results of multivariate logistic regression analysis revealed that SII, NLR, LMR, CEA, and ALB were all independently correlated with EOCRC (all P<0.05). The diagnostic model for EOCRC was established by the combination of SII, NLR, LMR, CEA, and ALB, and the AUC of the model in the training set and validation set was 0.911 and 0.883, respectively. The Hosmer-Lemeshow goodness-of-fit test indicated good model fit ( P=0.437). Calibration curve analysis showed strong consistency between predicted probabilities and actual probabilities, and the mean absolute error was 0.015. Conclusions:SII, NLR, LMR, CEA, D-dimer, and ALB all demonstrate diagnostic value in the diagnosis of EOCRC. The combined diagnostic model based on SII, NLR, LMR, CEA, and ALB demonstrates excellent diagnostic performance, which may serve as an adjunctive diagnostic approach for EOCRC.

Objective:To investigate the risk factors and establish a prediction model of primary non-response (PNR) to anti-tumor necrosis factor-α(TNF-α) monoclonal antibody in Crohn′s disease (CD) patients.Methods:From December 1, 2018 to July 31, 2022, 103 patients with CD treated with the anti-TNF-α monoclonal antibody in Renmin Hospital of Wuhan University were enrolled (modeling group), and at the same time, 109 patients with CD treated with anti-TNF-α monoclonal antibody in Zhongnan Hospital of Wuhan University were selected (validation group). The baseline clinical data of all the patients before the first treatment of anti-TNF-α monoclonal antibody were collected, which included C-reactive protein (CRP), the simplified Crohn′s disease activity index (CDAI), and modified multiplier simple endoscopic score for Crohn′s disease (MM-SES-CD), etc. Multivariate logistic regression was used to screen the independent risk factors of PNR in patients with CD treated with the anti-TNF-α monoclonal antibody, and to establish the nomograms prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), the net reclassification index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA) were used to evaluate the predictive efficacy and clinical application value of the prediction model. DeLong test was used for statistical analysis.Results:The results of multivariate logistic regression analysis showed that high level of CRP ( OR=1.030, 95% confidence interval (95% CI) 1.002 to 1.059), simplified CDAI ( OR=1.399, 95% CI 1.023 to 1.913), and MM-SES-CD ( OR=1.100, 95% CI 1.025 to 1.181) in baseline were independent risk factors of PNR in patients with CD treated with the anti-TNF-α monoclonal antibody ( P=0.033, 0.036 and 0.008). The results of ROC analysis showed that the AUCs of CRP, simplified CDAI, MM-SES-CD, and the prediction model in the modeling group and the validation group were 0.697(95% CI 0.573 to 0.821), 0.772(95% CI 0.666 to 0.879), 0.819(95% CI 0.725 to 0.912), 0.869 (95% CI 0.786 to 0.951) and 0.856 (95% CI 0.756 to 0.955), respectively. The AUC of the prediction model in the modeling group was greater than those of CRP and simplified CDAI, and the differences were statistically significant ( Z=3.00 and 2.75, P=0.003 and 0.006), while compared with MM-SES-CD and the validation group, the differences were not statistically significant (both P>0.05). However, compared with MM-SES-CD, the NRI and IDI of the prediction model in the modeling group were 0.205(95% CI 0.002 to 0.409, P=0.048) and 0.098(95% CI 0.022 to 0.174, P=0.011), respectively, suggesting that the predictive ability of the prediction model was better than that of MM-SES-CD. The results of DCA indicated that the prediction model had significant clinical benefits in both the modeling group and the validation group. Conclusions:A prediction model was successfully constructed based on the independent risk factors for PNR in patients with CD treated with the anti-TNF-α monoclonal antibody. After verification, the prediction model has good prediction performance and significant clinical benefits.