Objective To analyze the trend of changes in the ranges of body mass index(BMI)among students at a university in Tianjin from 2021 to 2024,and to propose intervention strategies based on the key influencing factors.Methods A total of 1 000 first-year students enrolled in 2021 at a university in Tianjin were randomly selected,and their physical fitness test data over the 4 years of undergraduate study were collected.Differences in physical fitness test results were compared among students with varying BMI ranges.Linear regression analysis was used to identify the key factors affecting changes in BMI ranges in the students.Results The proportion of underweight students remained relatively stable,decreasing slightly from 2.5%in the first year to 2.0%in the senior year.The proportion of students with normal weight showed a fluctuating downward trend,from 71.5%at college enrollment to 69.0%by the fourth year.In contrast,the proportion of overweight/obese students showed a fluctuating upward trend.In physical fitness tests for the first year,overweight/obese students showed the best performance in the standing long jump test([2.15±0.42]m,P=0.08),normal-weight students showed the best performance in the sit-and-reach test([20.1±7.5]cm,P=0.03),and underweight students performed best in the sit-up test(1 min[48.7±19.5]repeats,P=0.02).Simple linear regression analysis revealed that students who changed from being underweight to having normal weight by the senior year showed associations with sit-up performance(β=0.03,95%CI,0.018 to 0.041)and 1 000 m run(β=0.73,95%CI,0.601 to 0.877).Students who transitioned from having normal weight to being overweight/obese were showed negative associations with their performance in 800 m run(β=-0.08,95%CI,-0.102 to-0.047)and 1 000 m run(β=-16.9,95%CI,-19.110 to-13.826).Students who changed from being overweight/obese to having normal weight showed associations with their performance in standing long jump(β=-0.41,95%CI,-0.631 to-0.227)and 1 000 m run(β=-0.15,95%CI,-0.215 to-0.073).Conclusion Male students of different BMI ranges should focus on improving their 1 000 m run performance,and overweight/obese male students should emphasize standing long jump training as well.Underweight female students should strengthen sit-up training,normal-weight female students should focus on 800 m run training,and overweight/obese females students should strengthen standing long jump training.

Objective: To analyze the association of 24 h activity behaviors and physical health of primary school students, so as to provide a reference for promoting the physical health of children and adolescents. Methods: From May to June, 2023, by stratified random sampling method, 583 primary school students aged 7-12 were selected from Tianjin for physical health examination. ActiGraph GT3X+ was used to measure their 24 h activity behaviors for 7 d, and their mental health and 24 h activity behaviors were analyzed by gender and grade. LASSO regression was applied for assessing the impact of 24 h activity on their health. Results: The compliance rate of seated forward bending (93.12%) were higher in boys than girls (91.86%), and the differences were statistically significant ( χ 2=4.53, P <0.05). Sleep time ( β =0.06), light intensity physical activity (LPA) time ( β =0.11), and moderate to vigorous physical activity (MVPA) time ( β =0.14) were positively correlated with physical fitness, whereas sedentary behavior (SB) time ( β =-0.08) were negatively correlated with physical fitness, and MVPA time had a positive effect on physical health of children and adolescents, followed by LPA time; while sleep time also had a positive effect , and SB time had a negative effect ( P <0.05). Conclusions Primary school students are generally faced with low physical activity level and high SB time, and MVPA and LPA have a significant impact on physical health. Therefore, it is crucial to develop personalized and differentiated physical activity promotion policies and interventions for primary school students with different classmates and gender.

Colorectal cancer is one of the most common malignant tumors worldwide. Due to the heterogeneity in patient outcomes and treatment responses to standard therapy regimens, personalized diagnostic and therapeutic strategies have remained a focus of sustained interest in research. In recent years, with the rapid progression of artificial intelligence (AI) technology in the medical field, an abundance of phased research results has emerged in the decision-making for preoperative, intraoperative, and postoperative diagnostic and therapeutic plans for colorectal cancer, demonstrating great potential for application. This new and efficient solution provides for the personalized evaluations and auxiliary diagnoses and treatments of patients with colorectal cancer. In the future, AI systems may continue to advance towards multimodal, multi-omics, and real-time directions. This paper aims to explore the current state of research on the multi-faceted auxiliary applications of AI in the diagnosis and treatment of colorectal cancer, as well as to present a prospective view of the innovations that AI technology could bring to personalized colorectal cancer treatment in the future and the challenges it may face.

Colorectal cancer is one of the most common malignant tumors worldwide. Due to the heterogeneity in patient outcomes and treatment responses to standard therapy regimens, personalized diagnostic and therapeutic strategies have remained a focus of sustained interest in research. In recent years, with the rapid progression of artificial intelligence (AI) technology in the medical field, an abundance of phased research results has emerged in the decision-making for preoperative, intraoperative, and postoperative diagnostic and therapeutic plans for colorectal cancer, demonstrating great potential for application. This new and efficient solution provides for the personalized evaluations and auxiliary diagnoses and treatments of patients with colorectal cancer. In the future, AI systems may continue to advance towards multimodal, multi-omics, and real-time directions. This paper aims to explore the current state of research on the multi-faceted auxiliary applications of AI in the diagnosis and treatment of colorectal cancer, as well as to present a prospective view of the innovations that AI technology could bring to personalized colorectal cancer treatment in the future and the challenges it may face.

Objective:To investigate the influencing factors of refractory anastomotic stenosis after laparoscopic intersphincteric resection (Ls-ISR) for rectal cancer and construction of nomogram prediction model.Methods:The retrospective case-control study was conducted. The clinicopatho-logical data of 495 patients who underwent Ls-ISR for rectal cancer in two medical centers, including 448 patients in Peking University First Hospital and 47 patients in Cancer Hospital Chinese Academy of Medical Sciences, from June 2012 to December 2021 were collected. There were 311 males and 184 females, aged 61 (range, 20-84)years. Observation indicators: (1) incidence of anastomotic stenosis; (2) influencing factors of refractory anastomotic stenosis after Ls-ISR; (3) construction and evaluation of nomogram prediction model for refractory anastomotic stenosis after Ls-ISR. Follow-up was conducted using outpatient examination and telephone interview to detect the incidence of postoperative anastomotic leakage and anastomotic stenosis up to August 2022. 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(range). Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. Factors with P<0.10 in univariate analysis were included in multivariate analysis. The R software (3.6.3 version) was used to construct nomogram prediction model. The receiver operating characteristic (ROC) curve was drawn and the area under curve (AUC) was used to evaluate the efficacy of nomogram prediction model. Results:(1) Incidence of anastomotic stenosis. All 495 patients underwent Ls-ISR successfully, without conversion to laparotomy, and all patients were followed up for 47(range, 8-116)months. During the follow-up period, there were 458 patients without anas-tomotic stenosis, and 37 patients with anastomotic stenosis. Of the 37 patients, there were 15 cases with grade A anastomotic stenosis, 3 cases with grade B anastomotic stenosis and 19 cases with grade C anastomotic stenosis, including 22 cases being identified as the refractory anastomotic stenosis. Fifteen patients with grade A anastomotic stenosis were relieved after anal dilation treat-ment. Three patients with grade B anastomotic stenosis were improved after balloon dilation and endoscopic treatment. Nineteen patients with grade C anastomotic stenosis underwent permanent stoma. During the follow-up period, there were 42 cases with anastomotic leakage including 17 cases combined with refractory anastomotic stenosis, and 453 cases without anastomotic leakage including 5 cases with refractory anastomotic stenosis. There was a significant difference in the refractory anastomotic stenosis between patients with and without anastomotic leakage ( χ2=131.181, P<0.05). (2) Influencing factors of refractory anastomotic stenosis after Ls-ISR. Results of multivariate analysis showed that neoadjuvant therapy, distance from tumor to anal margin ≤4 cm, clinic N+ stage were independent risk factors of refractory anastomotic stenosis after Ls-ISR ( hazard ratio=7.297, 3.898, 2.672, 95% confidence interval as 2.870-18.550, 1.050-14.465, 1.064-6.712, P<0.05). (3) Construction and evaluation of nomogram prediction model for refractory anastomotic stenosis after Ls-ISR. Based on the results of multivariate analysis, neoadjuvant therapy, distance from tumor to anal margin and clinic N staging were included to constructed the nomogram prediction model for refractory anastomotic stenosis after Ls-ISR. Results of ROC curve showed the AUC of nomogram prediction model for refractory anastomotic stenosis after Ls-ISR was 0.739 (95% confidence interval as 0.646-0.833). Conclusions:Neoadjuvant therapy, distance from tumor to anal margin ≤4 cm, clinic N+ stage are independent risk factors of refractory anastomotic stenosis after Ls-ISR. Nomogram prediction model based on these factors can predict the incidence of refractory anastomotic stenosis after Ls-ISR.

Objective:To develop a risk prediction lineogram of neooperative atrial fibrillation in patients with esophageal cancer.Methods:The clinical data of 1 509 patients undergoing esophageal cancer surgery admitted to the department of esophageal surgery of our hospital from December 2019 to April 2022 were gathered, and they were divided into two layers according to whether they had new atrial fibrillation after surgery. In each layer, they were randomly divided into training set and test set in a ratio of 7∶3. In the training population, the multi-factor logistic regression method was used to establish the prediction model, and the line graph of the prediction model was drawn. The ROC curve and calibration curve were drawn to assess the differentiation ability and calibration ability of the prediction model. The test set population is used to validate the prediction model. Results:A total of 1 509 patients with esophageal cancer were included in the study, and the incidence of new atrial fibrillation after surgery was 247 patients(16.4%). A total of 1 039 patients(68.9%) were enrolled in the training set. The multivariate logistic regression model indicated that age, gender, BMI, pulmonary infection, the use of invasive ventilator, and the need for additional drainage of fluid accumulation were the influencing factors for new postoperative atrial fibrillation. The AUC of the training set prediction model under ROC curve was 0.775(95% CI: 0.737-0.812, P<0.001), indicating that the model has high predictive discrimination ability. Calibration curve and Hosmer- Lemeshow test results P=0.796, indicating that the model has good consistency of prediction ability. There were 470 subjects(31.1%) in the test set. The results showed that the AUC of the prediction model under the ROC curve was 0.773(95% CI: 0.719-0.826, P<0.001), indicating that the prediction model still has a high discriminative ability in the test set population. Conclusion:Patients with age, gender, BMI, pulmonary infection, the use of invasive ventilator, and the need for additional drainage of effusion are at higher risk of new atrial fibrillation after surgery. The timely prediction, prevention and management of POAF are crucial to improve the prognostic quality of postoperative patients with esophageal cancer by constructing clinical prediction models.

Objective:To investigate the risk factors for organoid culture failure in colorectal cancer.Methods:This was a retrospective observational study. Tumor specimens were obtained from 1130 patients with colorectal cancer who had undergone surgery or biopsy and had no other concurrent malignancies at Nanfang Hospital of Southern Medical University from December 2021 to November 2022. Organoid culture was performed on 1231 tumor tissue samples. Univariate analysis and multivariate logistic regression were used to analyze the factors that might have influenced the rate of successful organoid culture of colorectal cancer tissue samples.Results:The median (range) duration of organoid culture was 7 (3–12) days. The overall rate of successful culture was 76.3% (939/1231). The rate of successful organoid cultures varied according to the sampling site, malignant ascites having the highest success rate (96.4%, 27/28), followed by liver metastases (83.1%, 54/65), lung metastases (8/10), primary tumors (76.0%, 816/1074), omental metastases (10/14), peritoneal metastases (61.5%, 16/26), ovarian metastases (3/5), and lymph node metastases (5/9). The difference in rates of successful organoid culture between primary tumors and malignant ascites was statistically significant ( P=0.012), whereas none of the other rates of successful organoid culture success differed significantly (all P>0.05). The rate of successful organoid culture was 96.4% (27/28) for malignant ascites obtained by abdominal puncture, 76.5% (864/1130) for surgical specimens, and 65.8% (48/73) for endoscopic biopsies; these differences are statistically significant (χ 2=10.773, P=0.005). The rate of successful organoid culture was 62.5% (40/64) in the neoadjuvant chemoradiotherapy group, which is significantly lower than in the non-adjuvant (76.9%, 787/1023) and chemotherapy groups (77.8%, 112/144) (χ 2=7.134, P=0.028). Multivariate logistic regression analysis revealed that endoscopic biopsy (OR=0.557, 95%CI: 0.335–0.924, P=0.024) and neoadjuvant chemoradiotherapy (OR=0.483, 95%CI: 0.285–0.820, P=0.007) were independent risk factors for failure of organoid culture of colorectal cancer samples. Malignant ascites (OR=8.537, 95%CI:1.154–63.131, P=0.036) and abdominal puncture (OR=8.294, 95% CI: 1.112–61.882, P=0.039) were identified as independent protective factors. Conclusions:The rate of successful organoid culture was influenced by the sampling site, sampling method, and chemoradiotherapy. The rate of successful organoid culture was lower for endoscopic biopsies and in patients receiving preoperative neoadjuvant chemoradiotherapy, and higher for malignant ascites. We consider that culture of malignant ascites is preferable when peritoneal metastases are suspected.

Objective:To investigate the risk factors for organoid culture failure in colorectal cancer.Methods:This was a retrospective observational study. Tumor specimens were obtained from 1130 patients with colorectal cancer who had undergone surgery or biopsy and had no other concurrent malignancies at Nanfang Hospital of Southern Medical University from December 2021 to November 2022. Organoid culture was performed on 1231 tumor tissue samples. Univariate analysis and multivariate logistic regression were used to analyze the factors that might have influenced the rate of successful organoid culture of colorectal cancer tissue samples.Results:The median (range) duration of organoid culture was 7 (3–12) days. The overall rate of successful culture was 76.3% (939/1231). The rate of successful organoid cultures varied according to the sampling site, malignant ascites having the highest success rate (96.4%, 27/28), followed by liver metastases (83.1%, 54/65), lung metastases (8/10), primary tumors (76.0%, 816/1074), omental metastases (10/14), peritoneal metastases (61.5%, 16/26), ovarian metastases (3/5), and lymph node metastases (5/9). The difference in rates of successful organoid culture between primary tumors and malignant ascites was statistically significant ( P=0.012), whereas none of the other rates of successful organoid culture success differed significantly (all P>0.05). The rate of successful organoid culture was 96.4% (27/28) for malignant ascites obtained by abdominal puncture, 76.5% (864/1130) for surgical specimens, and 65.8% (48/73) for endoscopic biopsies; these differences are statistically significant (χ 2=10.773, P=0.005). The rate of successful organoid culture was 62.5% (40/64) in the neoadjuvant chemoradiotherapy group, which is significantly lower than in the non-adjuvant (76.9%, 787/1023) and chemotherapy groups (77.8%, 112/144) (χ 2=7.134, P=0.028). Multivariate logistic regression analysis revealed that endoscopic biopsy (OR=0.557, 95%CI: 0.335–0.924, P=0.024) and neoadjuvant chemoradiotherapy (OR=0.483, 95%CI: 0.285–0.820, P=0.007) were independent risk factors for failure of organoid culture of colorectal cancer samples. Malignant ascites (OR=8.537, 95%CI:1.154–63.131, P=0.036) and abdominal puncture (OR=8.294, 95% CI: 1.112–61.882, P=0.039) were identified as independent protective factors. Conclusions:The rate of successful organoid culture was influenced by the sampling site, sampling method, and chemoradiotherapy. The rate of successful organoid culture was lower for endoscopic biopsies and in patients receiving preoperative neoadjuvant chemoradiotherapy, and higher for malignant ascites. We consider that culture of malignant ascites is preferable when peritoneal metastases are suspected.

Objective:To evaluate CT and or MRI imaging in the diagnosis of lateral lymph node metastasis in patients of middle and low rectal cancer.Methods:In this study, 112 lateral lymph nodes were harvested in 79 patients with middle and low rectal cancer. The relationship between the preoperative imaging features of the lateral lymph nodes and the postoperative pathology was evaluated.Result:Thirty-eight cases (48%) were pathologically confirmed to have lateral lymph node metastasis. The diameter of metastasis-positive lateral lymph nodes was significantly larger than that of metastasis-negative lateral lymph nodes ( P<0.01). Multivariate analysis of clinical features and imaging features found that, tumors poorly differentiated, mucinous adenocarcinoma, signet ring cell carcinoma ( P=0.006), and the largest short diameter of the lateral lymph node ≥7 mm ( P=0.024), uneven density or signal ( P=0.022) were independent risk factors for lateral lymph node metastasis. Conclusion:Poor tumor differentiation, lateral lymph node maximum short diameter ≥7 mm, density or signal unevenness are independent risk factors for lateral lymph node metastasis in middle and low rectal carcinoma.